Sandy Napel

All Publications

• Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines. Journal of medical imaging (Bellingham, Wash.) Mattonen, S. A., Gude, D., Echegaray, S., Bakr, S., Rubin, D. L., Napel, S. 2020; 7 (4): 042803

  Abstract

  Quantitative image features that can be computed from medical images are proving to be valuable biomarkers of underlying cancer biology that can be used for assessing treatment response and predicting clinical outcomes. However, validation and eventual clinical implementation of these tools is challenging due to the absence of shared software algorithms, architectures, and the tools required for computing, comparing, evaluating, and disseminating predictive models. Similarly, researchers need to have programming expertise in order to complete these tasks. The quantitative image feature pipeline (QIFP) is an open-source, web-based, graphical user interface (GUI) of configurable quantitative image-processing pipelines for both planar (two-dimensional) and volumetric (three-dimensional) medical images. This allows researchers and clinicians a GUI-driven approach to process and analyze images, without having to write any software code. The QIFP allows users to upload a repository of linked imaging, segmentation, and clinical data or access publicly available datasets (e.g., The Cancer Imaging Archive) through direct links. Researchers have access to a library of file conversion, segmentation, quantitative image feature extraction, and machine learning algorithms. An interface is also provided to allow users to upload their own algorithms in Docker containers. The QIFP gives researchers the tools and infrastructure for the assessment and development of new imaging biomarkers and the ability to use them for single and multicenter clinical and virtual clinical trials.

  View details for DOI 10.1117/1.JMI.7.4.042803

  View details for PubMedID 32206688

• Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. Tomography (Ann Arbor, Mich.) Jaggi, A., Mattonen, S. A., McNitt-Gray, M., Napel, S. 2020; 6 (2): 111–17

  Abstract

  Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.

  View details for DOI 10.18383/j.tom.2019.00030

  View details for PubMedID 32548287

• Radiomics and Radiogenomics: Technical Basis and Clinical Applications edited by Xing, L., Li, R., Napel, S., Rubin, D. L. CRC Press. 2019
• Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats. Cancer Napel, S., Mu, W., Jardim-Perassi, B. V., Aerts, H. J., Gillies, R. J. 2018

  Abstract

  Although cancer often is referred to as "a disease of the genes," it is indisputable that the (epi)genetic properties of individual cancer cells are highly variable, even within the same tumor. Hence, preexisting resistant clones will emerge and proliferate after therapeutic selection that targets sensitive clones. Herein, the authors propose that quantitative image analytics, known as "radiomics," can be used to quantify and characterize this heterogeneity. Virtually every patient with cancer is imaged radiologically. Radiomics is predicated on the beliefs that these images reflect underlying pathophysiologies, and that they can be converted into mineable data for improved diagnosis, prognosis, prediction, and therapy monitoring. In the last decade, the radiomics of cancer has grown from a few laboratories to a worldwide enterprise. During this growth, radiomics has established a convention, wherein a large set of annotated image features (1-2000 features) are extracted from segmented regions of interest and used to build classifier models to separate individual patients into their appropriate class (eg, indolent vs aggressive disease). An extension of this conventional radiomics is the application of "deep learning," wherein convolutional neural networks can be used to detect the most informative regions and features without human intervention. A further extension of radiomics involves automatically segmenting informative subregions ("habitats") within tumors, which can be linked to underlying tumor pathophysiology. The goal of the radiomics enterprise is to provide informed decision support for the practice of precision oncology.

  View details for PubMedID 30383900

• A radiogenomic dataset of non-small cell lung cancer. Scientific data Bakr, S., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J. A., Zhang, W., Leung, A. N., Kadoch, M., D Hoang, C., Shrager, J., Quon, A., Rubin, D. L., Plevritis, S. K., Napel, S. 2018; 5: 180202

  Abstract

  Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.

  View details for PubMedID 30325352

• Radiogenomics is the future of treatment response assessment in clinical oncology MEDICAL PHYSICS El Naqa, I., Napel, S., Zaidi, H. 2018; 45 (10): 4325–28

  View details for PubMedID 29863785

• NOTE: This list is not complete see CV link Napel, S. 2013
• AI in Radiology: Opportunities and Challenges. Seminars in ultrasound, CT, and MR Flory, M. N., Napel, S., Tsai, E. B. 2024

  Abstract

  Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process - from algorithm initiation and design to development and implementation - to maximize benefit and minimize harm that can be enabled by this technology.

  View details for DOI 10.1053/j.sult.2024.02.004

  View details for PubMedID 38403128

• Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules. Journal of medical imaging (Bellingham, Wash.) Selby, H. M., Mukherjee, P., Parham, C., Malik, S. B., Gevaert, O., Napel, S., Shah, R. P. 2023; 10 (4): 044006

  Abstract

  We aim to evaluate the performance of radiomic biopsy (RB), best-fit bounding box (BB), and a deep-learning-based segmentation method called no-new-U-Net (nnU-Net), compared to the standard full manual (FM) segmentation method for predicting benign and malignant lung nodules using a computed tomography (CT) radiomic machine learning model.A total of 188 CT scans of lung nodules from 2 institutions were used for our study. One radiologist identified and delineated all 188 lung nodules, whereas a second radiologist segmented a subset (n=20) of these nodules. Both radiologists employed FM and RB segmentation methods. BB segmentations were generated computationally from the FM segmentations. The nnU-Net, a deep-learning-based segmentation method, performed automatic nodule detection and segmentation. The time radiologists took to perform segmentations was recorded. Radiomic features were extracted from each segmentation method, and models to predict benign and malignant lung nodules were developed. The Kruskal-Wallis and DeLong tests were used to compare segmentation times and areas under the curve (AUC), respectively.For the delineation of the FM, RB, and BB segmentations, the two radiologists required a median time (IQR) of 113 (54 to 251.5), 21 (9.25 to 38), and 16 (12 to 64.25) s, respectively (p=0.04). In dataset 1, the mean AUC (95% CI) of the FM, RB, BB, and nnU-Net model were 0.964 (0.96 to 0.968), 0.985 (0.983 to 0.987), 0.961 (0.956 to 0.965), and 0.878 (0.869 to 0.888). In dataset 2, the mean AUC (95% CI) of the FM, RB, BB, and nnU-Net model were 0.717 (0.705 to 0.729), 0.919 (0.913 to 0.924), 0.699 (0.687 to 0.711), and 0.644 (0.632 to 0.657).Radiomic biopsy-based models outperformed FM and BB models in prediction of benign and malignant lung nodules in two independent datasets while deep-learning segmentation-based models performed similarly to FM and BB. RB could be a more efficient segmentation method, but further validation is needed.

  View details for DOI 10.1117/1.JMI.10.4.044006

  View details for PubMedID 37564098

  View details for PubMedCentralID PMC10411216

• Machine learning with multimodal data for COVID-19. Heliyon Chen, W., Sá, R. C., Bai, Y., Napel, S., Gevaert, O., Lauderdale, D. S., Giger, M. L. 2023; 9 (7): e17934

  Abstract

  In response to the unprecedented global healthcare crisis of the COVID-19 pandemic, the scientific community has joined forces to tackle the challenges and prepare for future pandemics. Multiple modalities of data have been investigated to understand the nature of COVID-19. In this paper, MIDRC investigators present an overview of the state-of-the-art development of multimodal machine learning for COVID-19 and model assessment considerations for future studies. We begin with a discussion of the lessons learned from radiogenomic studies for cancer diagnosis. We then summarize the multi-modality COVID-19 data investigated in the literature including symptoms and other clinical data, laboratory tests, imaging, pathology, physiology, and other omics data. Publicly available multimodal COVID-19 data provided by MIDRC and other sources are summarized. After an overview of machine learning developments using multimodal data for COVID-19, we present our perspectives on the future development of multimodal machine learning models for COVID-19.

  View details for DOI 10.1016/j.heliyon.2023.e17934

  View details for PubMedID 37483733

  View details for PubMedCentralID PMC10362086

• Predicting treatment response for the safe non-operative management of patients with rectal cancer using an MRI-based deep-learning model Selby, H. M., Liu, C., Sheth, V., Napel, S., Wagner, T., Morris, A. M. LIPPINCOTT WILLIAMS & WILKINS. 2023

  View details for Web of Science ID 001053772002055

• Early Detection of Lung Cancer in the NLST Dataset. medRxiv : the preprint server for health sciences Mukherjee, P., Brezhneva, A., Napel, S., Gevaert, O. 2023

  Abstract

  Lung Cancer is the leading cause of cancer mortality in the U.S. The effectiveness of standard treatments, including surgery, chemotherapy or radiotherapy, depends on several factors like type and stage of cancer, with the survival rate being much worse for later cancer stages. The National Lung Screening Trial (NLST) established that patients screened using low-dose Computed Tomography (CT) had a 15 to 20 percent lower risk of dying from lung cancer than patients screened using chest X-rays. While CT excelled at detecting small early stage malignant nodules, a large proportion of patients ( > 25%) screened positive and only a small fraction ( < 10%) of these positive screens actually had or developed cancer in the subsequent years. We developed a model to distinguish between high and low risk patients among the positive screens, predicting the likelihood of having or developing lung cancer at the current time point or in subsequent years non-invasively, based on current and previous CT imaging data. However, most of the nodules in NLST are very small, and nodule segmentations or even precise locations are unavailable. Our model comprises two stages: the first stage is a neural network model trained on the Lung Image Database Consortium (LIDC-IDRI) cohort which detects nodules and assigns them malignancy scores. The second part of our model is a boosted tree which outputs a cancer probability for a patient based on the nodule information (location and malignancy score) predicted by the first stage. Our model, built on a subset of the NLST cohort ( n = 1138) shows excellent performance, achieving an area under the receiver operating characteristics curve (ROC AUC) of 0.85 when predicting based on CT images from all three time points available in the NLST dataset.

  View details for DOI 10.1101/2023.03.01.23286632

  View details for PubMedID 36909593

• Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests. Journal of medical imaging (Bellingham, Wash.) Christie, J. R., Daher, O., Abdelrazek, M., Romine, P. E., Malthaner, R. A., Qiabi, M., Nayak, R., Napel, S., Nair, V. S., Mattonen, S. A. 2022; 9 (6): 066001

  Abstract

  Purpose: We developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC).Approach: We retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort ( n = 101 ), validated in the testing cohort ( n = 34 ) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan-Meier analysis.Results: The model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 ( p < 0.005 ) and 0.60 ( p = 0.008 ), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training ( p < 0.005 ) and the testing ( p = 0.03 ) cohorts.Conclusions: Our radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.

  View details for DOI 10.1117/1.JMI.9.6.066001

  View details for PubMedID 36388142

• The Medical Segmentation Decathlon. Nature communications Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., van Ginneken, B., Bilello, M., Bilic, P., Christ, P. F., Do, R. K., Gollub, M. J., Heckers, S. H., Huisman, H., Jarnagin, W. R., McHugo, M. K., Napel, S., Pernicka, J. S., Rhode, K., Tobon-Gomez, C., Vorontsov, E., Meakin, J. A., Ourselin, S., Wiesenfarth, M., Arbeláez, P., Bae, B., Chen, S., Daza, L., Feng, J., He, B., Isensee, F., Ji, Y., Jia, F., Kim, I., Maier-Hein, K., Merhof, D., Pai, A., Park, B., Perslev, M., Rezaiifar, R., Rippel, O., Sarasua, I., Shen, W., Son, J., Wachinger, C., Wang, L., Wang, Y., Xia, Y., Xu, D., Xu, Z., Zheng, Y., Simpson, A. L., Maier-Hein, L., Cardoso, M. J. 2022; 13 (1): 4128

  Abstract

  International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

  View details for DOI 10.1038/s41467-022-30695-9

  View details for PubMedID 35840566

• Radiomic features quantifying pixel-level characteristics of breast tumors from magnetic resonance imaging predict risk factors in triple-negative breast cancer. Mantz, A. B., Zhou, R., Kozlov, A., DeMartini, W., Chen, S., Okamoto, S., Ikeda, D. M., Mattonen, S. A., Napel, S., Alkim, E., Sledge, G. W., Kurian, A. W., Liu, M., Telli, M. L., Itakura, H. LIPPINCOTT WILLIAMS & WILKINS. 2022

  View details for Web of Science ID 000863680302515

• Artificial intelligence and machine learning in cancer imaging. Communications medicine Koh, D., Papanikolaou, N., Bick, U., Illing, R., Kahn, C. E., Kalpathi-Cramer, J., Matos, C., Marti-Bonmati, L., Miles, A., Mun, S. K., Napel, S., Rockall, A., Sala, E., Strickland, N., Prior, F. 2022; 2: 133

  Abstract

  An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

  View details for DOI 10.1038/s43856-022-00199-0

  View details for PubMedID 36310650

• Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge IEEE TRANSACTIONS ON MEDICAL IMAGING Balagurunathan, Y., Beers, A., Mcnitt-Gray, M., Hadjiiski, L., Napel, S., Goldgof, D., Perez, G., Arbelaez, P., Mehrtash, A., Kapur, T., Yang, E., Moon, J., Perez, G., Delgado-Gonzalo, R., Farhangi, M., Amini, A. A., Ni, R., Feng, X., Bagari, A., Vaidhya, K., Veasey, B., Safta, W., Frigui, H., Enguehard, J., Gholipour, A., Castillo, L., Daza, L., Pinsky, P., Kalpathy-Cramer, J., Farahani, K. 2021; 40 (12): 3748-3761

  Abstract

  Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

  View details for DOI 10.1109/TMI.2021.3097665

  View details for Web of Science ID 000724511900043

  View details for PubMedID 34264825

• Machine Learning Approach to Differentiation of Peripheral Schwannomas and Neurofibromas: A Multi-Center Study. Neuro-oncology Zhang, M., Tong, E., Wong, S., Hamrick, F., Mohammadzadeh, M., Rao, V., Pendleton, C., Smith, B. W., Hug, N. F., Biswal, S., Seekins, J., Napel, S., Spinner, R. J., Mahan, M. A., Yeom, K. W., Wilson, T. J. 2021

  Abstract

  BACKGROUND: Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas.METHODS: We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline. Age, sex, neurogenetic syndrome, spontaneous pain, and motor deficit were recorded. We evaluated the performance of 6 radiomics-based classifier models with and without clinical features and compared model performance against human expert evaluators.RESULTS: 107 schwannomas and 59 neurofibroma were included. The primary models included both clinical and imaging data. The accuracy of the human evaluators (0.765) did not significantly exceed the no-information rate (NIR), whereas the Support Vector Machine (0.929), Logistic Regression (0.929), and Random Forest (0.905) classifiers exceeded the NIR. Using the method of DeLong, the AUC for the Logistic Regression (AUC=0.923) and K Nearest Neighbor (AUC=0.923) classifiers was significantly greater than the human evaluators (AUC=0.766; p = 0.041).CONCLUSIONS: The radiomics-based classifiers developed here proved to be more accurate and had a higher AUC on the ROC curve than expert human evaluators. This demonstrates that radiomics using routine MRI sequences and clinical features can aid in differentiation of peripheral schwannomas and neurofibromas.

  View details for DOI 10.1093/neuonc/noab211

  View details for PubMedID 34487172

• Machine-learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study Zhang, M., Tong, E., Hamrick, F., Pendleton, C., Smith, B., Hug, N., Mattonen, S., Napel, S., Spinner, R., Yeom, K., Wilson, T., Mahan, M. AMER ASSOC NEUROLOGICAL SURGEONS. 2021

  View details for Web of Science ID 000680654900137

• Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study. Neurosurgery Zhang, M., Tong, E., Hamrick, F., Lee, E. H., Tam, L. T., Pendleton, C., Smith, B. W., Hug, N. F., Biswal, S., Seekins, J., Mattonen, S. A., Napel, S., Campen, C. J., Spinner, R. J., Yeom, K. W., Wilson, T. J., Mahan, M. A. 2021

  Abstract

  BACKGROUND: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications.OBJECTIVE: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs.METHODS: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers.RESULTS: A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P=.002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P=.001).CONCLUSION: Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

  View details for DOI 10.1093/neuros/nyab212

  View details for PubMedID 34131749

• Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans. JCO clinical cancer informatics Shah, R. P., Selby, H. M., Mukherjee, P., Verma, S., Xie, P., Xu, Q., Das, M., Malik, S., Gevaert, O., Napel, S. 2021; 5: 746-757

  Abstract

  PURPOSE: Small-cell lung cancer (SCLC) is the deadliest form of lung cancer, partly because of its short doubling time. Delays in imaging identification and diagnosis of nodules create a risk for stage migration. The purpose of our study was to determine if a machine learning radiomics model can detect SCLC on computed tomography (CT) among all nodules at least 1 cm in size.MATERIALS AND METHODS: Computed tomography scans from a single institution were selected and resampled to 1 * 1 * 1 mm. Studies were divided into SCLC and other scans comprising benign, adenocarcinoma, and squamous cell carcinoma that were segregated into group A (noncontrast scans) and group B (contrast-enhanced scans). Four machine learning classification models, support vector classifier, random forest (RF), XGBoost, and logistic regression, were used to generate radiomic models using 59 quantitative first-order and texture Imaging Biomarker Standardization Initiative compliant PyRadiomics features, which were found to be robust between two segmenters with minimum Redundancy Maximum Relevance feature selection within each leave-one-out-cross-validation to avoid overfitting. The performance was evaluated using a receiver operating characteristic curve. A final model was created using the RF classifier and aggregate minimum Redundancy Maximum Relevance to determine feature importance.RESULTS: A total of 103 studies were included in the analysis. The area under the receiver operating characteristic curve for RF, support vector classifier, XGBoost, and logistic regression was 0.81, 0.77, 0.84, and 0.84 in group A, and 0.88, 0.87, 0.85, and 0.81 in group B, respectively. Nine radiomic features in group A and 14 radiomic features in group B were predictive of SCLC. Six radiomic features overlapped between groups A and B.CONCLUSION: A machine learning radiomics model may help differentiate SCLC from other lung lesions.

  View details for DOI 10.1200/CCI.21.00021

  View details for PubMedID 34264747

• MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study. Neuro-oncology advances Tam, L. T., Yeom, K. W., Wright, J. N., Jaju, A., Radmanesh, A., Han, M., Toescu, S., Maleki, M., Chen, E., Campion, A., Lai, H. A., Eghbal, A. A., Oztekin, O., Mankad, K., Hargrave, D., Jacques, T. S., Goetti, R., Lober, R. M., Cheshier, S. H., Napel, S., Said, M., Aquilina, K., Ho, C. Y., Monje, M., Vitanza, N. A., Mattonen, S. A. 2021; 3 (1): vdab042

  Abstract

  Background: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model.Methods: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naive DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables.Results: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02).Conclusions: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

  View details for DOI 10.1093/noajnl/vdab042

  View details for PubMedID 33977272

• Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma. Journal of medical imaging (Bellingham, Wash.) Jaggi, A., Mastrodicasa, D., Charville, G. W., Jeffrey, R. B., Napel, S., Patel, B. 2021; 8 (5): 054501

  Abstract

  Purpose: To differentiate oncocytoma and chromophobe renal cell carcinoma (RCC) using radiomics features computed from spherical samples of image regions of interest, "radiomic biopsies" (RBs). Approach: In a retrospective cohort study of 102 CT cases [68 males (67%), 34 females (33%); mean age ± SD, 63 ± 12    years ], we pathology-confirmed 42 oncocytomas (41%) and 60 chromophobes (59%). A board-certified radiologist performed two RB rounds. From each RB round, we computed radiomics features and compared the performance of a random forest and AdaBoost binary classifier trained from the features. To control for overfitting, we performed 10 rounds of 70% to 30% train-test splits with feature-selection, cross-validation, and hyperparameter-optimization on each split. We evaluated the performance with test ROC AUC. We tested models on data from the other RB round and compared with the same round testing with the DeLong test. We clustered important features for each round and measured a bootstrapped adjusted Rand index agreement. Results: Our best classifiers achieved an average AUC of 0.71 ± 0.024 . We found no evidence of an effect for RB round ( p = 1 ). We also found no evidence for a decrease in model performance when tested on the other RB round ( p = 0.85 ). Feature clustering produced seven clusters in each RB round with high agreement ( Rand index = 0.981 ± 0.002 , p < 0.00001 ). Conclusions: A consistent radiomic signature can be derived from RBs and could help distinguish oncocytoma and chromophobe RCC.

  View details for DOI 10.1117/1.JMI.8.5.054501

  View details for PubMedID 34514033

  View details for PubMedCentralID PMC8423237

• Interreader Variability in Semantic Annotation of Microvascular Invasion in Hepatocellular Carcinoma on Contrast-enhanced Triphasic CT Images. Radiology. Imaging cancer Bakr, S., Gevaert, O., Patel, B., Kesselman, A., Shah, R., Napel, S., Kothary, N. 2020; 2 (3): e190062

  Abstract

  Purpose: To evaluate interreader agreement in annotating semantic features on preoperative CT images to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).Materials and Methods: Preoperative, contrast material-enhanced triphasic CT studies from 89 patients (median age, 64 years; age range, 36-85 years; 70 men) who underwent hepatic resection between 2008 and 2017 for a solitary HCC were reviewed. Three radiologists annotated CT images obtained during the arterial and portal venous phases, independently and in consensus, with features associated with MVI reported by other investigators. The assessed factors were the presence or absence of discrete internal arteries, hypoattenuating halo, tumor-liver difference, peritumoral enhancement, and tumor margin. Testing also included previously proposed MVI signatures: radiogenomic venous invasion (RVI) and two-trait predictor of venous invasion (TTPVI), using single-reader and consensus annotations. Cohen (two-reader) and Fleiss (three-reader) kappa and the bootstrap method were used to analyze interreader agreement and differences in model performance, respectively.Results: Of HCCs assessed, 32.6% (29 of 89) had MVI at histopathologic findings. Two-reader agreement, as assessed by pairwise Cohen kappa statistics, varied as a function of feature and imaging phase, ranging from 0.02 to 0.6; three-reader Fleiss kappa varied from -0.17 to 0.56. For RVI and TTPVI, the best single-reader performance had sensitivity and specificity of 52% and 77% and 67% and 74%, respectively. In consensus, the sensitivity and specificity for the RVI and TTPVI signatures were 59% and 67% and 70% and 62%, respectively.Conclusion: Interreader variability in semantic feature annotation remains a challenge and affects the reproducibility of predictive models for preoperative detection of MVI in HCC.Supplemental material is available for this article.© RSNA, 2020.

  View details for DOI 10.1148/rycan.2020190062

  View details for PubMedID 32550600

• A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data. Nature machine intelligence Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., Gevaert, O. 2020; 2 (5): 274-282

  Abstract

  Lung cancer is the most common fatal malignancy in adults worldwide, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography (CT) is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of NSCLC patients. We trained and evaluated LungNet on four independent cohorts of NSCLC patients from four medical centers: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité - Universitätsmedizin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3, and 4, respectively. Further, the survival model can be used, via transfer learning, for classifying benign vs malignant nodules on the Lung Image Database Consortium (n = 1010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a noninvasive predictor for prognosis in NSCLC patients and can facilitate interpretation of CT images for lung cancer stratification and prognostication.

  View details for DOI 10.1038/s42256-020-0173-6

  View details for PubMedID 33791593

  View details for PubMedCentralID PMC8008967

• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology Zwanenburg, A., Vallieres, M., Abdalah, M. A., Aerts, H. J., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., Bogowicz, M., Boldrini, L., Buvat, I., Cook, G. J., Davatzikos, C., Depeursinge, A., Desseroit, M., Dinapoli, N., Dinh, C. V., Echegaray, S., El Naqa, I., Fedorov, A. Y., Gatta, R., Gillies, R. J., Goh, V., Gotz, M., Guckenberger, M., Ha, S. M., Hatt, M., Isensee, F., Lambin, P., Leger, S., Leijenaar, R. T., Lenkowicz, J., Lippert, F., Losnegard, A., Maier-Hein, K. H., Morin, O., Muller, H., Napel, S., Nioche, C., Orlhac, F., Pati, S., Pfaehler, E. A., Rahmim, A., Rao, A. U., Scherer, J., Siddique, M. M., Sijtsema, N. M., Socarras Fernandez, J., Spezi, E., Steenbakkers, R. J., Tanadini-Lang, S., Thorwarth, D., Troost, E. G., Upadhaya, T., Valentini, V., van Dijk, L. V., van Griethuysen, J., van Velden, F. H., Whybra, P., Richter, C., Lock, S. 2020: 191145

  Abstract

  Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

  View details for DOI 10.1148/radiol.2020191145

  View details for PubMedID 32154773

• A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets Nature Machine Intelligence Mukherjee, P., Zhou, M., Lee, E., Schicht, A., Balagurunathan, Y., Napel, S., Gillies, R., Wong, S., Thieme, A., Leung, A., Gevaert, O. 2020; 2 (5): 274–282

  View details for DOI 10.1038/s42256-020-0173-6

• Radiomics Signatures of Cardiovascular Risk Factors in Cardiac MRI: Results From the UK Biobank. Frontiers in cardiovascular medicine Cetin, I., Raisi-Estabragh, Z., Petersen, S. E., Napel, S., Piechnik, S. K., Neubauer, S., Gonzalez Ballester, M. A., Camara, O., Lekadir, K. 2020; 7: 591368

  Abstract

  Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.

  View details for DOI 10.3389/fcvm.2020.591368

  View details for PubMedID 33240940

• The utility of three-dimensional models in complex microsurgical reconstruction. Archives of plastic surgery Ogunleye, A. A., Deptula, P. L., Inchauste, S. M., Zelones, J. T., Walters, S. n., Gifford, K. n., LeCastillo, C. n., Napel, S. n., Fleischmann, D. n., Nguyen, D. H. 2020; 47 (5): 428–34

  Abstract

  Three-dimensional (3D) model printing improves visualization of anatomical structures in space compared to two-dimensional (2D) data and creates an exact model of the surgical site that can be used for reference during surgery. There is limited evidence on the effects of using 3D models in microsurgical reconstruction on improving clinical outcomes.A retrospective review of patients undergoing reconstructive breast microsurgery procedures from 2017 to 2019 who received computed tomography angiography (CTA) scans only or with 3D models for preoperative surgical planning were performed. Preoperative decision-making to undergo a deep inferior epigastric perforator (DIEP) versus muscle-sparing transverse rectus abdominis myocutaneous (MS-TRAM) flap, as well as whether the decision changed during flap harvest and postoperative complications were tracked based on the preoperative imaging used. In addition, we describe three example cases showing direct application of 3D mold as an accurate model to guide intraoperative dissection in complex microsurgical reconstruction.Fifty-eight abdominal-based breast free-flaps performed using conventional CTA were compared with a matched cohort of 58 breast free-flaps performed with 3D model print. There was no flap loss in either group. There was a significant reduction in flap harvest time with use of 3D model (CTA vs. 3D, 117.7±14.2 minutes vs. 109.8±11.6 minutes; P=0.001). In addition, there was no change in preoperative decision on type of flap harvested in all cases in 3D print group (0%), compared with 24.1% change in conventional CTA group.Use of 3D print model improves accuracy of preoperative planning and reduces flap harvest time with similar postoperative complications in complex microsurgical reconstruction.

  View details for DOI 10.5999/aps.2020.00829

  View details for PubMedID 32971594

• Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions MEDICAL PHYSICS Tunali, I., Hall, L. O., Napel, S., Cherezov, D., Guvenis, A., Gillies, R. J., Schabath, M. B. 2019; 46 (11): 5075–85

  View details for DOI 10.1002/mp.13808]

  View details for Web of Science ID 000494894600034

• Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology Mattonen, S. A., Davidzon, G. A., Benson, J., Leung, A. N., Vasanawala, M., Horng, G., Shrager, J. B., Napel, S., Nair, V. S. 2019: 190357

  Abstract

  Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.

  View details for DOI 10.1148/radiol.2019190357

  View details for PubMedID 31526257

• [18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer. Tomography (Ann Arbor, Mich.) Mattonen, S. A., Davidzon, G. A., Bakr, S., Echegaray, S., Leung, A. N., Vasanawala, M., Horng, G., Napel, S., Nair, V. S. 2019; 5 (1): 145–53

  Abstract

  We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non-small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56-2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction (P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66-0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67-0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.

  View details for PubMedID 30854452

• Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Medical physics Tunali, I. n., Hall, L. O., Napel, S. n., Cherezov, D. n., Guvenis, A. n., Gillies, R. J., Schabath, M. B. 2019

  Abstract

  Recent efforts have demonstrated that radiomic features extracted from the peritumoral region, the area surrounding the tumor parenchyma, have clinical utility in various cancer types. However, as like any radiomic features, peritumoral features may also be unstable and/or non-reproducible. Hence, the purpose of this study was to assess the stability and reproducibility of computed tomography (CT) radiomic features extracted from the peritumoral regions of lung lesions where stability was defined as the consistency of a feature by different segmentations, and reproducibility was defined as the consistency of a feature to image acquisition.Stability was measured utilizing the "Moist run" dataset and reproducibility was measured utilizing the Reference Image Database to Evaluate Therapy Response test-retest dataset. Peritumoral radiomic features were extracted from incremental distances of 3-12 mm outside the tumor parenchyma segmentation. A total of 264 statistical, histogram and texture radiomic features were assessed from the selected peritumoral region-of-interests. All features (except wavelet texture features) were extracted using standardized algorithms defined by the Image Biomarker Standardization Initiative. Stability and reproducibility of features were assessed using concordance correlation coefficient. The clinical utility of stable and reproducible peritumoral features were tested in three previously published lung cancer datasets using overall survival as the endpoint.Features found to be stable and reproducible, regardless of the peritumoral distances, included statistical, histogram and a subset of texture features suggesting that these features are less affected by changes size or shape differences of the peritumoral region due to different segmentations and image acquisitions. The stability and reproducibility of 3D Laws and wavelet texture features were inconsistent across all peritumoral distances. The analyses also revealed that a subset of features were consistently stable irrespective of the initial parameters (e.g., seed point) for a given segmentation algorithm. No significant differences were found for stability for features that were extracted from region-of-interests (ROIs) bounded by a lung parenchyma mask versus ROIs that were not bounded by a lung parenchyma mask (i.e., peritumoral regions that were allowed to extend outside of lung parenchyma). After testing the clinical utility of peritumoral features, stable and reproducible features were shown to be more likely to create repeatable models than unstable and non-reproducible features.This study identified a subset of stable and reproducible CT radiomic features extracted from the peritumoral region of lung lesions. The stable and reproducible features identified in this study could be applied to a feature selection pipeline for CT radiomic analyses. According to our findings, top performing features in models for overall survival are most likely to be stable and reproducible hence, it may be best practice to utilize them to achieve repeatable studies and reduce the creation of overfit models.

  View details for DOI 10.1002/mp.13808

  View details for PubMedID 31494946

• A RADIOMICS APPROACH TO ANALYZE CARDIAC ALTERATIONS IN HYPERTENSION Cetin, I., Petersen, S. E., Napel, S., Camara, O., Gonzalez Ballester, M., Lekadir, K., IEEE IEEE. 2019: 640–43

  View details for Web of Science ID 000485040000136

• Principles and Rationale of Radiomics and Radiogenomics Radiomics and Radiogenomics: Technical Basis and Clinical Applications Napel, S. CRC Press. 2019; 1: 3–12
• Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE TRANSACTIONS ON MEDICAL IMAGING Bernard, O., Lalande, A., Zotti, C., Cervenansky, F., Yang, X., Heng, P., Cetin, I., Lekadir, K., Camara, O., Gonzalez Ballester, M., Sanroma, G., Napel, S., Petersen, S., Tziritas, G., Grinias, E., Khened, M., Kollerathu, V., Krishnamurthi, G., Rohe, M., Pennec, X., Sermesant, M., Isensee, F., Jaeger, P., Maier-Hein, K. H., Full, P. M., Wolf, I., Engelhardt, S., Baumgartner, C. F., Koch, L. M., Wolterink, J. M., Isgum, I., Jang, Y., Hong, Y., Patravali, J., Jain, S., Humbert, O., Jodoin, P. 2018; 37 (11): 2514–25

  Abstract

  Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

  View details for DOI 10.1109/TMI.2018.2837502

  View details for Web of Science ID 000449113800013

  View details for PubMedID 29994302

• Magnetic resonance imaging and molecular features associated with tumor-infiltrating lymphocytes in breast cancer. Breast cancer research : BCR Wu, J., Li, X., Teng, X., Rubin, D. L., Napel, S., Daniel, B. L., Li, R. 2018; 20 (1): 101

  Abstract

  BACKGROUND: We sought to investigate associations between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) features and tumor-infiltrating lymphocytes (TILs) in breast cancer, as well as to study if MRI features are complementary to molecular markers of TILs.METHODS: In this retrospective study, we extracted 17 computational DCE-MRI features to characterize tumor and parenchyma in The Cancer Genome Atlas cohort (n=126). The percentage of stromal TILs was evaluated on H&E-stained histological whole-tumor sections. We first evaluated associations between individual imaging features and TILs. Multiple-hypothesis testing was corrected by the Benjamini-Hochberg method using false discovery rate (FDR). Second, we implemented LASSO (least absolute shrinkage and selection operator) and linear regression nested with tenfold cross-validation to develop an imaging signature for TILs. Next, we built a composite prediction model for TILs by combining imaging signature with molecular features. Finally, we tested the prognostic significance of the TIL model in an independent cohort (I-SPY 1; n=106).RESULTS: Four imaging features were significantly associated with TILs (P<0.05 and FDR<0.2), including tumor volume, cluster shade of signal enhancement ratio (SER), mean SER of tumor-surrounding background parenchymal enhancement (BPE), and proportion of BPE. Among molecular and clinicopathological factors, only cytolytic score was correlated with TILs (rho=0.51; 95% CI, 0.36-0.63; P=1.6E-9). An imaging signature that linearly combines five features showed correlation with TILs (rho=0.40; 95% CI, 0.24-0.54; P=4.2E-6). A composite model combining the imaging signature and cytolytic score improved correlation with TILs (rho=0.62; 95% CI, 0.50-0.72; P=9.7E-15). The composite model successfully distinguished low vs high, intermediate vs high, and low vs intermediate TIL groups, with AUCs of 0.94, 0.76, and 0.79, respectively. During validation (I-SPY 1), the predicted TILs from the imaging signature separated patients into two groups with distinct recurrence-free survival (RFS), with log-rank P=0.042 among triple-negative breast cancer (TNBC). The composite model further improved stratification of patients with distinct RFS (log-rank P=0.0008), where TNBC with no/minimal TILs had a worse prognosis.CONCLUSIONS: Specific MRI features of tumor and parenchyma are associated with TILs in breast cancer, and imaging may play an important role in the evaluation of TILs by providing key complementary information in equivocal cases or situations that are prone to sampling bias.

  View details for PubMedID 30176944

• Deep learning to predict survival prognosis for patients with non-small cell lung cancer using images and clinical data Lee, E. H., Zhou, M., Gamboa, N., Brennan, K., Itakura, H., Nair, V., Napel, S., Wong, S., Gevaert, O. AMER ASSOC CANCER RESEARCH. 2018

  View details for DOI 10.1158/1538-7445.AM2018-3048

  View details for Web of Science ID 000468819500411

• Erratum: Semi-automated pulmonary nodule interval segmentation using the NLST data. Medical physics Balagurunathan, Y., Beers, A., Kalpathy-Cramer, J., McNitt-Gray, M., Hadjiiski, L., Zhao, B., Zhu, J., Yang, H., Yip, S. S., Aerts, H. J., Napel, S., Cherezov, D., Cha, K., Chan, H. P., Flores, C., Garcia, A., Gillies, R., Goldgof, D. 2018; 45 (6): 2689-2690

  View details for DOI 10.1002/mp.12905

  View details for PubMedID 29894564

• GFPT2-expressing cancer-associated fibroblasts mediate metabolic reprogramming in human lung adenocarcinoma. Cancer research Zhang, W., Bouchard, G., Yu, A., Shafiq, M., Jamali, M., Shrager, J. B., Ayers, K., Bakr, S., Gentles, A. J., Diehn, M., Quon, A., West, R. B., Nair, V., van de Rijn, M., Napel, S., Plevritis, S. K. 2018

  Abstract

  Metabolic reprogramming of the tumor microenvironment is recognized as a cancer hallmark. To identify new molecular processes associated with tumor metabolism, we analyzed the transcriptome of bulk and flow-sorted human primary non-small cell lung cancer (NSCLC) together with 18FDG-positron emission tomography scans, which provide a clinical measure of glucose uptake. Tumors with higher glucose uptake were functionally enriched for molecular processes associated with invasion in adenocarcinoma (AD) and cell growth in squamous cell carcinoma (SCC). Next, we identified genes correlated to glucose uptake that were predominately overexpressed in a single cell-type comprising the tumor microenvironment. For SCC, most of these genes were expressed by malignant cells, whereas in AD they were predominately expressed by stromal cells, particularly cancer-associated fibroblasts (CAFs). Among these AD genes correlated to glucose uptake, we focused on Glutamine-Fructose-6-Phosphate Transaminase 2 (GFPT2), which codes for the Glutamine-Fructose-6-Phosphate Aminotransferase 2 (GFAT2), a rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which is responsible for glycosylation. GFPT2 was predictive of glucose uptake independent of GLUT1, the primary glucose transporter, and was prognostically significant at both gene and protein level. We confirmed that normal fibroblasts transformed to CAF-like cells, following TGF-beta treatment, upregulated HBP genes, including GFPT2, with less change in genes driving glycolysis, pentose phosphate pathway and TCA cycle. Our work provides new evidence of histology-specific tumor-stromal properties associated with glucose uptake in NSCLC and identifies GFPT2 as a critical regulator of tumor metabolic reprogramming in AD.

  View details for PubMedID 29760045

• Semi-automated pulmonary nodule interval segmentation using the NLST data MEDICAL PHYSICS Balagurunathan, Y., Beers, A., Kalpathy-Cramer, J., McNitt-Gray, M., Hadjiiski, L., Zhao, B., Zhu, J., Yang, H., Yip, S. F., Aerts, H. L., Napel, S., Cherezov, D., Cha, K., Chan, H., Flores, C., Garcia, A., Gillies, R., Goldgof, D. 2018; 45 (3): 1093–1107

  Abstract

  To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm.We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [-0.0038 to 0.69] and percent change in volume was [-0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77).We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams).

  View details for PubMedID 29363773

• Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy. Radiology Wu, J. n., Cao, G. n., Sun, X. n., Lee, J. n., Rubin, D. L., Napel, S. n., Kurian, A. W., Daniel, B. L., Li, R. n. 2018: 172462

  Abstract

  Purpose To characterize intratumoral spatial heterogeneity at perfusion magnetic resonance (MR) imaging and investigate intratumoral heterogeneity as a predictor of recurrence-free survival (RFS) in breast cancer. Materials and Methods In this retrospective study, a discovery cohort (n = 60) and a multicenter validation cohort (n = 186) were analyzed. Each tumor was divided into multiple spatially segregated, phenotypically consistent subregions on the basis of perfusion MR imaging parameters. The authors first defined a multiregional spatial interaction (MSI) matrix and then, based on this matrix, calculated 22 image features. A network strategy was used to integrate all image features and classify patients into different risk groups. The prognostic value of imaging-based stratification was evaluated in relation to clinical-pathologic factors with multivariable Cox regression. Results Three intratumoral subregions with high, intermediate, and low MR perfusion were identified and showed high consistency between the two cohorts. Patients in both cohorts were stratified according to network analysis of multiregional image features regarding RFS (log-rank test, P = .002 for both). Aggressive tumors were associated with a larger volume of the poorly perfused subregion as well as interaction between poorly and moderately perfused subregions and surrounding parenchyma. At multivariable analysis, the proposed MSI-based marker was independently associated with RFS (hazard ratio: 3.42; 95% confidence interval: 1.55, 7.57; P = .002) adjusting for age, estrogen receptor (ER) status, progesterone receptor status, human epidermal growth factor receptor type 2 (HER2) status, tumor volume, and pathologic complete response (pCR). Furthermore, imaging helped stratify patients for RFS within the ER-positive and HER2-positive subgroups (log-rank test, P = .007 and .004) and among patients without pCR after neoadjuvant chemotherapy (log-rank test, P = .003). Conclusion Breast cancer consists of multiple spatially distinct subregions. Imaging heterogeneity is an independent prognostic factor beyond traditional risk predictors.

  View details for PubMedID 29714680

• Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology Zhou, M. n., Leung, A. n., Echegaray, S. n., Gentles, A. n., Shrager, J. B., Jensen, K. C., Berry, G. J., Plevritis, S. K., Rubin, D. L., Napel, S. n., Gevaert, O. n. 2018; 286 (1): 307–15

  Abstract

  Purpose To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Materials and Methods A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. Conclusion Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article.

  View details for PubMedID 28727543

• Radiogenomics Map: A Novel Approach for Noninvasive Identification of Molecular Properties? Response RADIOLOGY Gevaert, O., Napel, S. 2017; 285 (3): 1061

  View details for Web of Science ID 000416570800053

• Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images. Journal of digital imaging Echegaray, S., Bakr, S., Rubin, D. L., Napel, S. 2017

  Abstract

  The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

  View details for PubMedID 28993897

• Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics. Oncotarget Minamimoto, R., Jamali, M., Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Plevritis, S. K., Rubin, D. L., Leung, A. N., Napel, S., Quon, A. 2017; 8 (32): 52792-52801

  Abstract

  This study investigated the relationship between epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the EGFR and KRAS gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker (P < 0.001) or low pack-year smoking history (p = 0.002) and female gender (p = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations (p = 0.018). The maximum standardized uptake value (SUVmax) of FDG uptake in lung lesions was a predictive factor of the EGFR mutations (p = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor (p < 0.02) of EGFR mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor (p < 0.001) for the EGFR mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUVmax were both predictive for the EGFR mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the EGFR and KRAS mutations in lung cancer.

  View details for DOI 10.18632/oncotarget.17782

  View details for PubMedID 28881771

  View details for PubMedCentralID PMC5581070

• Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics. Oncotarget Minamimoto, R., Jamali, M., Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Plevritis, S. K., Rubin, D. L., Leung, A. N., Napel, S., Quon, A. 2017

  Abstract

  This study investigated the relationship between epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the EGFR and KRAS gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker (P < 0.001) or low pack-year smoking history (p = 0.002) and female gender (p = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations (p = 0.018). The maximum standardized uptake value (SUVmax) of FDG uptake in lung lesions was a predictive factor of the EGFR mutations (p = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor (p < 0.02) of EGFR mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor (p < 0.001) for the EGFR mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUVmax were both predictive for the EGFR mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the EGFR and KRAS mutations in lung cancer.

  View details for DOI 10.18632/oncotarget.17782

  View details for PubMedID 28538213

• Variations in the functional visual field for detection of lung nodules on chest computed tomography: Impact of nodule size, distance, and local lung complexity. Medical physics Ebner, L., Tall, M., Choudhury, K. R., Ly, D. L., Roos, J. E., Napel, S., Rubin, G. D. 2017

  Abstract

  To explore the characteristics that impact lung nodule detection by peripheral vision when searching for lung nodules on chest CT-scans.This study was approved by the local IRB and is HIPAA compliant. A simulated primary (1°) target mass (2 × 2 × 5 cm) was embedded into 5 cm thick subvolumes (SV) extracted from three unenhanced lung MDCT scans (64 row, 1.25 mm thickness, 0.7 mm increment). One of 30 solid, secondary nodules with either 3-4 mm and 5-8 mm diameters were embedded into 192 of 207 SVs. The secondary nodule was placed at a random depth within each SV, a transverse distance of 2.5, 5, 7.5, or 10 mm, and along one of eight rays cast every 45° from the center of the 1° mass. Video recordings of transverse paging in cranio-caudal direction were created for each SV (frame rate three sections/sec). Six radiologists observed each cine-loop once while gaze-tracking hardware assured that gaze was centered on the 1° mass. Each radiologist assigned a confidence rating (0-5) to the detection of a secondary nodule and indicated its location. Detection sensitivity was analyzed relative to secondary nodule size, transverse distance, radial orientation, and lung complexity. Lung complexity was characterized by the number of particles (connected pixels) and the sum of the area of all particles above a -500 HU threshold within regions of interest around the 1° mass and secondary nodule.Using a proportional odds logistic regression model and eliminating redundant predictors, models fit individually to each reader resulted in the following decreasing order of association based on greatest reduction in Akaike Information Criterion: secondary nodule diameter (6/6 readers, P < 0.001), distance from central mass (6/6 readers, P < 0.001), lung complexity particle count (5/6 readers, P = 0.05), and lung complexity particle area (3/6 readers, P = 0.03). Substantial inter-reader differences in sensitivity to decreasing nodule diameter, distance, and complexity characteristics were observed.Of the investigated parameters, secondary nodule size, distance from the gaze center and lung complexity (particle number and area) significantly impact nodule detection with peripheral vision.

  View details for DOI 10.1002/mp.12277

  View details for PubMedID 28419484

• Adaptive local window for level set segmentation of CT and MRI liver lesions. Medical image analysis Hoogi, A., Beaulieu, C. F., Cunha, G. M., Heba, E., Sirlin, C. B., Napel, S., Rubin, D. L. 2017; 37: 46-55

  Abstract

  We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p < 0.001).

  View details for DOI 10.1016/j.media.2017.01.002

  View details for PubMedID 28157660

  View details for PubMedCentralID PMC5393306

• Predictive radiogenomics modeling of EGFR mutation status in lung cancer SCIENTIFIC REPORTS Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., Rubin, D. L., Napel, S., Leung, A. N. 2017; 7

  Abstract

  Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient's tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.

  View details for DOI 10.1038/srep41674

  View details for PubMedID 28139704

• A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS Lekadir, K., Galimzianova, A., Betriu, A., del Mar Vila, M., Igual, L., Rubin, D. L., Fernandez, E., Radeva, P., Napel, S. 2017; 21 (1): 48-55

  Abstract

  Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound.

  View details for DOI 10.1109/JBHI.2016.2631401

  View details for Web of Science ID 000395538500006

  View details for PubMedID 27893402

• Heterogeneous Enhancement Patterns of Tumor-adjacent Parenchyma at MR Imaging Are Associated with Dysregulated Signaling Pathways and Poor Survival in Breast Cancer. Radiology Wu, J. n., Li, B. n., Sun, X. n., Cao, G. n., Rubin, D. L., Napel, S. n., Ikeda, D. M., Kurian, A. W., Li, R. n. 2017: 162823

  Abstract

  Purpose To identify the molecular basis of quantitative imaging characteristics of tumor-adjacent parenchyma at dynamic contrast material-enhanced magnetic resonance (MR) imaging and to evaluate their prognostic value in breast cancer. Materials and Methods In this institutional review board-approved, HIPAA-compliant study, 10 quantitative imaging features depicting tumor-adjacent parenchymal enhancement patterns were extracted and screened for prognostic features in a discovery cohort of 60 patients. By using data from The Cancer Genome Atlas (TCGA), a radiogenomic map for the tumor-adjacent parenchymal tissue was created and molecular pathways associated with prognostic parenchymal imaging features were identified. Furthermore, a multigene signature of the parenchymal imaging feature was built in a training cohort (n = 126), and its prognostic relevance was evaluated in two independent cohorts (n = 879 and 159). Results One image feature measuring heterogeneity (ie, information measure of correlation) was significantly associated with prognosis (false-discovery rate < 0.1), and at a cutoff of 0.57 stratified patients into two groups with different recurrence-free survival rates (log-rank P = .024). The tumor necrosis factor signaling pathway was identified as the top enriched pathway (hypergeometric P < .0001) among genes associated with the image feature. A 73-gene signature based on the tumor profiles in TCGA achieved good association with the tumor-adjacent parenchymal image feature (R(2) = 0.873), which stratified patients into groups regarding recurrence-free survival (log-rank P = .029) and overall survival (log-rank P = .042) in an independent TCGA cohort. The prognostic value was confirmed in another independent cohort (Gene Expression Omnibus GSE 1456), with log-rank P = .00058 for recurrence-free survival and log-rank P = .0026 for overall survival. Conclusion Heterogeneous enhancement patterns of tumor-adjacent parenchyma at MR imaging are associated with the tumor necrosis signaling pathway and poor survival in breast cancer. (©) RSNA, 2017 Online supplemental material is available for this article.

  View details for PubMedID 28708462

• Noninvasive radiomics signature based on quantitative analysis of computed tomography images as a surrogate for microvascular invasion in hepatocellular carcinoma: a pilot study. Journal of medical imaging (Bellingham, Wash.) Bakr, S. n., Echegaray, S. n., Shah, R. n., Kamaya, A. n., Louie, J. n., Napel, S. n., Kothary, N. n., Gevaert, O. n. 2017; 4 (4): 041303

  Abstract

  We explore noninvasive biomarkers of microvascular invasion (mVI) in patients with hepatocellular carcinoma (HCC) using quantitative and semantic image features extracted from contrast-enhanced, triphasic computed tomography (CT). Under institutional review board approval, we selected 28 treatment-naive HCC patients who underwent surgical resection. Four radiologists independently selected and delineated tumor margins on three axial CT images and extracted computational features capturing tumor shape, image intensities, and texture. We also computed two types of "delta features," defined as the absolute difference and the ratio computed from all pairs of imaging phases for each feature. 717 arterial, portal-venous, delayed single-phase, and delta-phase features were robust against interreader variability ([Formula: see text]). An enhanced cross-validation analysis showed that combining robust single-phase and delta features in the arterial and venous phases identified mVI (AUC [Formula: see text]). Compared to a previously reported semantic feature signature (AUC 0.47 to 0.58), these features in our cohort showed only slight to moderate agreement (Cohen's kappa range: 0.03 to 0.59). Though preliminary, quantitative analysis of image features in arterial and venous phases may be potential surrogate biomarkers for mVI in HCC. Further study in a larger cohort is warranted.

  View details for PubMedID 28840174

• Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography : a journal for imaging research Kalpathy-Cramer, J., Mamomov, A., Zhao, B., Lu, L., Cherezov, D., Napel, S., Echegaray, S., Rubin, D., McNitt-Gray, M., Lo, P., Sieren, J. C., Uthoff, J., Dilger, S. K., Driscoll, B., Yeung, I., Hadjiiski, L., Cha, K., Balagurunathan, Y., Gillies, R., Goldgof, D. 2016; 2 (4): 430-437

  Abstract

  Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

  View details for DOI 10.18383/j.tom.2016.00235

  View details for PubMedID 28149958

  View details for PubMedCentralID PMC5279995

• A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer. Tomography : a journal for imaging research Echegaray, S., Nair, V., Kadoch, M., Leung, A., Rubin, D., Gevaert, O., Napel, S. 2016; 2 (4): 283-294

  Abstract

  Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

  View details for DOI 10.18383/j.tom.2016.00163

  View details for PubMedID 28612050

  View details for PubMedCentralID PMC5466872

• Intratumor Partitioning of Serial Computed Tomography and FDG Positron Emission Tomography Images Identifies High-Risk Tumor Subregions and Predicts Patterns of Failure in Non-Small Cell Lung Cancer After Radiation Therapy 58th Annual Meeting of the American-Society-for-Radiation-Oncology (ASTRO) Wu, J., Gensheimer, M. F., Dong, X., Rubin, D. L., Napel, S., Diehn, M., Loo, B. W., Li, R. ELSEVIER SCIENCE INC. 2016: S100–S100

  View details for Web of Science ID 000387655804563

• Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study. International journal of radiation oncology, biology, physics Wu, J., Gensheimer, M. F., Dong, X., Rubin, D. L., Napel, S., Diehn, M., Loo, B. W., Li, R. 2016; 95 (5): 1504-1512

  Abstract

  To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer.In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP).Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05).We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.

  View details for DOI 10.1016/j.ijrobp.2016.03.018

  View details for PubMedID 27212196

• A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study JOURNAL OF DIGITAL IMAGING Kalpathy-Cramer, J., Zhao, B., Goldgof, D., Gu, Y., Wang, X., Yang, H., Tan, Y., Gillies, R., Napel, S. 2016; 29 (4): 476-487

  Abstract

  Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

  View details for DOI 10.1007/s10278-016-9859-z

  View details for Web of Science ID 000379751200011

  View details for PubMedID 26847203

  View details for PubMedCentralID PMC4942386

• SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer. Medical physics Wu, J., Gensheimer, M., Dong, X., Rubin, D., Napel, S., Diehn, M., Loo, B., Li, R. 2016; 43 (6): 3349-?

  View details for DOI 10.1118/1.4955673

  View details for PubMedID 28046308

• A Rapid Segmentation-Insensitive 'Digital Biopsy' Method for Radiomic Feature Extraction; Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer Tomography Echegaray, S., Nair, V., Kadoch, M., Leung, A., Rubin, D., Gevaert, O., Napel Sandy , et al 2016; 2 (4): 283–94

  Abstract

  Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.

  View details for DOI 10.18383/j.tom.2016.00163

  View details for PubMedCentralID PMC5466872

• Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma. Journal of medical imaging (Bellingham, Wash.) Echegaray, S., Gevaert, O., Shah, R., Kamaya, A., Louie, J., Kothary, N., Napel, S. 2015; 2 (4): 041011-?

  Abstract

  The purpose of this study is to investigate the utility of obtaining "core samples" of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers' segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.

  View details for DOI 10.1117/1.JMI.2.4.041011

  View details for PubMedID 26587549

• Special Section Guest Editorial:Radiomics and Imaging Genomics: Quantitative Imaging for Precision Medicine. Journal of medical imaging (Bellingham, Wash.) Napel, S., Giger, M. 2015; 2 (4): 041001-?

  View details for DOI 10.1117/1.JMI.2.4.041001

  View details for PubMedID 26839908

  View details for PubMedCentralID PMC4729214

• Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Science translational medicine Itakura, H., Achrol, A. S., Mitchell, L. A., Loya, J. J., Liu, T., Westbroek, E. M., Feroze, A. H., Rodriguez, S., Echegaray, S., Azad, T. D., Yeom, K. W., Napel, S., Rubin, D. L., Chang, S. D., Harsh, G. R., Gevaert, O. 2015; 7 (303): 303ra138-?

  Abstract

  Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative magnetic resonance (MR) imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 single-institution patients with de novo, solitary, unilateral GBM. Three distinct phenotypic "clusters" emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters--pre-multifocal, spherical, and rim-enhancing, names reflecting their image features--were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from the analysis of TCGA tumor copy number and gene expression data with the PARADIGM (Pathway Recognition Algorithm Using Data Integration on Genomic Models) algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by the image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

  View details for DOI 10.1126/scitranslmed.aaa7582

  View details for PubMedID 26333934

• Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Science translational medicine Itakura, H., Achrol, A. S., Mitchell, L. A., Loya, J. J., Liu, T., Westbroek, E. M., Feroze, A. H., Rodriguez, S., Echegaray, S., Azad, T. D., Yeom, K. W., Napel, S., Rubin, D. L., Chang, S. D., Harsh, G. R., Gevaert, O. 2015; 7 (303): 303ra138-?

  Abstract

  Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative magnetic resonance (MR) imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 single-institution patients with de novo, solitary, unilateral GBM. Three distinct phenotypic "clusters" emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters--pre-multifocal, spherical, and rim-enhancing, names reflecting their image features--were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from the analysis of TCGA tumor copy number and gene expression data with the PARADIGM (Pathway Recognition Algorithm Using Data Integration on Genomic Models) algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by the image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

  View details for DOI 10.1126/scitranslmed.aaa7582

  View details for PubMedID 26333934

  View details for PubMedCentralID PMC4666025

• Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features. Radiology Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., Plevritis, S. K. 2015; 276 (1): 313-?

  View details for DOI 10.1148/radiol.2015154019

  View details for PubMedID 26101929

• Content-based image retrieval in radiology: analysis of variability in human perception of similarity. Journal of medical imaging (Bellingham, Wash.) Faruque, J., Beaulieu, C. F., Rosenberg, J., Rubin, D. L., Yao, D., Napel, S. 2015; 2 (2): 025501-?

  Abstract

  We aim to develop a better understanding of perception of similarity in focal computed tomography (CT) liver images to determine the feasibility of techniques for developing reference sets for training and validating content-based image retrieval systems. In an observer study, four radiologists and six nonradiologists assessed overall similarity and similarity in 5 image features in 136 pairs of focal CT liver lesions. We computed intra- and inter-reader agreements in these similarity ratings and viewed the distributions of the ratings. The readers' ratings of overall similarity and similarity in each feature primarily appeared to be bimodally distributed. Median Kappa scores for intra-reader agreement ranged from 0.57 to 0.86 in the five features and from 0.72 to 0.82 for overall similarity. Median Kappa scores for inter-reader agreement ranged from 0.24 to 0.58 in the five features and were 0.39 for overall similarity. There was no significant difference in agreement for radiologists and nonradiologists. Our results show that developing perceptual similarity reference standards is a complex task. Moderate to high inter-reader variability precludes ease of dividing up the workload of rating perceptual similarity among many readers, while low intra-reader variability may make it possible to acquire large volumes of data by asking readers to view image pairs over many sessions.

  View details for DOI 10.1117/1.JMI.2.2.025501

  View details for PubMedID 26158112

  View details for PubMedCentralID PMC4478987

• Characterizing Search, Recognition, and Decision in the Detection of Lung Nodules on CT Scans: Elucidation with Eye Tracking RADIOLOGY Rubin, G. D., Roos, J. E., Tall, M., Harrawood, B., Bag, S., Ly, D. L., Seaman, D. M., Hurwitz, L. M., Napel, S., Choudhury, K. R. 2015; 274 (1): 276-286

  Abstract

  To determine the effectiveness of radiologists' search, recognition, and acceptance of lung nodules on computed tomographic (CT) images by using eye tracking.This study was performed with a protocol approved by the institutional review board. All study subjects provided informed consent, and all private health information was protected in accordance with HIPAA. A remote eye tracker was used to record time-varying gaze paths while 13 radiologists interpreted 40 lung CT images with an average of 3.9 synthetic nodules (5-mm diameter) embedded randomly in the lung parenchyma. The radiologists' gaze volumes ( GV gaze volume s) were defined as the portion of the lung parenchyma within 50 pixels (approximately 3 cm) of all gaze points. The fraction of the total lung volume encompassed within the GV gaze volume s, the fraction of lung nodules encompassed within each GV gaze volume (search effectiveness), the fraction of lung nodules within the GV gaze volume detected by the reader (recognition-acceptance effectiveness), and overall sensitivity of lung nodule detection were measured.Detected nodules were within 50 pixels of the nearest gaze point for 990 of 992 correct detections. On average, radiologists searched 26.7% of the lung parenchyma in 3 minutes and 16 seconds and encompassed between 86 and 143 of 157 nodules within their GV gaze volume s. Once encompassed within their GV gaze volume , the average sensitivity of nodule recognition and acceptance ranged from 47 of 100 nodules to 103 of 124 nodules (sensitivity, 0.47-0.82). Overall sensitivity ranged from 47 to 114 of 157 nodules (sensitivity, 0.30-0.73) and showed moderate correlation (r = 0.62, P = .02) with the fraction of lung volume searched.Relationships between reader search, recognition and acceptance, and overall lung nodule detection rate can be studied with eye tracking. Radiologists appear to actively search less than half of the lung parenchyma, with substantial interreader variation in volume searched, fraction of nodules included within the search volume, sensitivity for nodules within the search volume, and overall detection rate.

  View details for DOI 10.1148/radiol.14132918

  View details for Web of Science ID 000348699400032

  View details for PubMedID 25325324

• GLIOBLASTOMA SUBTYPES DEFINED BY QUANTITATIVE IMAGING MAP TO DIFFERENT CANONICAL SIGNALING PATHWAYS Itakura, H., Achrol, A., Loya, J., Mitchell, L., Azad, T., Echegaray, S., Yeom, K., Napel, S., Harsh, G., Gevaert, O. OXFORD UNIV PRESS INC. 2014

  View details for DOI 10.1093/neuonc/nou264.36

  View details for Web of Science ID 000350452200580

• On combining image-based and ontological semantic dissimilarities for medical image retrieval applications. Medical image analysis Kurtz, C., Depeursinge, A., Napel, S., Beaulieu, C. F., Rubin, D. L. 2014; 18 (7): 1082-1100

  Abstract

  Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.

  View details for DOI 10.1016/j.media.2014.06.009

  View details for PubMedID 25036769

  View details for PubMedCentralID PMC4173098

• Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., Plevritis, S. K. 2014; 273 (1): 168-174

  Abstract

  To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.

  View details for DOI 10.1148/radiol.14131731

  View details for PubMedID 24827998

• NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures TRANSLATIONAL ONCOLOGY Colen, R., Foster, I., Gatenby, R., Giger, M. E., Gillies, R., Gutman, D., Heller, M., Jain, R., Madabhushi, A., Madhavan, S., Napel, S., Rao, A., Saltz, J., Tatum, J., Verhaak, R., Whitman, G. 2014; 7 (5): 556-569

  Abstract

  The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.

  View details for DOI 10.1016/j.tranon.2014.07.007

  View details for Web of Science ID 000348837100004

  View details for PubMedID 25389451

  View details for PubMedCentralID PMC4225695

• Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features RADIOLOGY Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., Plevritis, S. K. 2014; 273 (1): 168-174

  Abstract

  To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.

  View details for DOI 10.1148/radiol.14131731

  View details for Web of Science ID 000344232100019

  View details for PubMedCentralID PMC4263772

• GLIOBLASTOMA SUBTYPES DEFINED BY QUANTITATIVE IMAGING MAP TO DIFFERENT CANONICAL SIGNALING PATHWAYS Itakura, H., Achrol, A., Loya, J., Mitchell, L., Azad, T., Echegaray, S., Yeom, K., Napel, S., Harsh, G., Gevaert, O. OXFORD UNIV PRESS INC. 2014

  View details for Web of Science ID 000344235700094

• Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT. IEEE transactions on medical imaging Depeursinge, A., Kurtz, C., Beaulieu, C., Napel, S., Rubin, D. 2014; 33 (8): 1669-1676

  Abstract

  We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

  View details for DOI 10.1109/TMI.2014.2321347

  View details for PubMedID 24808406

  View details for PubMedCentralID PMC4129229

• A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations JOURNAL OF BIOMEDICAL INFORMATICS Kurtz, C., Beaulieu, C. F., Napel, S., Rubin, D. L. 2014; 49: 227-244

  Abstract

  Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.

  View details for DOI 10.1016/j.jbi.2014.02.018

  View details for Web of Science ID 000337772200023

  View details for PubMedCentralID PMC4058405

• A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations. Journal of biomedical informatics Kurtz, C., Beaulieu, C. F., Napel, S., Rubin, D. L. 2014; 49: 227-244

  Abstract

  Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.

  View details for DOI 10.1016/j.jbi.2014.02.018

  View details for PubMedID 24632078

• CT Angiography after 20 Years: A Transformation in Cardiovascular Disease Characterization Continues to Advance RADIOLOGY Rubin, G. D., Leipsic, J., Schoepf, U. J., Fleischmann, D., Napel, S. 2014; 271 (3): 633-652

  Abstract

  Through a marriage of spiral computed tomography (CT) and graphical volumetric image processing, CT angiography was born 20 years ago. Fueled by a series of technical innovations in CT and image processing, over the next 5-15 years, CT angiography toppled conventional angiography, the undisputed diagnostic reference standard for vascular disease for the prior 70 years, as the preferred modality for the diagnosis and characterization of most cardiovascular abnormalities. This review recounts the evolution of CT angiography from its development and early challenges to a maturing modality that has provided unique insights into cardiovascular disease characterization and management. Selected clinical challenges, which include acute aortic syndromes, peripheral vascular disease, aortic stent-graft and transcatheter aortic valve assessment, and coronary artery disease, are presented as contrasting examples of how CT angiography is changing our approach to cardiovascular disease diagnosis and management. Finally, the recently introduced capabilities for multispectral imaging, tissue perfusion imaging, and radiation dose reduction through iterative reconstruction are explored with consideration toward the continued refinement and advancement of CT angiography.

  View details for DOI 10.1148/radiol.14132232

  View details for Web of Science ID 000336894600004

  View details for PubMedID 24848958

• CREATING A RADIOGENOMICS MAP OF MULTI-OMICS AND QUANTITATIVE IMAGE FEATURES IN GLIOBLASTOMA MULTIFORME Gevaert, O., Mitchell, L., Achrol, A., Xu, J., Steinberg, G., Cheshier, S., Napel, S., Zaharchuk, G., Plevritis, S. OXFORD UNIV PRESS INC. 2013: 140–41

  View details for Web of Science ID 000327456200564

• Modeling Perceptual Similarity Measures in CT Images of Focal Liver Lesions JOURNAL OF DIGITAL IMAGING Faruque, J., Rubin, D. L., Beaulieu, C. F., Napel, S. 2013; 26 (4): 714-720

  Abstract

  Motivation: A gold standard for perceptual similarity in medical images is vital to content-based image retrieval, but inter-reader variability complicates development. Our objective was to develop a statistical model that predicts the number of readers (N) necessary to achieve acceptable levels of variability. Materials and Methods: We collected 3 radiologists' ratings of the perceptual similarity of 171 pairs of CT images of focal liver lesions rated on a 9-point scale. We modeled the readers' scores as bimodal distributions in additive Gaussian noise and estimated the distribution parameters from the scores using an expectation maximization algorithm. We (a) sampled 171 similarity scores to simulate a ground truth and (b) simulated readers by adding noise, with standard deviation between 0 and 5 for each reader. We computed the mean values of 2-50 readers' scores and calculated the agreement (AGT) between these means and the simulated ground truth, and the inter-reader agreement (IRA), using Cohen's Kappa metric. Results: IRA for the empirical data ranged from =0.41 to 0.66. For between 1.5 and 2.5, IRA between three simulated readers was comparable to agreement in the empirical data. For these values , AGT ranged from =0.81 to 0.91. As expected, AGT increased with N, ranging from =0.83 to 0.92 for N = 2 to 50, respectively, with =2. Conclusion: Our simulations demonstrated that for moderate to good IRA, excellent AGT could nonetheless be obtained. This model may be used to predict the required N to accurately evaluate similarity in arbitrary size datasets.

  View details for DOI 10.1007/s10278-012-9557-4

  View details for Web of Science ID 000322434700017

  View details for PubMedID 23254627

• Uncluttered Single-Image Visualization of Vascular Structures Using GPU and Integer Programming IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS Won, J., Jeon, Y., Rosenberg, J. K., Yoon, S., Rubin, G. D., Napel, S. 2013; 19 (1): 81-93

  Abstract

  Direct projection of 3D branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single 2D stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm.

  View details for DOI 10.1109/TVCG.2012.25

  View details for Web of Science ID 000311124600008

  View details for PubMedCentralID PMC3758916

• Prognostic PET F-18-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non-Small Cell Lung Cancer CANCER RESEARCH Nair, V. S., Gevaert, O., Davidzon, G., Napel, S., Graves, E. E., Hoang, C. D., Shrager, J. B., Quon, A., Rubin, D. L., Plevritis, S. K. 2012; 72 (15): 3725-3734

  Abstract

  Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non-small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (LY6E, RNF149, MCM6, and FAP) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUV(max)), SUV(variance), and SUV(PCA2)], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49-13.8] and validation (HR, 6.12; CI, 1.08-34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis.

  View details for DOI 10.1158/0008-5472.CAN-11-3943

  View details for PubMedID 22710433

• Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results RADIOLOGY Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., Rubin, D. L., Napel, S., Plevritis, S. K. 2012; 264 (2): 387-396

  Abstract

  To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

  View details for DOI 10.1148/radiol.12111607

  View details for PubMedID 22723499

• Radiogenomic analysis indicates MR images are potentially predictive of EGFR mutation status in glioblastoma multiforme Gevaert, O., Mitchell, L., Xu, J., Yu, C., Rubin, D., Zaharchuk, G., Napel, S., Plevritis, S. AMER ASSOC CANCER RESEARCH. 2012

  View details for DOI 10.1158/1538-7445.AM2012-5561

  View details for Web of Science ID 000209701601072

• A Comprehensive Descriptor of Shape: Method and Application to Content-Based Retrieval of Similar Appearing Lesions in Medical Images JOURNAL OF DIGITAL IMAGING Xu, J., Faruque, J., Beaulieu, C. F., Rubin, D., Napel, S. 2012; 25 (1): 121-128

  Abstract

  We have developed a method to quantify the shape of liver lesions in CT images and to evaluate its performance for retrieval of images with similarly-shaped lesions. We employed a machine learning method to combine several shape descriptors and defined similarity measures for a pair of shapes as a weighted combination of distances calculated based on each feature. We created a dataset of 144 simulated shapes and established several reference standards for similarity and computed the optimal weights so that the retrieval result agrees best with the reference standard. Then we evaluated our method on a clinical database consisting of 79 portal-venous-phase CT liver images, where we derived a reference standard of similarity from radiologists' visual evaluation. Normalized Discounted Cumulative Gain (NDCG) was calculated to compare this ordering with the expected ordering based on the reference standard. For the simulated lesions, the mean NDCG values ranged from 91% to 100%, indicating that our methods for combining features were very accurate in representing true similarity. For the clinical images, the mean NDCG values were still around 90%, suggesting a strong correlation between the computed similarity and the independent similarity reference derived the radiologists.

  View details for DOI 10.1007/s10278-011-9388-8

  View details for Web of Science ID 000304113400018

  View details for PubMedID 21547518

  View details for PubMedCentralID PMC3264721

• Accuracy of a Remote Eye Tracker for Radiologic Observer Studies: Effects of Calibration and Recording Environment ACADEMIC RADIOLOGY Tall, M., Choudhury, K. R., Napel, S., Roos, J. E., Rubin, G. D. 2012; 19 (2): 196-202

  Abstract

  To determine the accuracy and reproducibility of a remote eye-tracking system for studies of observer gaze while displaying volumetric chest computed tomography (CT) images.Four participants performed calibrations using three different gray-scale backgrounds (black, gray, and white). Each participant then observed a three-dimensional 10-point test pattern embedded in five Digital Imaging and Communications in Medicine (DICOM) datasets (test backgrounds): a full 190-section chest CT scan, 190 copies of a single chest CT section, and three 190-section datasets of homogeneous intensity (black, gray, and white).Significant variances between participants, calibration backgrounds, and test backgrounds were observed. The least mean systematic error (deviation of recorded gaze position from target) was obtained when the calibration background and test background were black (27 pixels). Systematic error increased when displaying a test background that deviated from the calibration background intensity. Hence, the largest mean systematic error occurred when calibrating to a black background and displaying a white background (67 pixels). For complex chest CT volumes the white calibration background performed best (38 pixels). An angular analysis of the systematic error was performed and demonstrated that the systemic error primarily affects the vertical position of the estimated gaze position.Our findings indicate a potential source of systematic error during gaze recording in a dynamic environment and highlight the importance of configuring the calibration procedure according to the brightness of the display. We recommend that investigators develop routines for postcalibration accuracy measurement and report the effective accuracy for the display environment in which the data are collected.

  View details for DOI 10.1016/j.acra.2011.10.011

  View details for Web of Science ID 000299245100011

  View details for PubMedID 22212422

• Automatic annotation of radiological observations in liver CT images. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium Gimenez, F., Xu, J., Liu, Y., Liu, T., Beaulieu, C., Rubin, D., Napel, S. 2012; 2012: 257-263

  Abstract

  We aim to predict radiological observations using computationally-derived imaging features extracted from computed tomography (CT) images. We created a dataset of 79 CT images containing liver lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Traditional logistic regression was compared to L(1)-regularized logistic regression (LASSO) in order to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hypervascular attenuation, and homogeneous retention were predicted well by computational features. By exploiting relationships between computational and semantic features, this approach could lead to more accurate and efficient radiology reporting.

  View details for PubMedID 23304295

• Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval MEDICAL PHYSICS Xu, J., Napel, S., Greenspan, H., Beaulieu, C. F., Neeraj, A., Rubin, D. L. 2012; 39 (9): 5405-5418
• Automated Tracing of the Adventitial Contour of Aortoiliac and Peripheral Arterial Walls in CT Angiography (CTA) to Allow Calculation of Non-calcified Plaque Burden JOURNAL OF DIGITAL IMAGING Raman, B., Raman, R., Rubin, G. D., Napel, S. 2011; 24 (6): 1078-1086

  Abstract

  Aortoiliac and lower extremity arterial atherosclerotic plaque burden is a risk factor for the development of visceral and peripheral ischemic and aneurismal vascular disease. While prior research allows automated quantification of calcified plaque in these body regions using CT angiograms, no automated method exists to quantify soft plaque. We developed an automatic algorithm that defines the outer wall contour and wall thickness of vessels to quantify non-calcified plaque in CT angiograms of the chest, abdomen, pelvis, and lower extremities. The algorithm encodes the search space as a constrained graph and calculates the outer wall contour by deriving a minimum cost path through the graph, following the visible outer wall contour while minimizing path tortuosity. Our algorithm was statistically equivalent to a reference standard made by two reviewers. Absolute error was 1.9 ± 2.3% compared to the inter-observer variability of 3.9 ± 3.6%. Wall thickness in vessels with atherosclerosis was 3.4 ± 1.6 mm compared to 1.2 ± 0.4 mm in normal vessels. The algorithm shows promise as a tool for quantification of non-calcified plaque in CT angiography. When combined with previous research, our method has the potential to quantify both non-calcified and calcified plaque in all clinically significant systemic arteries, from the thoracic aorta to the arteries of the calf, over a wide range of diameters. This algorithm has the potential to enable risk stratification of patients and facilitate investigations into the relationships between asymptomatic atherosclerosis and a variety of behavioral, physiologic, pathologic, and genotypic conditions.

  View details for DOI 10.1007/s10278-011-9373-2

  View details for Web of Science ID 000296882500012

  View details for PubMedID 21547519

  View details for PubMedCentralID PMC3222556

• Automated temporal tracking and segmentation of lymphoma on serial CT examinations MEDICAL PHYSICS Xu, J., Greenspan, H., Napel, S., Rubin, D. L. 2011; 38 (11): 5879-5886

  Abstract

  It is challenging to reproducibly measure and compare cancer lesions on numerous follow-up studies; the process is time-consuming and error-prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams.Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow-up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non-nodal tissue surrounding the lymph node and to map them onto the follow-up scans, in order to reduce the search space to locate the lymph node on the follow-up scans. Adaptive region-growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow-up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a "reference standard" was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist-generated contours.Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7 ± 9.7% s.d., and the average Hausdorff distance was 3.2 ± 1.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%.Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.

  View details for DOI 10.1118/1.3643027

  View details for Web of Science ID 000296534000008

  View details for PubMedID 22047352

  View details for PubMedCentralID PMC3210189

• Managing Biomedical Image Metadata for Search and Retrieval of Similar Images JOURNAL OF DIGITAL IMAGING Korenblum, D., Rubin, D., Napel, S., Rodriguez, C., Beaulieu, C. 2011; 24 (4): 739-748

  Abstract

  Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations ("semantic" metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system's "match observations" function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

  View details for DOI 10.1007/s10278-010-9328-z

  View details for Web of Science ID 000292888700020

  View details for PubMedID 20844917

  View details for PubMedCentralID PMC3138941

• Integrating medical images and transcriptomic data in non-small cell lung cancer Gevaert, O., Xu, J., Hoang, C., Leung, A., Quon, A., Rubin, D., Napel, S., Plevritis, S. AMER ASSOC CANCER RESEARCH. 2011

  View details for DOI 10.1158/1538-7445.AM2011-4148

  View details for Web of Science ID 000209701405023

• Content-Based Image Retrieval in Radiology: Current Status and Future Directions JOURNAL OF DIGITAL IMAGING Akgul, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., Acar, B. 2011; 24 (2): 208-222

  Abstract

  Diagnostic radiology requires accurate interpretation of complex signals in medical images. Content-based image retrieval (CBIR) techniques could be valuable to radiologists in assessing medical images by identifying similar images in large archives that could assist with decision support. Many advances have occurred in CBIR, and a variety of systems have appeared in nonmedical domains; however, permeation of these methods into radiology has been limited. Our goal in this review is to survey CBIR methods and systems from the perspective of application to radiology and to identify approaches developed in nonmedical applications that could be translated to radiology. Radiology images pose specific challenges compared with images in the consumer domain; they contain varied, rich, and often subtle features that need to be recognized in assessing image similarity. Radiology images also provide rich opportunities for CBIR: rich metadata about image semantics are provided by radiologists, and this information is not yet being used to its fullest advantage in CBIR systems. By integrating pixel-based and metadata-based image feature analysis, substantial advances of CBIR in medicine could ensue, with CBIR systems becoming an important tool in radiology practice.

  View details for DOI 10.1007/s10278-010-9290-9

  View details for Web of Science ID 000288394700007

  View details for PubMedID 20376525

  View details for PubMedCentralID PMC3056970

• Automated Quantification of Aortoaortic and Aortoiliac Angulation for Computed Tomographic Angiography of Abdominal Aortic Aneurysms before Endovascular Repair: Preliminary Study JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY Raman, B., Raman, R., Napel, S., Rubin, G. D. 2010; 21 (11): 1746-1750

  Abstract

  The degree of angulation of abdominal aortic aneurysms (AAAs) has emerged as an important factor in assessing eligibility for endovascular aneurysm repair (EVAR). The authors developed an automatic algorithm that reduces variability of measurement of aortoiliac angulation. For highly structured manual methods, intraobserver variability was 8.2 degrees ± 5.0 (31% ± 20) and interobserver variability was 5.6 degrees ± 2.5 (20% ± 9.1) compared with 0.6 degrees ± 0.8 (2.2% ± 3.6) (intraobserver) and 0.4 degrees ± 0.4 (1.4% ± 1.9) (interobserver) for the automatic algorithm (P < .01). In phantoms, the automatically measured angles were equivalent to reference values (P < .05). This algorithm was also faster than manual methods and has the potential to enhance the clinical utility and reliability of computed tomographic angiography for preoperative assessment for EVAR.

  View details for DOI 10.1016/j.jvir.2010.07.025

  View details for Web of Science ID 000284244200016

  View details for PubMedID 20932776

  View details for PubMedCentralID PMC2966507

• Automated Retrieval of CT Images of Liver Lesions on the Basis of Image Similarity: Method and Preliminary Results RADIOLOGY Napel, S. A., Beaulieu, C. F., Rodriguez, C., Cui, J., Xu, J., Gupta, A., Korenblum, D., Greenspan, H., Ma, Y., Rubin, D. L. 2010; 256 (1): 243-252

  Abstract

  To develop a system to facilitate the retrieval of radiologic images that contain similar-appearing lesions and to perform a preliminary evaluation of this system with a database of computed tomographic (CT) images of the liver and an external standard of image similarity.Institutional review board approval was obtained for retrospective analysis of deidentified patient images. Thereafter, 30 portal venous phase CT images of the liver exhibiting one of three types of liver lesions (13 cysts, seven hemangiomas, 10 metastases) were selected. A radiologist used a controlled lexicon and a tool developed for complete and standardized description of lesions to identify and annotate each lesion with semantic features. In addition, this software automatically computed image features on the basis of image texture and boundary sharpness. Semantic and computer-generated features were weighted and combined into a feature vector representing each image. An independent reference standard was created for pairwise image similarity. This was used in a leave-one-out cross-validation to train weights that optimized the rankings of images in the database in terms of similarity to query images. Performance was evaluated by using precision-recall curves and normalized discounted cumulative gain (NDCG), a common measure for the usefulness of information retrieval.When used individually, groups of semantic, texture, and boundary features resulted in various levels of performance in retrieving relevant lesions. However, combining all features produced the best overall results. Mean precision was greater than 90% at all values of recall, and mean, best, and worst case retrieval accuracy was greater than 95%, 100%, and greater than 78%, respectively, with NDCG.Preliminary assessment of this approach shows excellent retrieval results for three types of liver lesions visible on portal venous CT images, warranting continued development and validation in a larger and more comprehensive database.

  View details for DOI 10.1148/radiol.10091694

  View details for Web of Science ID 000279106900029

  View details for PubMedID 20505065

  View details for PubMedCentralID PMC2897688

• Assessing operating characteristics of CAD algorithms in the absence of a gold standard MEDICAL PHYSICS Choudhury, K. R., Paik, D. S., Yi, C. A., Napel, S., Roos, J., Rubin, G. D. 2010; 37 (4): 1788-1795

  Abstract

  The authors examine potential bias when using a reference reader panel as "gold standard" for estimating operating characteristics of CAD algorithms for detecting lesions. As an alternative, the authors propose latent class analysis (LCA), which does not require an external gold standard to evaluate diagnostic accuracy.A binomial model for multiple reader detections using different diagnostic protocols was constructed, assuming conditional independence of readings given true lesion status. Operating characteristics of all protocols were estimated by maximum likelihood LCA. Reader panel and LCA based estimates were compared using data simulated from the binomial model for a range of operating characteristics. LCA was applied to 36 thin section thoracic computed tomography data sets from the Lung Image Database Consortium (LIDC): Free search markings of four radiologists were compared to markings from four different CAD assisted radiologists. For real data, bootstrap-based resampling methods, which accommodate dependence in reader detections, are proposed to test of hypotheses of differences between detection protocols.In simulation studies, reader panel based sensitivity estimates had an average relative bias (ARB) of -23% to -27%, significantly higher (p-value < 0.0001) than LCA (ARB--2% to -6%). Specificity was well estimated by both reader panel (ARB -0.6% to -0.5%) and LCA (ARB 1.4%-0.5%). Among 1145 lesion candidates LIDC considered, LCA estimated sensitivity of reference readers (55%) was significantly lower (p-value 0.006) than CAD assisted readers' (68%). Average false positives per patient for reference readers (0.95) was not significantly lower (p-value 0.28) than CAD assisted readers' (1.27).Whereas a gold standard based on a consensus of readers may substantially bias sensitivity estimates, LCA may be a significantly more accurate and consistent means for evaluating diagnostic accuracy.

  View details for DOI 10.1118/1.3352687

  View details for Web of Science ID 000276211200044

  View details for PubMedID 20443501

  View details for PubMedCentralID PMC2864671

• Computer-aided detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance EUROPEAN RADIOLOGY Roos, J. E., Paik, D., Olsen, D., Liu, E. G., Chow, L. C., Leung, A. N., Mindelzun, R., Choudhury, K. R., Naidich, D. P., Napel, S., Rubin, G. D. 2010; 20 (3): 549-557

  Abstract

  The diagnostic performance of radiologists using incremental CAD assistance for lung nodule detection on CT and their temporal variation in performance during CAD evaluation was assessed.CAD was applied to 20 chest multidetector-row computed tomography (MDCT) scans containing 190 non-calcified > or =3-mm nodules. After free search, three radiologists independently evaluated a maximum of up to 50 CAD detections/patient. Multiple free-response ROC curves were generated for free search and successive CAD evaluation, by incrementally adding CAD detections one at a time to the radiologists' performance.The sensitivity for free search was 53% (range, 44%-59%) at 1.15 false positives (FP)/patient and increased with CAD to 69% (range, 59-82%) at 1.45 FP/patient. CAD evaluation initially resulted in a sharp rise in sensitivity of 14% with a minimal increase in FP over a time period of 100 s, followed by flattening of the sensitivity increase to only 2%. This transition resulted from a greater prevalence of true positive (TP) versus FP detections at early CAD evaluation and not by a temporal change in readers' performance. The time spent for TP (9.5 s +/- 4.5 s) and false negative (FN) (8.4 s +/- 6.7 s) detections was similar; FP decisions took two- to three-times longer (14.4 s +/- 8.7 s) than true negative (TN) decisions (4.7 s +/- 1.3 s).When CAD output is ordered by CAD score, an initial period of rapid performance improvement slows significantly over time because of non-uniformity in the distribution of TP CAD output and not to a changing reader performance over time.

  View details for DOI 10.1007/s00330-009-1596-y

  View details for Web of Science ID 000274544800005

  View details for PubMedID 19760237

• Imaging informatics: toward capturing and processing semantic information in radiology images. Yearbook of medical informatics Rubin, D. L., Napel, S. 2010: 34-42

  Abstract

  To identify challenges and opportunities in imaging informatics that can lead to the use of images for discovery, and that can potentially improve the diagnostic accuracy of imaging professionals.Recent articles on imaging informatics and related articles from PubMed were reviewed and analyzed. Some new developments and challenges that recent research in imaging informatics will meet are identified and discussed.While much literature continues to be devoted to traditional imaging informatics topics of image processing, visualization, and computerized detection, three new trends are emerging: (1) development of ontologies to describe radiology reports and images, (2) structured reporting and image annotation methods to make image semantics explicit and machine-accessible, and (3) applications that use semantic image information for decision support to improve radiologist interpretation performance. The informatics methods being developed have similarities and synergies with recent work in the biomedical informatics community that leverage large high-throughput data sets, and future research in imaging informatics will build on these advances to enable discovery by mining large image databases.Imaging informatics is beginning to develop and apply knowledge representation and analysis methods to image datasets. This type of work, already commonplace in biomedical research with large scale molecular and clinical datasets, will lead to new ways for computers to work with image data. The new advances hold promise for integrating imaging with the rest of the patient record as well as molecular data, for new data-driven discoveries in imaging analogous to that in bioinformatics, and for improved quality of radiology practice.

  View details for PubMedID 20938568

• Uncluttered single-image visualization of the abdominal aortic vessel tree: Method and evaluation MEDICAL PHYSICS Won, J., Rosenberg, J., Rubin, G. D., Napel, S. 2009; 36 (11): 5245-5260

  Abstract

  The authors develop a method to visualize the abdominal aorta and its branches, obtained by CT or MR angiography, in a single 2D stylistic image without overlap among branches.The abdominal aortic vasculature is modeled as an articulated object whose underlying topology is a rooted tree. The inputs to the algorithm are the 3D centerlines of the abdominal aorta, its branches, and their associated diameter information. The visualization problem is formulated as an optimization problem that finds a spatial configuration of the bounding boxes of the centerlines most similar to the projection of the input into a given viewing direction (e.g., anteroposterior), while not introducing intersections among the boxes. The optimization algorithm minimizes a score function regarding the overlap of the bounding boxes and the deviation from the input. The output of the algorithm is used to produce a stylistic visualization, made of the 2D centerlines modulated by the associated diameter information, on a plane. The authors performed a preliminary evaluation by asking three radiologists to label 366 arterial branches from the 30 visualizations of five cases produced by the method. Each of the five patients was presented in six different variant images, selected from ten variants with the three lowest and three highest scores. For each label, they assigned confidence and distortion ratings (low/medium/high). They studied the association between the quantitative metrics measured from the visualization and the subjective ratings by the radiologists.All resulting visualizations were free from branch overlaps. Labeling accuracies of the three readers were 93.4%, 94.5%, and 95.4%, respectively. For the total of 1098 samples, the distortion ratings were low: 77.39%, medium: 10.48%, and high: 12.12%. The confidence ratings were low: 5.56%, medium: 16.50%, and high: 77.94%. The association study shows that the proposed quantitative metrics can predict a reader's subjective ratings and suggests that the visualization with the lowest score should be selected for readers.The method for eliminating misleading false intersections in 2D projections of the abdominal aortic tree conserves the overall shape and does not diminish accurate identifiability of the branches.

  View details for DOI 10.1118/1.3243866

  View details for Web of Science ID 000271217900045

  View details for PubMedID 19994535

  View details for PubMedCentralID PMC2774353

• Lower Extremity CT Angiography (CTA): Initial Evaluation of a Knowledge-Based Centerline Estimation Algorithm for Femoro-Popliteal Artery (FPA) Occlusions ACADEMIC RADIOLOGY Roos, J. E., Rakshe, T., Tran, D. N., Rosenberg, J., Straka, M., El-Helw, T., Sofilos, M. C., Napel, S., Fleischmann, D. 2009; 16 (6): 646-653

  Abstract

  Existing density- and gradient-based automated centerline-extraction algorithms fail in severely diseased or occluded arterial segments for the generation of curved planar reformations (CPRs). We aimed to quantitatively and qualitatively assess the precision of a knowledge-based centerline-extraction algorithm in patients with occluded femoro-popliteal artery (FPA).Computed tomography angiograms of 38 FPA occlusions (mean length 120 mm) were retrospectively identified. Reference centerlines were determined as the mean of eight manual expert readings. Each occlusion was also interpolated using a new knowledge-based algorithm (partial vector space projection [PVSP]), which uses shape information extracted from a separate database of 30 nondiseased FPAs. Precision of PVSP was quantified as the maximum departure error (MDE) from the standard of reference and the proportion of the interpolated centerlines remaining within an assumed vessel radius of 3 mm. Multiple regression method was used to determine the factors predicting the precision of the algorithm. CPR quality was independently assigned by two readers.The mean MDE (in mm) for occlusion lengths of <50 mm, 50-100 mm, 100-200 mm, and >200 mm was 0.95, 1.19, 1.40, and 2.25, for manual readings and 1.68, 2.90, 9.43, and 19.95 for PVSP, respectively. MDEs of the algorithm were completely contained within 3 mm of the assumed vessel radius in 20 of 38 occlusions. CPR quality was rated diagnostic by both readers in 23 of 38 occlusions.Shape-based centerline extraction of FPA occlusions in lower extremity CTA is feasible, and independent from local density and gradient information. PVSP centerline extraction allows interpolation of occlusions up to 100 mm within the variability of manually derived centerlines.

  View details for DOI 10.1016/j.acra.2009.01.015

  View details for Web of Science ID 000266210300002

  View details for PubMedID 19427978

• Dual-energy CT Discrimination of Iodine and Calcium: Experimental Results and Implications for Lower Extremity CT Angiography ACADEMIC RADIOLOGY Tran, D. N., Straka, M., Roos, J. E., Napel, S., Fleischmann, D. 2009; 16 (2): 160-171

  Abstract

  The purpose of this work was to measure the accuracy of dual-energy computed tomography for identifying iodine and calcium and to determine the effects of calcium suppression in phantoms and lower-extremity computed tomographic (CT) angiographic data sets.Using a three-material basis decomposition method for 80- and 140-kVp data, the accuracy of correctly identified contrast medium and calcium voxels and the mean attenuation before and after calcium suppression were computed. Experiments were first performed on a phantom of homogenous contrast medium and hydroxyapatite samples with mean attenuation of 57.2, 126, and 274 Hounsfield units (HU) and 50.0, 122, and 265 HU, respectively. Experiments were repeated in corresponding attenuation groups of voxels from manually segmented bones and contrast medium-enhanced arteries in a lower-extremity CT angiographic data set with mean attenuation of 293 and 434 HU, respectively. Calcium suppression in atherosclerotic plaques of a cadaveric specimen was also studied, using micro-computed tomography as a reference, and in a lower-extremity CT angiographic data set with substantial below-knee calcified plaques.Higher concentrations showed increased accuracy of iodine and hydroxyapatite identification of 87.4%, 99.7%, and 99.9% and 88.0%, 95.0%, and 99.9%, respectively. Calcium suppression was also more accurate with higher concentrations of iodine and hydroxyapatite, with mean attenuation after suppression of 47.1, 122, and 263 HU and 7.14, 11.6, and 12.6 HU, respectively. Similar patterns were seen in the corresponding attenuation groups of the contrast medium-enhanced arteries and bone in the clinical data set, which had overall accuracy of 81.3% and 78.9%, respectively, and mean attenuation after calcium suppression of 254 and 73.7 HU, respectively. The suppression of calcified atherosclerotic plaque was accurate compared with the micro-CT reference; however, the suppression in the clinical data set showed probable inappropriate suppression of the small vessels.Dual-energy computed tomography can detect and differentiate between contrast medium and calcified tissues, but its accuracy is dependent on the CT density of tissues and limited when CT attenuation is low.

  View details for DOI 10.1016/j.acra.2008.09.004

  View details for Web of Science ID 000262536500007

  View details for PubMedID 19124101

• Adaptive border marching algorithm: Automatic lung segmentation on chest CT images COMPUTERIZED MEDICAL IMAGING AND GRAPHICS Pu, J., Roos, J., Yi, C. A., Napel, S., Rubin, G. D., Paik, D. S. 2008; 32 (6): 452-462

  Abstract

  Segmentation of the lungs in chest-computed tomography (CT) is often performed as a preprocessing step in lung imaging. This task is complicated especially in presence of disease. This paper presents a lung segmentation algorithm called adaptive border marching (ABM). Its novelty lies in the fact that it smoothes the lung border in a geometric way and can be used to reliably include juxtapleural nodules while minimizing oversegmentation of adjacent regions such as the abdomen and mediastinum. Our experiments using 20 datasets demonstrate that this computational geometry algorithm can re-include all juxtapleural nodules and achieve an average oversegmentation ratio of 0.43% and an average under-segmentation ratio of 1.63% relative to an expert determined reference standard. The segmentation time of a typical case is under 1min on a typical PC. As compared to other available methods, ABM is more robust, more efficient and more straightforward to implement, and once the chest CT images are input, there is no further interaction needed from users. The clinical impact of this method is in potentially avoiding false negative CAD findings due to juxtapleural nodules and improving volumetry and doubling time accuracy.

  View details for DOI 10.1016/j.compmedimag.2008.04.005

  View details for Web of Science ID 000258739700004

  View details for PubMedID 18515044

  View details for PubMedCentralID PMC2536655

• An improved algorithm for femoropopliteal artery centerline restoration using prior knowledge of shapes and image space data MEDICAL PHYSICS Rakshe, T., Fleischmann, D., Rosenberg, J., Roos, J. E., Straka, M., Napel, S. 2008; 35 (7): 3372-3382

  Abstract

  Accurate arterial centerline extraction is essential for comprehensive visualization in CT Angiography. Time consuming manual tracking is needed when automated methods fail to track centerlines through severely diseased and occluded vessels. A previously described algorithm, Partial Vector Space Projection (PVSP), which uses vessel shape information from a database to bridge occlusions of the femoropopliteal artery, has a limited accuracy in long (>100 mm) occlusions. In this article we introduce a new algorithm, Intermediate Point Detection (IPD), which uses calcifications in the occluded artery to provide additional information about the location of the centerline to facilitate improvement in PVSP performance. It identifies calcified plaque in image space to find the most useful point within the occlusion to improve the estimate from PVSP. In this algorithm candidates for calcified plaque are automatically identified on axial CT slices in a restricted region around the estimate obtained from PVSP. A modified Canny edge detector identifies the edge of the calcified plaque and a convex polygon fit is used to find the edge of the calcification bordering the wall of the vessel. The Hough transform for circles estimates the center of the vessel on the slice, which serves as a candidate intermediate point. Each candidate is characterized by two scores based on radius and relative position within the occluded segment, and a polynomial function is constructed to define a net score representing the potential benefit of using this candidate for improving the centerline. We tested our approach in 44 femoropopliteal artery occlusions of lengths up to 398 mm in 30 patients with peripheral arterial occlusive disease. Centerlines were tracked manually by four-experts, twice each, with their mean serving as the reference standard. All occlusions were first interpolated with PVSP using a database of femoropopliteal arterial shapes obtained from a total of 60 subjects. Occlusions longer than 80 mm (N = 20) were then processed with the IPD algorithm, provided calcifications were found (N = 14). We used the maximum point-wise distance of an interpolated curve from the reference standard as our error metric. The IPD algorithm significantly reduced the average error of the initial PVSP from 2.76 to 1.86 mm (p < 0.01). The error was less than the clinically desirable 3 mm (smallest radius of the femoropopliteal artery) in 13 of 14 occlusions. The IPD algorithm achieved results within the range of the human readers in 11 of 14 cases. We conclude that the additional use of sparse but specific image space information, such as calcified atherosclerotic plaque, can be used to substantially improve the performance of a previously described knowledge-based method to restore the centerlines of femoropopliteal arterial occlusions.

  View details for DOI 10.1118/1.2940194

  View details for Web of Science ID 000257231700039

  View details for PubMedID 18697561

  View details for PubMedCentralID PMC2673553

• Improved speed of bone removal in computed tomographic angiography using automated targeted morphological separation: Method and evaluation in computed tomographic angiography of lower extremity occlusive disease JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Raman, R., Raman, B., Napel, S., Rubin, G. D. 2008; 32 (3): 485-491

  Abstract

  We developed an automated algorithm for bone removal in computed tomographic angiographic images that identifies and deletes connections between bone and vessels. Our automated algorithm is significantly faster than manual methods (2.45 minutes vs 73 minutes) and only generates about 2 small artifactual deletions per patient, mostly in the region of the ankle. Image quality was equivalent to manual methods. It shows promise as a tool for fast and accurate postprocessing of computed tomographic angiograms.

  View details for Web of Science ID 000256739400030

  View details for PubMedID 18520561

• Semiautomated quantification of the mass and distribution of vascular calcification with multidetector CT: Method and evaluation RADIOLOGY Raman, R., Raman, B., Napel, S., Rubin, G. D. 2008; 247 (1): 241-250

  Abstract

  Institutional review board approval was obtained for this HIPAA-compliant study. Informed consent was obtained for prospective evaluation in 21 asymptomatic volunteers (10 women, 11 men; mean age, 60 years) but waived for retrospective (10 patients with and five patients without disease) evaluation. Prospective validation was in phantoms. Quantification of mass and calcium distribution was performed with fast semiautomated method, without calibration. For actual versus measured mass in phantoms, R(2) was 0.98; absolute and percentage errors were 1.2 mg and 9.1%, respectively. In asymptomatic volunteers, mean interscan variability for calcium mass quantification in extracoronary arteries was 24.9 mg; mean was 991 units for Agatston scoring. In coronary arteries, mean variability was 5.5 mg; mean Agatston variability was 27.7 units. At retrospective computed tomography, mean total calcified mass was 321.3 mg. Accurate quantification of mass and distribution of calcification in simulated arteries with this method can be applied in vivo, with low interscan variability.

  View details for DOI 10.1148/radiol.2471062190

  View details for Web of Science ID 000254358600029

  View details for PubMedID 18292472

• Colon polyp detection using smoothed shape operators: Preliminary results MEDICAL IMAGE ANALYSIS Sundaram, P., Zomorodian, A., Beaulieu, C., Napel, S. 2008; 12 (2): 99-119

  Abstract

  Computer-aided detection (CAD) algorithms identify locations in computed tomographic (CT) images of the colon that are most likely to contain polyps. Existing CAD methods treat the CT data as a voxelized, volume image. They estimate a curvature-based feature at the mucosal surface voxels. However, curvature is a smooth notion, while our data are discrete and noisy. As a second order differential quantity, curvature amplifies noise. In this paper, we present the smoothed shape operators method (SSO), which uses a geometry processing approach. We extract a triangle mesh representation of the colon surface, and estimate curvature on this surface using the shape operator. We then smooth the shape operators on the surface iteratively. Throughout, we use techniques explicitly designed for discrete geometry. All our computation occurs on the surface, rather than in the voxel grid. We evaluate our algorithm on patient data and provide free-response receiver-operating characteristic performance analysis over all size ranges of polyps. We also provide confidence intervals for our performance estimates. We compare our performance with the surface normal overlap (SNO) method for the same data. A preliminary evaluation of our method on 35 patients yielded the following results (polyp diameter range; sensitivity; false positives/case): (10mm; 100%; 17.5), (5-10 mm; 89.7%, 21.23), (<5 mm; 59.1%; 23.9) and (overall; 80.3%; 23.9). The evaluation of the SNO method yielded: (10 mm; 75%; 17.5), (5-10 mm; 43.1%; 21.23), (<5 mm; 15.9%; 23.9) and (overall; 38.5%; 23.9).

  View details for DOI 10.1016/j.media.2007.08.001

  View details for Web of Science ID 000256156500002

  View details for PubMedID 17910934

• Learning-enhanced simulated annealing: method, evaluation, and application to lung nodule registration APPLIED INTELLIGENCE Sun, S., Zhuge, F., Rosenberg, J., Steiner, R. M., Rubin, G. D., Napel, S. 2008; 28 (1): 83-99

  View details for DOI 10.1007/s10489-007-0043-5

  View details for Web of Science ID 000251994900006

• ConTrack: Finding the most likely pathways between brain regions using diffusion tractography JOURNAL OF VISION Sherbondy, A. J., Dougherty, R. F., Ben-Shachar, M., Napel, S., Wandell, B. A. 2008; 8 (9)

  Abstract

  Magnetic resonance diffusion-weighted imaging coupled with fiber tractography (DFT) is the only non-invasive method for measuring white matter pathways in the living human brain. DFT is often used to discover new pathways. But there are also many applications, particularly in visual neuroscience, in which we are confident that two brain regions are connected, and we wish to find the most likely pathway forming the connection. In several cases, current DFT algorithms fail to find these candidate pathways. To overcome this limitation, we have developed a probabilistic DFT algorithm (ConTrack) that identifies the most likely pathways between two regions. We introduce the algorithm in three parts: a sampler to generate a large set of potential pathways, a scoring algorithm that measures the likelihood of a pathway, and an inferential step to identify the most likely pathways connecting two regions. In a series of experiments using human data, we show that ConTrack estimates known pathways at positions that are consistent with those found using a high quality deterministic algorithm. Further we show that separating sampling and scoring enables ConTrack to identify valid pathways, known to exist, that are missed by other deterministic and probabilistic DFT algorithms.

  View details for DOI 10.1167/8.9.15

  View details for Web of Science ID 000258709300015

  View details for PubMedID 18831651

  View details for PubMedCentralID PMC2696074

• Identifying the human optic radiation using diffusion imaging and fiber tractography JOURNAL OF VISION Sherbondy, A. J., Dougherty, R. F., Napel, S., Wandell, B. A. 2008; 8 (10)

  Abstract

  Measuring the properties of the white matter pathways from retina to cortex in the living human brain will have many uses for understanding visual performance and guiding clinical treatment. For example, identifying the Meyer's loop portion of the optic radiation (OR) has clinical significance because of the large number of temporal lobe resections. We use diffusion tensor imaging and fiber tractography (DTI-FT) to identify the most likely pathway between the lateral geniculate nucleus (LGN) and the calcarine sulcus in sixteen hemispheres of eight healthy volunteers. Quantitative population comparisons between DTI-FT estimates and published postmortem dissections match with a spatial precision of about 1 mm. The OR can be divided into three bundles that are segmented based on the direction of the fibers as they leave the LGN: Meyer's loop, central, and direct. The longitudinal and radial diffusivities of the three bundles do not differ within the measurement noise; there is a small difference in the radial diffusivity between the right and left hemispheres. We find that the anterior tip of Meyer's loop is 28 +/- 3 mm posterior to the temporal pole, and the population range is 1 cm. Hence, it is important to identify the location of this bundle in individual subjects or patients.

  View details for DOI 10.1167/8.10.12

  View details for Web of Science ID 000262231200013

  View details for PubMedID 19146354

  View details for PubMedCentralID PMC2759943

• A directional distance aided method for medical image segmentation MEDICAL PHYSICS Zhuge, F., Sun, S., Rubin, G., Napel, S. 2007; 34 (12): 4962-4976

  Abstract

  A challenging problem in image segmentation is preventing boundary leakage through poorly resolved edges because not enough local information can be provided along them. In this article, we propose a new directional distance aided image segmentation method, formulated under the level set framework, to prevent the leakage. At each evolution step, the zero level set is extracted and smoothed. For each point on the zero level set, a new directional distance (DD) term, defined as the vector starting from itself and pointing to its counterpart on the smoothed version of the zero level set, is calculated to measure its "degree of protrusion." The evolution speed of the points that are considered to be protruding out will be penalized. Other terms, e.g., curvature and gradient terms and user specified constraints, are used along with the DD term to influence the level set evolution. Our smoothing technique augments traditional Gaussian smoothing with a new antishrinkage operation. The novelty of our method is that the DD term does not depend on intensity or gradient boundaries to regulate the regional shape and, therefore, help prevent leakage and the method incorporates vertex-based curve/surface smoothing into curve evolution under the level set framework. Experimental results show that the new DDA method achieves promising results and reasonable stability in segmenting simulated objects as well as abdominal aortic aneurysms in computed tomography (CT) angiograms, in both 2D and 3D, by preventing leakage into adjacent structures while preserving local shape details.

  View details for DOI 10.1118/1.2804556

  View details for Web of Science ID 000251910200042

  View details for PubMedID 18196822

• Polyp enhancing level set evolution of colon wall: Method and pilot study IEEE TRANSACTIONS ON MEDICAL IMAGING Konukoglu, E., Acar, B., Paik, D. S., Beaulieu, C. F., Rosenberg, J., Napel, S. 2007; 26 (12): 1649-1656

  Abstract

  Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of colon cancer. In this work, we propose a colon wall evolution algorithm polyp enhancing level sets (PELS) based on the level-set formulation that regularizes and enhances polyps as a preprocessing step to CTC CAD algorithms. The underlying idea is to evolve the polyps towards spherical protrusions on the colon wall while keeping other structures, such as haustral folds, relatively unchanged and, thereby, potentially improve the performance of CTC CAD algorithms, especially for smaller polyps. To evaluate our methods, we conducted a pilot study using an arbitrarily chosen CTC CAD method, the surface normal overlap (SNO) CAD algorithm, on a nine patient CTC data set with 47 polyps of sizes ranging from 2.0 to 17.0 mm in diameter. PELS increased the maximum sensitivity by 8.1% (from 21/37 to 24/37) for small polyps of sizes ranging from 5.0 to 9.0 mm in diameter. This is accompanied by a statistically significant separation between small polyps and false positives. PELS did not change the CTC CAD performance significantly for larger polyps.

  View details for DOI 10.1109/TMI.2007.901429

  View details for Web of Science ID 000251376500004

  View details for PubMedID 18092735

• Femoropopliteal artery centerline interpolation using contralateral shape MEDICAL PHYSICS Tran, D. N., Fleischmann, D., Rakshe, T., Roos, J. E., Rosenberg, J., Straka, M., Napel, S. 2007; 34 (9): 3428-3435

  Abstract

  Curved planar reformation allows comprehensive visualization of arterial flow channels, providing information about calcified and noncalcified plaques and degrees of stenoses. Existing semiautomated centerline-extraction algorithms for curved planar reformation generation fail in severely diseased and occluded arteries. We explored whether contralateral shape information could be used to reconstruct centerlines through femoropopliteal occlusions. We obtained CT angiography data sets of 29 subjects (16m/13f, 19-86yo) without peripheral arterial occlusive disease and five consecutive subjects (1m/4f, 54-85yo) with unilateral femoropopliteal arterial occlusions. A gradient-based method was used to extract the femoropopliteal centerlines in nondiseased segments. Centerlines of the five occluded segments were manually determined by four experts, two times each. We interpolated missing centerlines in 2475 simulated occlusions of various occlusion lengths in nondiseased subjects. We used different curve registration methods (reflection, similarity, affine, and global polynomial) to align the nonoccluded segments, matched the end points of the occluded segments to the corresponding patent end points, and recorded maximum Euclidean distances to the known centerlines. We also compared our algorithm to an existing knowledge-based PCA interpolation algorithm using the nondiseased subjects. In the five subjects with real femoropopliteal occlusions, we measured the maximum Euclidean distance and the percentage of the interpolation that remained within a typical 3 mm radius vessel. In the nondiseased subjects, we found that the rigid registration methods were not significantly (p<0.750) different among themselves but were more accurate than the nonrigid methods (p<0.001). In simulations using nondiseased subjects, our method produced centerlines that stayed within 3 mm of a semiautomatically tracked centerline in occlusions up to 100 mm in length; however, the PCA method was significantly more accurate for all occlusions lengths. In the actual clinical cases, we found the following [occlusion length (mm):error (mm)]: 16.5:0.775, 42.0:1.54, 79.9:1.82, 145:3.23, and 292:6.13, which were almost always more accurate than the PCA algorithm. We conclude that the use of contralateral shape information, when available, is a promising method for the interpolation of centerlines through arterial occlusions.

  View details for DOI 10.1118/1.2759603

  View details for Web of Science ID 000249547200003

  View details for PubMedID 17926944

• Transparent rendering of intraluminal contrast for 3D polyp visualization at CT colonography JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Shi, R., Napel, S., Rosenberg, J. K., Shin, L. K., Walsh, C. F., Mogensen, M. A., Joshi, A. J., Pankhudi, P., Beaulieu, C. F. 2007; 31 (5): 773-779

  Abstract

  We developed a classifier that permits transparent rendering of both tagging material and air to facilitate interpretation of tagged computed tomographic (CT) colonography. With this technique, a reader can simultaneously appreciate polyps on endoluminal views both covered with tagging material and against air, along with unmodified 2-dimensional CT images. Evaluated with 49 polyps from 26 patients (data from public National Library of Medicine, Health Insurance Portability and Accountability Act compliant), 3 readers were able to determine the presence/absence of polyps in tagged locations with equivalent accuracy compared with polyps in air. This method offers an alternative way to visualize tagged CT colonography.

  View details for Web of Science ID 000249964800020

  View details for PubMedID 17895791

• Knowledge-based interpolation of curves: Application to femoropopliteal arterial centerline restoration MEDICAL IMAGE ANALYSIS Rakshe, T., Fleischmann, D., Rosenberg, J., Roos, J. E., Napel, S. 2007; 11 (2): 157-168

  Abstract

  We present a novel algorithm, Partial Vector Space Projection (PVSP), for estimation of missing data given a database of similar datasets, and demonstrate its use in restoring the centerlines through simulated occlusions of femoropopliteal arteries, derived from CT angiography data. The algorithm performs Principal Component Analysis (PCA) on a database of centerlines to obtain a set of orthonormal basis functions defined in a scaled and oriented frame of reference, and assumes that any curve not in the database can be represented as a linear combination of these basis functions. Using a database of centerlines derived from 30 normal femoropopliteal arteries, we evaluated the algorithm, and compared it to a correlation-based linear Minimum Mean Squared Error (MMSE) method, by deleting portions of a centerline for several occlusion lengths (OL: 10 mm, 25 mm, 50 mm, 75 mm, 100 mm, 125 mm, 150 mm, 175 mm and 200 mm). For each simulated occlusion, we projected the partially known dataset on the set of basis functions derived from the remaining 29 curves to restore the missing segment. We calculated the maximum point-wise distance (Maximum Departure or MD) between the actual and estimated centerline as the error metric. Mean (standard deviation) of MD increased from 0.18 (0.14) to 4.35 (2.23) as OL increased. The results were fairly accurate even for large occlusion lengths and are clinically useful. The results were consistently better than those using the MMSE method. Multivariate regression analysis found that OL and the root-mean-square error in the 2 cm proximal and distal to the occlusion accounted for most of the error.

  View details for DOI 10.1016/j.media.2006.11.005

  View details for Web of Science ID 000245596200005

  View details for PubMedID 17218147

  View details for PubMedCentralID PMC1989127

• Registration of lung nodules using a semi-rigid model: Method and preliminary results MEDICAL PHYSICS Sun, S., Rubin, G. D., Paik, D., Steiner, R. M., Zhuge, F., Napel, S. 2007; 34 (2): 613-626

  Abstract

  The tracking of lung nodules across computed tomography (CT) scans acquired at different times for the same patient is helpful for the determination of malignancy. We are developing a nodule registration system to facilitate this process. We propose to use a semi-rigid method that considers principal structures surrounding the nodule and allows relative movements among the structures. The proposed similarity metric, which evaluates both the image correlation and the degree of elastic deformation amongst the structures, is maximized by a two-layered optimization method, employing a simulated annealing framework. We tested our method by simulating five cases that represent physiological deformation as well as different nodule shape/size changes with time. Each case is made up of a source and target scan, where the source scan consists of a nodule-free patient CT volume into which we inserted ten simulated lung nodules, and the target scan is the result of applying a known, physiologically based nonrigid transformation to the nodule-free source scan, into which we inserted modified versions of the corresponding nodules at the same, known locations. Five different modification strategies were used, one for each of the five cases: (1) nodules maintain size and shape, (2) nodules disappear, (3) nodules shrink uniformly by a factor of 2, (4) nodules grow uniformly by a factor of 2, and (5) nodules grow nonuniformly. We also matched 97 real nodules in pairs of scans (acquired at different times) from 12 patients and compared our registration to a radiologist's visual determination. In the simulation experiments, the mean absolute registration errors were 1.0+/-0.8 mm (s.d.), 1.1+/-0.7 mm (s.d.), 1.0+/-0.7 mm (s.d.), 1.0+/-0.6 mm (s.d.), and 1.1+/- 0.9 mm (s.d.) for the five cases, respectively. For the 97 nodule pairs in 12 patient scans, the mean absolute registration error was 1.4+/-0.8 mm (s.d.).

  View details for DOI 10.1118/1.2432073

  View details for Web of Science ID 000244424200027

  View details for PubMedID 17388179

• Rotational roadmapping: a new image-based navigation technique for the interventional room. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Kukuk, M., Napel, S. 2007; 10: 636-643

  Abstract

  For decades, conventional 2D-roadmaping has been the method of choice for image-based guidewire navigation during endovascular procedures. Only recently have 3D-roadmapping techniques become available that are based on the acquisition and reconstruction of a 3D image of the vascular tree. In this paper, we present a new image-based navigation technique called RoRo (Rotational Roadmapping) that eliminates the guess-work inherent to the conventional 2D method, but does not require a 3D image. Our preliminary clinical results show that there are situations in which RoRo is preferred over the existing two methods, thus demonstrating potential for filling a clinical niche and complementing the spectrum of available navigation tools.

  View details for PubMedID 18044622

• Rotational roadmapping: A new image-based navigation technique for the interventional room 10th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2007) Kukuk, M., Napel, S. SPRINGER-VERLAG BERLIN. 2007: 636–43

  Abstract

  For decades, conventional 2D-roadmaping has been the method of choice for image-based guidewire navigation during endovascular procedures. Only recently have 3D-roadmapping techniques become available that are based on the acquisition and reconstruction of a 3D image of the vascular tree. In this paper, we present a new image-based navigation technique called RoRo (Rotational Roadmapping) that eliminates the guess-work inherent to the conventional 2D method, but does not require a 3D image. Our preliminary clinical results show that there are situations in which RoRo is preferred over the existing two methods, thus demonstrating potential for filling a clinical niche and complementing the spectrum of available navigation tools.

  View details for Web of Science ID 000250917700077

• Targeted 2D/3D registration using ray normalization and a hybrid optimizer MEDICAL PHYSICS Dey, J., Napel, S. 2006; 33 (12): 4730-4738

  Abstract

  X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell).

  View details for DOI 10.1118/1.2388156

  View details for Web of Science ID 000243137600030

  View details for PubMedID 17278825

• "Flying through" and "flying around" a PET/CT scan: Pilot study and development of 3D integrated F-18-FDG PET/CT for virtual bronchoscopy and colonoscopy JOURNAL OF NUCLEAR MEDICINE Quon, A., Napel, S., Beaulieu, C. F., Gambhir, S. S. 2006; 47 (7): 1081-1087

  Abstract

  The objective of this pilot project was to devise a new image acquisition and processing technique to produce PET/CT images rendered in 3-dimensional (3D) volume that can then be reviewed in several 3D formats such as virtual bronchoscopy and colonoscopy "fly-throughs" and external "fly-arounds."We tested the new imaging and processing protocol on 24 patients with various malignancies to determine whether it could dependably acquire and reformat standard tomographic 2-dimensional PET/CT images into 3D renderings.This new technique added helpful information to the diagnostic interpretation for 2 of the 24 patients. Further, in the 6 patients undergoing mediastinoscopy, bronchoscopy, or endoscopy, 3D imaging helped in preprocedural planning.In this initial study, we demonstrated both the feasibility of rendering PET/CT images into 3D volumes and the potential clinical utility of this technique for diagnostic lesion characterization and preprocedural planning.

  View details for Web of Science ID 000238879300008

  View details for PubMedID 16818940

• CT colonography: Influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection RADIOLOGY Shi, R., Schraedley-Desmond, P., Napel, S., Olcott, E. W., Jeffrey, R. B., Yee, J., Zalis, M. E., Margolis, D., Paik, D. S., Sherbondy, A. J., Sundaram, P., Beaulieu, C. F. 2006; 239 (3): 768-776

  Abstract

  To retrospectively determine if three-dimensional (3D) viewing improves radiologists' accuracy in classifying true-positive (TP) and false-positive (FP) polyp candidates identified with computer-aided detection (CAD) and to determine candidate polyp features that are associated with classification accuracy, with known polyps serving as the reference standard.Institutional review board approval and informed consent were obtained; this study was HIPAA compliant. Forty-seven computed tomographic (CT) colonography data sets were obtained in 26 men and 10 women (age range, 42-76 years). Four radiologists classified 705 polyp candidates (53 TP candidates, 652 FP candidates) identified with CAD; initially, only two-dimensional images were used, but these were later supplemented with 3D rendering. Another radiologist unblinded to colonoscopy findings characterized the features of each candidate, assessed colon distention and preparation, and defined the true nature of FP candidates. Receiver operating characteristic curves were used to compare readers' performance, and repeated-measures analysis of variance was used to test features that affect interpretation.Use of 3D viewing improved classification accuracy for three readers and increased the area under the receiver operating characteristic curve to 0.96-0.97 (P<.001). For TP candidates, maximum polyp width (P=.038), polyp height (P=.019), and preparation (P=.004) significantly affected accuracy. For FP candidates, colonic segment (P=.007), attenuation (P<.001), surface smoothness (P<.001), distention (P=.034), preparation (P<.001), and true nature of candidate lesions (P<.001) significantly affected accuracy.Use of 3D viewing increases reader accuracy in the classification of polyp candidates identified with CAD. Polyp size and examination quality are significantly associated with accuracy.

  View details for Web of Science ID 000237738600018

  View details for PubMedID 16714460

• An abdominal aortic aneurysm segmentation method: Level set with region and statistical information MEDICAL PHYSICS Zhuge, F., Rubin, G. D., Sun, S., Napel, S. 2006; 33 (5): 1440-1453

  Abstract

  We present a system for segmenting the human aortic aneurysm in CT angiograms (CTA), which, in turn, allows measurements of volume and morphological aspects useful for treatment planning. The system estimates a rough "initial surface," and then refines it using a level set segmentation scheme augmented with two external analyzers: The global region analyzer, which incorporates a priori knowledge of the intensity, volume, and shape of the aorta and other structures, and the local feature analyzer, which uses voxel location, intensity, and texture features to train and drive a support vector machine classifier. Each analyzer outputs a value that corresponds to the likelihood that a given voxel is part of the aneurysm, which is used during level set iteration to control the evolution of the surface. We tested our system using a database of 20 CTA scans of patients with aortic aneurysms. The mean and worst case values of volume overlap, volume error, mean distance error, and maximum distance error relative to human tracing were 95.3% +/- 1.4% (s.d.); worst case = 92.9%, 3.5% +/- 2.5% (s.d.); worst case = 7.0%, 0.6 +/- 0.2 mm (s.d.); worst case = 1.0 mm, and 5.2 +/- 2.3 mm (s.d.); worst case = 9.6 mm, respectively. When implemented on a 2.8 GHz Pentium IV personal computer, the mean time required for segmentation was 7.4 +/- 3.6 min (s.d.). We also performed experiments that suggest that our method is insensitive to parameter changes within 10% of their experimentally determined values. This preliminary study proves feasibility for an accurate, precise, and robust system for segmentation of the abdominal aneurysm from CTA data, and may be of benefit to patients with aortic aneurysms.

  View details for DOI 10.1118/1.2193247

  View details for Web of Science ID 000237673600027

  View details for PubMedID 16752579

• Flattening the abdominal aortic tree for effective visualization. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Won, J. H., Rubin, G. D., Napel, S. 2006; 1: 3345-3348

  Abstract

  We developed a novel visualization method for providing an uncluttered view of the abdominal aorta and its branches. The method abstracts the complex geometry of vessels using a convex primitive, and uses a sweep line algorithm to find a suboptimal placement of the primitive. The method was evaluated using 10 CT angiography datasets and resulted in a clear visualization with all cluttering intersections removed. The method can be used to convey clinical findings, including lumen patency and lesion locations, in a single two-dimensional image.

  View details for PubMedID 17946176

• Flattening the abdominal aortic tree for effective visualization 28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Won, J. H., Rubin, G. D., Napel, S. IEEE. 2006: 2098–2101

  View details for Web of Science ID 000247284702106

• Biomedical imaging research opportunities workshop II: Report and recommendations RADIOLOGY Partain, C. L., Chan, H. P., Gelovani, J. G., Giger, M. L., Izatt, J. A., Jolesz, F. A., Kandarpa, K., Li, K. C., McNitt-Gray, M., Napel, S., Summers, R. M., Gazelle, G. S. 2005; 236 (2): 389-403

  View details for DOI 10.1148/radiol.2362041876

  View details for Web of Science ID 000230670200005

  View details for PubMedID 16040898

• Alternative input devices for efficient navigation of large CT angiography data sets RADIOLOGY Sherboncly, A., Holmlund, D., Rubin, G. D., Schraedley, P. K., Winograd, T., Napel, S. 2005; 234 (2): 391-398

  Abstract

  To compare devices for the task of navigating through large computed tomographic (CT) data sets at a picture archiving and communication system workstation.The institutional review board approved this study, and all subjects provided informed consent. Five radiologists were asked to find 25 different vascular targets in three CT angiography data sets (average number of sections, 1025) by using several devices (trackball, tablet, jog-shuttle wheel, and mouse). For each trial, the total time to acquire the targets (T1) was recorded. A secondary study in which 13 nonradiologists performed seven trials with an artificial target inserted at a random location in the same image data was also performed. For each trial, the following items were recorded: time until first target sighting (t2), time to manipulate the device after seeing the target, sections traversed during t2 (d1), time from first sight to target acquisition (t4), sections traversed during t4 (d2), and total trial time. Statistical analysis involved repeated-measures analysis of variance (ANOVA) and pairwise comparisons.Repeated-measures ANOVA revealed that the device used had a significant (P < .05) effect on T1. Pairwise comparisons revealed that the trackball was significantly slower than the tablet (P < .05) and marginally slower than the jog-shuttle wheel (P < .10). Further repeated-measures ANOVA for each secondary outcome measure revealed significant differences between devices for all outcome measures (P < .005). Pairwise comparisons revealed the trackball to be significantly slower than the other devices in all measures (P < .05). The trackball was significantly (P < .05) more accurate than the other devices for d1 and d2.The trackball may not be the optimal device for navigation of large CT angiography data sets; the use of other existing devices may improve the efficiency of interpretation of these sets.

  View details for DOI 10.1148/radiol.2342032017

  View details for Web of Science ID 000226483200013

  View details for PubMedID 15670996

• Pulmonary nodules on multi-detector row CT scans: Performance comparison of radiologists and computer-aided detection RADIOLOGY Rubin, G. D., Lyo, J. K., Paik, D. S., Sherbondy, A. J., Chow, L. C., Leung, A. N., Mindelzun, R., Schraedley-Desmond, P. K., Zinck, S. E., Naidich, D. P., Napel, S. 2005; 234 (1): 274-283

  Abstract

  To compare the performance of radiologists and of a computer-aided detection (CAD) algorithm for pulmonary nodule detection on thin-section thoracic computed tomographic (CT) scans.The study was approved by the institutional review board. The requirement of informed consent was waived. Twenty outpatients (age range, 15-91 years; mean, 64 years) were examined with chest CT (multi-detector row scanner, four detector rows, 1.25-mm section thickness, and 0.6-mm interval) for pulmonary nodules. Three radiologists independently analyzed CT scans, recorded the locus of each nodule candidate, and assigned each a confidence score. A CAD algorithm with parameters chosen by using cross validation was applied to the 20 scans. The reference standard was established by two experienced thoracic radiologists in consensus, with blind review of all nodule candidates and free search for additional nodules at a dedicated workstation for three-dimensional image analysis. True-positive (TP) and false-positive (FP) results and confidence levels were used to generate free-response receiver operating characteristic (ROC) plots. Double-reading performance was determined on the basis of TP detections by either reader.The 20 scans showed 195 noncalcified nodules with a diameter of 3 mm or more (reference reading). Area under the alternative free-response ROC curve was 0.54, 0.48, 0.55, and 0.36 for CAD and readers 1-3, respectively. Differences between reader 3 and CAD and between readers 2 and 3 were significant (P < .05); those between CAD and readers 1 and 2 were not significant. Mean sensitivity for individual readings was 50% (range, 41%-60%); double reading resulted in increase to 63% (range, 56%-67%). With CAD used at a threshold allowing only three FP detections per CT scan, mean sensitivity was increased to 76% (range, 73%-78%). CAD complemented individual readers by detecting additional nodules more effectively than did a second reader; CAD-reader weighted kappa values were significantly lower than reader-reader weighted kappa values (Wilcoxon rank sum test, P < .05).With CAD used at a level allowing only three FP detections per CT scan, sensitivity was substantially higher than with conventional double reading.

  View details for DOI 10.1148/radiol.2341040589

  View details for Web of Science ID 000225864800038

  View details for PubMedID 15537839

• Registration of central paths and colonic polyps between supine and prone scans in computed tomography colonography: Pilot study MEDICAL PHYSICS Ping, L., Napel, S., Acar, B., Paik, D. S., Jeffrey, R. B., Beaulieu, C. F. 2004; 31 (10): 2912-2923

  Abstract

  Computed tomography colonography (CTC) is a minimally invasive method that allows the evaluation of the colon wall from CT sections of the abdomen/pelvis. The primary goal of CTC is to detect colonic polyps, precursors to colorectal cancer. Because imperfect cleansing and distension can cause portions of the colon wall to be collapsed, covered with water, and/or covered with retained stool, patients are scanned in both prone and supine positions. We believe that both reading efficiency and computer aided detection (CAD) of CTC images can be improved by accurate registration of data from the supine and prone positions. We developed a two-stage approach that first registers the colonic central paths using a heuristic and automated algorithm and then matches polyps or polyp candidates (CAD hits) by a statistical approach. We evaluated the registration algorithm on 24 patient cases. After path registration, the mean misalignment distance between prone and supine identical anatomic landmarks was reduced from 47.08 to 12.66 mm, a 73% improvement. The polyp registration algorithm was specifically evaluated using eight patient cases for which radiologists identified polyps separately for both supine and prone data sets, and then manually registered corresponding pairs. The algorithm correctly matched 78% of these pairs without user input. The algorithm was also applied to the 30 highest-scoring CAD hits in the prone and supine scans and showed a success rate of 50% in automatically registering corresponding polyp pairs. Finally, we computed the average number of CAD hits that need to be manually compared in order to find the correct matches among the top 30 CAD hits. With polyp registration, the average number of comparisons was 1.78 per polyp, as opposed to 4.28 comparisons without polyp registration.

  View details for DOI 10.1118/1.1796171

  View details for Web of Science ID 000224743200025

  View details for PubMedID 15543800

• Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT MEDICAL PHYSICS Bilello, M., Gokturk, S. B., Desser, T., Napel, S., Jeffrey, R. B., Beaulieu, C. F. 2004; 31 (9): 2584-2593

  Abstract

  The objective of this work was to develop and validate algorithms for detection and classification of hypodense hepatic lesions, specifically cysts, hemangiomas, and metastases from CT scans in the portal venous phase of enhancement. Fifty-six CT sections from 51 patients were used as representative of common hypodense liver lesions, including 22 simple cysts, 11 hemangiomas, 22 metastases, and 1 image containing both a cyst and a hemangioma. The detection algorithm uses intensity-based histogram methods to find central lesions, followed by liver contour refinement to identify peripheral lesions. The classification algorithm operates on the focal lesions identified during detection, and includes shape-based segmentation, edge pixel weighting, and lesion texture filtering. Support vector machines are then used to perform a pair-wise lesion classification. For the detection algorithm, 80% lesion sensitivity was achieved at approximately 0.3 false positives (FP) per slice for central lesions, and 0.5 FP per slice for peripheral lesions, giving a total of 0.8 FP per section. For 90% sensitivity, the total number of FP rises to about 2.2 per section. The pair-wise classification yielded good discrimination between cysts and metastases (at 95% sensitivity for detection of metastases, only about 5% of cysts are incorrectly classified as metastases), perfect discrimination between hemangiomas and cysts, and was least accurate in discriminating between hemangiomas and metastases (at 90% sensitivity for detection of hemangiomas, about 28% of metastases were incorrectly classified as hemangiomas). Initial implementations of our algorithms are promising for automating liver lesion detection and classification.

  View details for DOI 10.1118/1.1782674

  View details for PubMedID 15487741

• Surface normal overlap: A computer-aided detection algorithm, with application to colonic polyps and lung nodules in helical CT IEEE TRANSACTIONS ON MEDICAL IMAGING Paik, D. S., Beaulieu, C. F., Rubin, G. D., Acar, B., Jeffrey, R. B., Yee, J., Dey, J., Napel, S. 2004; 23 (6): 661-675

  Abstract

  We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.

  View details for DOI 10.1109/TMI.2004.826362

  View details for Web of Science ID 000221723600001

  View details for PubMedID 15191141

• Computed tomography colonography - Feasibility of computer-aided polyp detection in a "First reader" paradigm 88th Scientific Assembly and Annual Meeting of the Radiological-Society-of-North-America Mani, A., Napel, S., Paik, D. S., Jeffrey, R. B., Yee, J., Olcott, E. W., Prokesch, R., Davila, M., Schraedley-Desmond, P., Beaulieu, C. F. LIPPINCOTT WILLIAMS & WILKINS. 2004: 318–26

  Abstract

  : To determine the feasibility of a computer-aided detection (CAD) algorithm as the "first reader" in computed tomography colonography (CTC).: In phase 1 of a 2-part blind trial, we measured the performance of 3 radiologists reading 41 CTC studies without CAD. In phase 2, readers interpreted the same cases using a CAD list of 30 potential polyps.: Unassisted readers detected, on average, 63% of polyps > or =10 mm in diameter. Using CAD, the sensitivity was 74% (not statistically different). Per-patient analysis showed a trend toward increased sensitivity for polyps > or =10 mm in diameter, from 73% to 90% with CAD (not significant) without decreasing specificity. Computer-aided detection significantly decreased interobserver variability (P = 0.017). Average time to detection of the first polyp decreased significantly with CAD, whereas total reading case reading time was unchanged.: Computer-aided detection as a first reader in CTC was associated with similar per-polyp and per-patient detection sensitivity to unassisted reading. Computer-aided detection decreased interobserver variability and reduced the time required to detect the first polyp.

  View details for Web of Science ID 000221234500003

  View details for PubMedID 15100534

• CT colonography: Does improved z resolution help computer-aided polyp detection? MEDICAL PHYSICS Sundaram, P., Beaulieu, C. F., Paik, D. S., Schraedley-Desmond, P., Napel, S. 2003; 30 (10): 2663-2674

  Abstract

  Multislice helical CT offers several retrospective choices of longitudinal (z) resolution at a given detector collimation setting. We sought to determine the effect of z resolution on the performance of a computer-aided colonic polyp detector, since a human reader and a computer-aided polyp detector may have optimal performances at different z resolutions. We ran a computer-aided polyp detection algorithm on phantom data sets as well as data obtained from a single patient. All data were reconstructed at various slice thicknesses ranging from 1.25 to 10 mm. We studied the performance of the detector at various ranges of polyp sizes using free-response receiver-operating characteristic analyses. We also studied contrast-to-noise ratios (CNR) as a function of slice thickness and polyp size. For the phantom data, reducing the slice thickness from 5 to 1.25 mm improves sensitivity from 84.5% to 98.3% (all polyps), from 61.4% to 95.5% (polyps in the range [0, 5) mm) and from 97.7% to 100% (polyps in the range [5, 10) mm) at a false positive rate of 20 per data set. For polyps larger than 10 mm, there is no significant improvement in detection sensitivity when slice thickness is reduced. CNRs showed expected behavior with slice thickness and polyp size, but in all cases remained high (> 4). The results for the patient data followed similar patterns to that of the phantom case. Thus we conclude that for this detector, the optimal slice thickness is dependent upon the size of the smallest polyps to be detected. For detection of polyps 10 mm and larger, reconstruction of 5 mm sections may be sufficient. Further study is required to generalize these results to a broader population of patients scanned on different scanners.

  View details for DOI 10.1118/1.1599985

  View details for Web of Science ID 000185953700012

  View details for PubMedID 14596303

• Semiautomated segmentation of blood vessels using ellipse-overlap criteria: Method and comparison to manual editing MEDICAL PHYSICS Shiffman, S., Rubin, G. D., Schraedley-Desmond, P., Napel, S. 2003; 30 (10): 2572-2583

  Abstract

  Two-dimensional intensity-based methods for the segmentation of blood vessels from computed-tomography-angiography data often result in spurious segments that originate from other objects whose intensity distributions overlap with those of the vessels. When segmented images include spurious segments, additional methods are required to select segments that belong to the target vessels. We describe a method that allows experts to select vessel segments from sequences of segmented images with little effort. Our method uses ellipse-overlap criteria to differentiate between segments that belong to different objects and are separated in plane but are connected in the through-plane direction. To validate our method, we used it to extract vessel regions from volumes that were segmented via analysis of isolabel-contour maps, and showed that the difference between the results of our method and manually-edited results was within inter-expert variability. Although the total editing duration for our method, which included user-interaction and computer processing, exceeded that of manual editing, the extent of user interaction required for our method was about a fifth of that required for manual editing.

  View details for DOI 10.1118/1.1604731

  View details for Web of Science ID 000185953700002

  View details for PubMedID 14596293

• Curved-slab maximum intensity projection: Method and evaluation 87th Scientific Assembly and Annual Meeting of the Radiological-Society-of-North-America Raman, R., Napel, S., Rubin, G. D. RADIOLOGICAL SOC NORTH AMERICA. 2003: 255–60

  Abstract

  The authors developed and evaluated a method to produce curved-slab maximum intensity projections (MIPs) through blood vessels that semiautomatically excludes soft tissue and bone. Results obtained with the algorithm were compared with those obtained with rectangular-slab MIPs by using computed tomographic (CT) data from four patients with abdominal aortic aneurysms. Curved-slab MIPs exhibited increased mean vessel-to-perivascular tissue contrast of 55.1 HU (36%), allowed a 10% increase in contrast-to-noise ratio, and decreased apparent vessel narrowing by 0.12-1.09 mm, without increasing processing time. Curved-slab MIPs may also include multiple vessels in a single image, thereby improving interpretation efficiency by reducing the number of MIPs required in these patients from eight to three.

  View details for DOI 10.1148/radiol.2291020370

  View details for Web of Science ID 000185424900039

  View details for PubMedID 12944605

• Can low-dose unenhanced multidetector CT be used for routine evaluation of suspected renal colic? AMERICAN JOURNAL OF ROENTGENOLOGY Katz, D. S., Venkataramanan, N., Napel, S., Sommer, F. G. 2003; 180 (2): 313-315

  View details for Web of Science ID 000180753200003

  View details for PubMedID 12540422

• Fast volume segmentation with simultaneous visualization using programmable graphics hardware IEEE Visualization 2003 Conference Sherbondy, A., Houston, M., Napel, S. IEEE. 2003: 171–176

  View details for Web of Science ID 000189041100019

• EPI distortion correction for MR-DTI by using texture memory on graphics hardware 17th International Congress and Exhibition of Computer Assisted Radiology and Surgery Masutani, Y., Yoshikawa, T., Aoki, S., Ohtomo, K., Sherbondy, A., Napel, S. ELSEVIER SCIENCE BV. 2003: 1315–1315

  View details for DOI 10.1016/S0531-5131(03)00286-3

  View details for Web of Science ID 000185617600224

• Edge displacement field-based classification for improved detection of polyps in CT colonography IEEE TRANSACTIONS ON MEDICAL IMAGING Acar, B., Beaulieu, C. F., Gokturk, S. B., Tomasi, C., Paik, D. S., Jeffrey, R. B., Yee, J., Napel, S. 2002; 21 (12): 1461-1467

  Abstract

  Colorectal cancer can easily be prevented provided that the precursors to tumors, small colonic polyps, are detected and removed. Currently, the only definitive examination of the colon is fiber-optic colonoscopy, which is invasive and expensive. Computed tomographic colonography (CTC) is potentially a less costly and less invasive alternative to FOC. It would be desirable to have computer-aided detection (CAD) algorithms to examine the large amount of data CTC provides. Most current CAD algorithms have high false positive rates at the required sensitivity levels. We developed and evaluated a postprocessing algorithm to decrease the false positive rate of such a CAD method without sacrificing sensitivity. Our method attempts to model the way a radiologist recognizes a polyp while scrolling a cross-sectional plane through three-dimensional computed tomography data by classification of the changes in the location of the edges in the two-dimensional plane. We performed a tenfold cross-validation study to assess its performance using sensitivity/specificity analysis on data from 48 patients. The mean specificity over all experiments increased from 0.19 (0.35) to 0.47 (0.56) for a sensitivity of 1.00 (0.95).

  View details for DOI 10.1109/TMI.2002.806405

  View details for Web of Science ID 000180871100003

  View details for PubMedID 12588030

• 3D differential descriptors for improved computer-aided detection (CAD) of colonic polyps in computed tomography colonography (CTC) 88th Scientific Assembly and Annual Meeting of the Radiological-Society-of-North-America Acar, B., Beaulieu, C. F., Paik, D. S., Yee, J., Jeffrey, R. B., Napel, S. S. RADIOLOGICAL SOC NORTH AMERICA. 2002: 405–406

  View details for Web of Science ID 000178825101150

• CT colonography: Improved polyp detection sensitivity and efficiency with computer aided detection (CAD) 88th Scientific Assembly and Annual Meeting of the Radiological-Society-of-North-America Mani, A., Napel, S. S., Paik, D. S., Olcott, E. W., Yee, J., Beaulieu, C. F. RADIOLOGICAL SOC NORTH AMERICA. 2002: 304–304

  View details for Web of Science ID 000178825100757

• Quantitative 3-D diagnostic ultrasound imaging using a modified transducer array and an automated image tracking technique IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL Hossack, J. A., Sumanaweera, T. S., Napel, S., Ha, J. S. 2002; 49 (8): 1029-1038

  Abstract

  An approach for acquiring dimensionally accurate three-dimensional (3-D) ultrasound data from multiple 2-D image planes is presented. This is based on the use of a modified linear-phased array comprising a central imaging array that acquires multiple, essentially parallel, 2-D slices as the transducer is translated over the tissue of interest. Small, perpendicularly oriented, tracking arrays are integrally mounted on each end of the imaging transducer. As the transducer is translated in an elevational direction with respect to the central imaging array, the images obtained by the tracking arrays remain largely coplanar. The motion between successive tracking images is determined using a minimum sum of absolute difference (MSAD) image matching technique with subpixel matching resolution. An initial phantom scanning-based test of a prototype 8 MHz array indicates that linear dimensional accuracy of 4.6% (2 sigma) is achievable. This result compares favorably with those obtained using an assumed average velocity [31.5% (2 sigma) accuracy] and using an approach based on measuring image-to-image decorrelation [8.4% (2 sigma) accuracy]. The prototype array and imaging system were also tested in a clinical environment, and early results suggest that the approach has the potential to enable a low cost, rapid, screening method for detecting carotid artery stenosis. The average time for performing a screening test for carotid stenosis was reduced from an average of 45 minutes using 2-D duplex Doppler to 12 minutes using the new 3-D scanning approach.

  View details for Web of Science ID 000177414700002

  View details for PubMedID 12201450

• Automated generation of curved planar reformations from volume data: Method and evaluation RADIOLOGY Raman, R., Napel, S., Beaulieu, C. F., Bain, E. S., Jeffrey, R. B., Rubin, G. D. 2002; 223 (1): 275-280

  Abstract

  The authors developed and evaluated a method to automatically create interactive vascular curved planar reformations with computed tomographic (CT) angiographic data. The method decreased user interaction time by 86%, from 15 to 2 minutes. Expert reviewers were asked to indicate their confidence in differentiating automatically created images from clinical-quality manually produced images. The area under the receiver operating characteristic curve was 0.45 (95% CI: 0.39, 0.51), and a test of equivalency indicated that reviewers could not distinguish between images. They also graded image quality as equivalent to that with manual methods and found fewer artifacts on automatically created images. Automatic methods rapidly produce curved planar reformations of equivalent quality with reduced time and effort.

  View details for Web of Science ID 000174611900037

  View details for PubMedID 11930078

• Quantification of distention in CT colonography: Development and validation of three computer algorithms RADIOLOGY Hung, P. W., Paik, D. S., Napel, S., Yee, J., Jeffrey, R. B., Steinauer-Gebauer, A., Min, J., Jathavedam, A., Beaulieu, C. F. 2002; 222 (2): 543-554

  Abstract

  Three bowel distention-measuring algorithms for use at computed tomographic (CT) colonography were developed, validated in phantoms, and applied to a human CT colonographic data set. The three algorithms are the cross-sectional area method, the moving spheres method, and the segmental volume method. Each algorithm effectively quantified distention, but accuracy varied between methods. Clinical feasibility was demonstrated. Depending on the desired spatial resolution and accuracy, each algorithm can quantitatively depict colonic diameter in CT colonography.

  View details for Web of Science ID 000173502500035

  View details for PubMedID 11818626

• Carotid disease: Automated analysis with cardiac-gated three-dimensional US - Technique and preliminary results RADIOLOGY Napel, S., Xu, H. B., Paik, D. S., Ross, B. A., Sumanaweera, T. S., Hossack, J. A., Jeffrey, R. B. 2002; 222 (2): 560-563

  Abstract

  Automatic analysis was performed of four-dimensional ultrasonographic (US) data in the carotid artery. The data, which were acquired in 31 subjects (eight healthy volunteers and 23 patients) by using a US scanner fitted with a special probe, were successfully processed. Acquisition time averaged 12 minutes. Data for all healthy volunteers (n = 8) and patients with complete occlusions (n = 3) were correctly classified. Data for two of the 12 patients with mild to severe (but not occlusive) disease were misclassified by one category.

  View details for PubMedID 11818628

• A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography IEEE TRANSACTIONS ON MEDICAL IMAGING Gokturk, S. B., Tomasi, C., Acar, B., Beaulieu, C. F., Paik, D. S., Jeffrey, R. B., Yee, J., Napel, S. 2001; 20 (12): 1251-1260

  Abstract

  Adenomatous polyps in the colon are believed to be the precursor to colorectal carcinoma, the second leading cause of cancer deaths in United States. In this paper, we propose a new method for computer-aided detection of polyps in computed tomography (CT) colonography (virtual colonoscopy), a technique in which polyps are imaged along the wall of the air-inflated, cleansed colon with X-ray CT. Initial work with computer aided detection has shown high sensitivity, but at a cost of too many false positives. We present a statistical approach that uses support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue, and uses this information for the classification of the new cases. One of the main contributions of the paper is the new three-dimensional pattern processing approach, called random orthogonal shape sections method, which combines the information from many random images to generate reliable signatures of shape. The input to the proposed system is a collection of volume data from candidate polyps obtained by a high-sensitivity, low-specificity system that we developed previously. The results of our ten-fold cross-validation experiments show that, on the average, the system increases the specificity from 0.19 (0.35) to 0.69 (0.74) at a sensitivity level of 1.0 (0.95).

  View details for Web of Science ID 000173296700006

  View details for PubMedID 11811825

• Prediction of aortoiliac stent-graft length: Comparison of measurement methods RADIOLOGY Tillich, M., Hill, B. B., Paik, D. S., Petz, K., Napel, S., Zarins, C. K., Rubin, G. D. 2001; 220 (2): 475-483

  Abstract

  To determine the accuracy of helical computed tomography (CT), projectional angiography derived from CT angiography, and intravascular ultrasonographic withdrawal (IUW) length measurements for predicting appropriate aortoiliac stent-graft length.Helical CT data from 33 patients were analyzed before and after endovascular repair of abdominal aortic aneurysm (Aneuryx graft, n = 31; Excluder graft, n = 2). The aortoiliac length of the median luminal centerline (MLC) and the shortest path (SP) that remained at least one common iliac arterial radius away from the vessel wall were calculated. Conventional angiographic measurements were simulated from CT data as the length of the three-dimensional MLC projected onto four standard viewing planes. These predeployment lengths and IUW length, available in 24 patients, were compared with the aortoiliac arterial length after stent-graft deployment.The mean error values of SP, MLC, the maximum projected MLC, and IUW were -2.1 mm +/- 4.6 (SD) (P =.013), 9.8 mm +/- 6.8 (P <.001), -5.2 mm +/- 7.8 (P <.001), and -14.1 mm +/- 9.3 (P <.001), respectively. The preprocedural prediction of the postprocedural aortoiliac length with the SP was significantly more accurate than that with the MLC (P <.001), maximum projected MLC (P <.001), and IUW (P <.001).The shortest aortoiliac path length maintaining at least one radius distance from the vessel wall most accurately enabled stent-graft length prediction for 31 AneuRx and two Excluder stent-grafts.

  View details for Web of Science ID 000169988700029

  View details for PubMedID 11477256

• Medial axis registration of supine and prone CT colonography data 23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Acar, B., Napel, S., Paik, D. S., Li, P., Yee, J., Jeffrey, R. B., Beaulieu, C. F. IEEE. 2001: 2433–2436

  View details for Web of Science ID 000178871900663

• A new 3-D volume processing method for polyp detection 23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Gokturk, S. B., Tomasi, C., Acar, B., Paik, D., Beaulieu, C., Napel, S. IEEE. 2001: 2522–2525

  View details for Web of Science ID 000178871900686

• Assessment of an optical flow field-based polyp detector for CT colonography 23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Acar, B., Beaulieu, C. F., Paik, D. S., Gokturk, S. B., Tomasi, C., Yee, J., Napel, S. IEEE. 2001: 2774–2777

  View details for Web of Science ID 000178871900754

• Medical image segmentation using analysis of isolable-contour maps IEEE TRANSACTIONS ON MEDICAL IMAGING Shiffman, S., Rubin, G. D., Napel, S. 2000; 19 (11): 1064-1074

  Abstract

  A common challenge for automated segmentation techniques is differentiation between images of close objects that have similar intensities, whose boundaries are often blurred due to partial-volume effects. We propose a novel approach to segmentation of two-dimensional images, which addresses this challenge. Our method, which we call intrinsic shape for segmentation (ISeg), analyzes isolabel-contour maps to identify coherent regions that correspond to major objects. ISeg generates an isolabel-contour map for an image by multilevel thresholding with a fine partition of the intensity range. ISeg detects object boundaries by comparing the shape of neighboring isolabel contours from the map. ISeg requires only little effort from users; it does not require construction of shape models of target objects. In a formal validation with computed-tomography angiography data, we showed that ISeg was more robust than conventional thresholding, and that ISeg's results were comparable to results of manual tracing.

  View details for Web of Science ID 000166707300002

  View details for PubMedID 11204844

• Stair-step artifacts with single versus multiple detector-row helical CT 85th Annual Meeting and Scientific Assembly of the Radiological-Society-of-North-America (RSNA) Fleischmann, D., Rubin, G. D., Paik, D. S., Yen, S. Y., Hilfiker, P. R., Beaulieu, C. F., Napel, S. RADIOLOGICAL SOC NORTH AMERICA. 2000: 185–96

  Abstract

  To compare the effects of acquisition parameters on the magnitude and appearance of artifacts between single and multiple detector-row helical computed tomography (CT).A cylindric (12.7 x 305.0-mm) acrylic rod inclined 45 degrees relative to the z axis was scanned at the isocenter and 100 mm from the isocenter with single detector-row (single-channel) helical CT (beam width, 1-10 mm; pitch, 1.0, 2.0, or 3.0) and multiple detector-row (four-channel) helical CT (detector width, 1. 25, 2.5, 3.75, and 5 mm; pitch, 0.75 or 1.5). The SD of radius measurements along the rod (SD(r)) was used to quantify artifacts in all 72 data sets and to analyze their frequency patterns. Volume-rendered images of the data sets were ranked by six independent and blinded readers; findings were correlated with acquisition parameters and SD(r) measurements.SD(r) was smaller in four- than in single-channel helical CT for any given table increment (TI). In single-channel helical CT, SD(r) increased linearly with beam width and geometrically with pitch. In four-channel helical CT, SD(r) measurements were directly proportional to the TI, regardless of the detector width and pitch combination used. Off-center object position on average increased SD(r) by a factor of 1.6 for single-channel helical CT and by a factor of 2.0 for four-channel helical CT. Subjective rankings of image quality correlated excellently with SD(r) (Spearman r = 0.94, P <.001).Artifacts are quantitatively and subjectively smaller with four- compared with single-channel helical CT for any given TI.

  View details for Web of Science ID 000087829500026

  View details for PubMedID 10887247

• Automated polyp detector for CT colonography: Feasibility study RADIOLOGY Summers, R. M., Beaulieu, C. F., Pusanik, L. M., Malley, J. D., Jeffrey, R. B., Glazer, D. I., Napel, S. 2000; 216 (1): 284-290

  Abstract

  An abdominal computed tomographic scan was modified by inserting 10 simulated colonic polyps with use of methods that closely mimic the attenuation, noise, and polyp-colon wall interface of naturally occurring polyps. A shape-based polyp detector successfully located six of the 10 polyps. When settings that enhanced the edge profile of polyps were chosen, eight of 10 polyps were detected. There were no false-positive detections. Shape analysis is technically feasible and is a promising approach to automated polyp detection.

  View details for Web of Science ID 000087829500042

  View details for PubMedID 10887263

• Cost identification of abdominal aortic aneurysm imaging by using time and motion analyses RADIOLOGY Rubin, G. D., Armerding, M. D., Dake, M. D., Napel, S. 2000; 215 (1): 63-70

  Abstract

  To compare the costs of performing helical computed tomographic (CT) angiography with three-dimensional rendering versus intraarterial digital subtraction angiography (DSA) for preoperative imaging of abdominal aortic aneurysms (AAAs).A single observer determined the variable direct costs of performing nine intraarterial DSA and 10 CT angiographic examinations in age- and general health-matched patients with AAA by using time and motion analyses. All personnel directly involved in the cases were tracked, and the involvement times were recorded to the nearest minute. All material items used during the procedures were recorded. The cost of labor was determined from personnel reimbursement data, and the cost of materials, from vendor pricing. The variable direct costs of laboratory tests and using the ambulatory treatment unit for postprocedural monitoring, as well as all fixed direct costs, were assessed from hospital accounting records. The total costs were determined for each procedure and compared by using the Student t test and calculating the CIs.The mean total direct cost of intraarterial DSA (+/- SD) was $1,052 +/- 71, and that of CT angiography was $300 +/- 30, which are significantly different (P < 4.1 x 10(-11)). With 95% confidence, intraarterial DSA cost 3.2-3.7 times more than CT angiography for the assessment of AAA.Assuming equal diagnostic utility and procedure-related morbidity, institutions may have substantial cost savings whenever CT angiography can replace intraarterial DSA for imaging AAAs.

  View details for Web of Science ID 000086156700011

  View details for PubMedID 10751469

• Visualization modes for CT colonography using cylindrical and planar map projections JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Paik, D. S., Beaulieu, C. F., Jeffrey, R. B., Karadi, C. A., Napel, S. 2000; 24 (2): 179-188

  Abstract

  The purpose of this study was to demonstrate the limitations to the effectiveness of CT colonography, colloquially called virtual colonoscopy (VC), for detecting polyps in the colon and to describe a new technique, map projection CT colonography using Mercator projection and stereographic projection, that overcomes these limitations.In one experiment, data sets from nine patients undergoing CT colonography were analyzed to determine the percentage of the mucosal surface visible in various visualization modes as a function of field of view (FOV). In another experiment, 40 digitally synthesized polyps of various sizes (10, 7, 5, and 3.5 mm) were randomly inserted into four copies of one patient data set. Both Mercator and stereographic projections were used to visualize the surface of the colon of each data set. The sensitivity and positive predictive value (PPV) were calculated and compared with the results of an earlier study of visualization modes using the same CT colonography data.The percentage of mucosal surface visualized by VC increases with greater FOV but only approaches that of map projection VC (98.8%) at a distorting, very high FOV. For both readers and polyp sizes of > or =7 mm, sensitivity for Mercator projection (87.5%) and stereographic projection (82.5%) was significantly greater (p < 0.05) than for viewing axial slices (62.5%), and Mercator projection was significantly more sensitive than VC (67.5%). Mercator and stereographic projection had PPVs of 75.4 and 78.9%, respectively.The sensitivity of conventional CT colonography is limited by the percentage of the mucosal surface seen. Map projection CT colonography overcomes this problem and provides a more sensitive method with a high PPV for detecting polyps than other methods currently being investigated.

  View details for Web of Science ID 000086026800001

  View details for PubMedID 10752876

• Reconstruction algorithm for polychromatic CT imaging: Application to beam hardening correction IEEE TRANSACTIONS ON MEDICAL IMAGING Yan, C. H., Whalen, R. T., Beaupre, G. S., Yen, S. Y., Napel, S. 2000; 19 (1): 1-11

  Abstract

  This paper presents a new reconstruction algorithm for both single- and dual-energy computed tomography (CT) imaging. By incorporating the polychromatic characteristics of the X-ray beam into the reconstruction process, the algorithm is capable of eliminating beam hardening artifacts. The single energy version of the algorithm assumes that each voxel in the scan field can be expressed as a mixture of two known substances, for example, a mixture of trabecular bone and marrow, or a mixture of fat and flesh. These assumptions are easily satisfied in a quantitative computed tomography (QCT) setting. We have compared our algorithm to three commonly used single-energy correction techniques. Experimental results show that our algorithm is much more robust and accurate. We have also shown that QCT measurements obtained using our algorithm are five times more accurate than that from current QCT systems (using calibration). The dual-energy mode does not require any prior knowledge of the object in the scan field, and can be used to estimate the attenuation coefficient function of unknown materials. We have tested the dual-energy setup to obtain an accurate estimate for the attenuation coefficient function of K2 HPO4 solution.

  View details for Web of Science ID 000086495700001

  View details for PubMedID 10782614

• Automated quantification of 4D ultrasound for carotid artery disease 14th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2000) Xu, H., Paik, D. S., Ross, B., Sumanaweera, T. S., Hossack, J., Jeffrey, R. B., Napel, S. ELSEVIER SCIENCE BV. 2000: 666–670

  View details for Web of Science ID 000165685600113

• Quantitative 3D ultrasound imaging using an automated image tracking technique IEEE Ultrasonics Symposium Hossack, J. A., Sumanaweera, T. S., Napel, S. IEEE. 2000: 1593–1596

  View details for Web of Science ID 000171881300350

• Automatic selection of blood-vessel regions from preprocessed image sequences: Method and evaluation 14th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2000) Shiffman, S., Napel, S. ELSEVIER SCIENCE BV. 2000: 1018–1018

  View details for Web of Science ID 000165685600214

• Spatially varying longitudinal aliasing and resolution in spiral computed tomography MEDICAL PHYSICS Yen, S. Y., Rubin, G. D., Napel, S. 1999; 26 (12): 2617-2625

  Abstract

  Spiral computed tomography (CT) has revolutionized conventional CT as a truly three-dimensional imaging modality. A number of studies aimed at evaluating the longitudinal resolution in spiral CT have been presented, but the spatially varying nature of the longitudinal resolution in spiral CT has been largely left undiscussed. In this paper, we investigate the longitudinal resolution in spiral CT as affected by the spatially varying longitudinal aliasing. We propose the treatment of aliasing as a signal dependent, additive noise, and define a new image quality parameter, the contrast-to-aliased-noise ratio (CNaR), that relates to possible image degradation or loss of resolution caused by aliasing. We performed CT simulations and actual phantom scans using a resolution phantom consisting of sequences of spherical beads of different diameters, extending along the longitudinal axis. Our results show that the off-isocenter longitudinal resolution differs significantly from the longitudinal resolution at the isocenter and that the CNaR decreases with distance from the isocenter, and is a function of pitch and the helical interpolation algorithm used. The longitudinal resolution was observed to worsen with decreasing CNaR. We conclude that the longitudinal resolution in spiral CT is spatially varying, and can be characterized by the CNaR measured at the transaxial location of interest.

  View details for Web of Science ID 000084359200016

  View details for PubMedID 10619247

• Computed tomographic angiography: Historical perspective and new state-of-the-art using multi detector-row helical computed tomography Workshop on Multiple Perspectives in Magnetic Resonance Imaging Contrast Rubin, G. D., Shiau, M. C., Schmidt, A. J., Fleischmann, D., Logan, L., Leung, A. N., Jeffrey, R. B., Napel, S. LIPPINCOTT WILLIAMS & WILKINS. 1999: S83–S90

  Abstract

  Since its clinical introduction in 1991, volumetric computed tomography scanning using spiral or helical scanners has resulted in a revolution for diagnostic imaging. In addition to new applications for computed tomography, such as computed tomographic angiography and the assessment of patients with renal colic, many routine applications such as the detection of lung and liver lesions have substantially improved. Helical computed tomographic technology has improved over the past eight years with faster gantry rotation, more powerful X-ray tubes, and improved interpolation algorithms, but the greatest advance has been the recent introduction of multi detector-row computed tomography scanners. These scanners provide similar scan quality at a speed gain of 3-6 times greater than single detector-row computed tomography scanners. This has a profound impact on the performance of computed tomography angiography, resulting in greater anatomic coverage, lower iodinated contrast doses, and higher spatial resolution scans than single detector-row systems.

  View details for Web of Science ID 000084391500012

  View details for PubMedID 10608402

• Display modes for CT colonography - Part II. Blinded comparison of axial CT and virtual endoscopic and panoramic endoscopic volume-rendered studies RADIOLOGY Beaulieu, C. F., Jeffrey, R. B., Karadi, C., Paik, D. S., Napel, S. 1999; 212 (1): 203-212

  Abstract

  To determine the sensitivity of radiologist observers for detecting colonic polyps by using three different data review (display) modes for computed tomographic (CT) colonography, or "virtual colonoscopy."CT colonographic data in a patient with a normal colon were used as base data for insertion of digitally synthesized polyps. Forty such polyps (3.5, 5, 7, and 10 mm in diameter) were randomly inserted in four copies of the base data. Axial CT studies, volume-rendered virtual endoscopic movies, and studies from a three-dimensional mode termed "panoramic endoscopy" were reviewed blindly and independently by two radiologists.Detection improved with increasing polyp size. Trends in sensitivity were dependent on whether all inserted lesions or only visible lesions were considered, because modes differed in how completely the colonic surface was depicted. For both reviewers and all polyps 7 mm or larger, panoramic endoscopy resulted in significantly greater sensitivity (90%) than did virtual endoscopy (68%, P = .014). For visible lesions only, the sensitivities were 85%, 81%, and 60% for one reader and 65%, 62%, and 28% for the other for virtual endoscopy, panoramic endoscopy, and axial CT, respectively. Three-dimensional displays were more sensitive than two-dimensional displays (P < .05).The sensitivity of panoramic endoscopy is higher than that of virtual endoscopy, because the former displays more of the colonic surface. Higher sensitivities for three-dimensional displays may justify the additional computation and review time.

  View details for Web of Science ID 000081086900032

  View details for PubMedID 10405743

• Display modes for CT colonography - Part I. Synthesis and insertion of polyps into patient CT data RADIOLOGY Karadi, C., Beaulieu, C. F., Jeffrey, R. B., Paik, D. S., Napel, S. 1999; 212 (1): 195-201

  Abstract

  To develop and validate a method for the insertion of digitally synthesized polyps into computed tomographic (CT) images of the human colon for use as ground truth for evaluation of virtual colonoscopy.Spiral CT simulator software was used to generate 10 synthetic polyps in various configurations. Additional software was developed to insert these polyps into volume CT scans. Ten polyps in eight patients were selected for comparison. Three radiologists evaluated whether two-dimensional (2D) CT images and three-dimensional (3D) volume-rendered CT images showed synthetic or real polyps.Edge-response profiles and noise of simulated polyps matched those of native polyps. Frequency distributions of reviewers' responses were not significantly different for synthetic versus real polyps in either 3D or 2D images. Responses were clustered around the response of "unsure" if lesions were real or synthetic. Receiver operating characteristic curves had areas of 0.54 (95% CI = 0.39, 0.68) for 3D and 0.39 (95% CI = 0.25, 0.53) for 2D images, which were not significantly different from random guessing (P = .70 and .28 for 3D and 2D images, respectively).Synthetic polyps were indistinguishable from real polyps. This method can be used to generate ground truth experimental data for comparison of CT colonographic display and detection methods.

  View details for Web of Science ID 000081086900031

  View details for PubMedID 10405742

• Fast 3D cardiac cine MR imaging JOURNAL OF MAGNETIC RESONANCE IMAGING Alley, M. T., Napel, S., Amano, Y., Paik, D. S., Shifrin, R. Y., Shimakawa, A., Pelc, N. J., Herfkens, R. J. 1999; 9 (5): 751-755

  Abstract

  We describe a technique for three-dimensional cine MR imaging. By using short repetition times (TR) and interleaved slice encoding, volumetric cine data can be acquired throughout the cardiac cycle with a temporal resolution of approximately 80 msec. A T1-shortening agent is used to produce contrast between blood and myocardium. A comparison between the acquisition times of this and several other two-dimensional techniques is presented.

  View details for Web of Science ID 000083418000021

  View details for PubMedID 10331775

• Virtual endoscopy using perspective volume-rendered three-dimensional sonographic Data: Technique and clinical applications AMERICAN JOURNAL OF ROENTGENOLOGY Yuh, E. L., Jeffrey, R. B., Birdwell, R. L., Chen, B. H., Napel, S. 1999; 172 (5): 1193-1197

  Abstract

  We present a technique for obtaining three-dimensional external and virtual endoscopy views of organs using perspective volume-rendered gray-scale and Doppler sonographic data, and we explore potential clinical applications in the carotid artery, the female pelvis, and the bladder.Using the proposed methods, radiologists will find it possible to create virtual endoscopy and external perspective views using sonographic data. The technique works well for revealing the interior of fluid-filled structures and cavities. However, expected improvements in computer performance and integration with existing sonographic equipment will be necessary for the technique to become practical in the clinical environment.

  View details for Web of Science ID 000079919700005

  View details for PubMedID 10227488

• Modeling of polychromatic attenuation using computed tomography reconstructed images MEDICAL PHYSICS Yan, C. H., Whalen, R. T., Beaupre, G. S., Yen, S. Y., Napel, S. 1999; 26 (4): 631-642

  Abstract

  This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.

  View details for Web of Science ID 000079769500019

  View details for PubMedID 10227366

• Longitudinal sampling and aliasing in spiral CT IEEE TRANSACTIONS ON MEDICAL IMAGING Yen, S. Y., Yan, C. H., Rubin, G. D., Napel, S. 1999; 18 (1): 43-58

  Abstract

  Although analyses of in-plane aliasing have been done for conventional computed tomography (CT) images, longitudinal aliasing in spiral CT has not been properly investigated. We propose a mathematical model of the three-dimensional (3-D) sampling scheme in spiral CT and analyze its effects on longitudinal aliasing. We investigated longitudinal aliasing as a function of the helical-interpolation algorithm, pitch, and reconstruction interval using CT simulations and actual phantom scans. Our model predicts, and we verified, that for a radially uniform object at the isocenter, the spiral sampling scheme results in spatially varying cancellation of the aliased spectral islands which, in turn, results in spatially varying longitudinal aliasing. The aliasing is minimal at the scanner isocenter, but worsens with distance from it and rapidly becomes significant. Our results agree with published results observed at the isocenter of the scanner and further provide new insight into the aliasing conditions at off-isocenter locations with respect to the pitch, interpolation algorithm, and reconstruction interval. We conclude that longitudinal aliasing at off-isocenter locations can be significant, and that its magnitude and effects cannot be predicted by measurements made only at the scanner isocenter.

  View details for Web of Science ID 000079154100004

  View details for PubMedID 10193696

• New visualization techniques for virtual colonoscopy: Methods and evaluation 1st International Workshop on Computer-Aided Diagnosis Napel, S., Beaulieu, C. F., Paik, D. S., Karadi, C., Jeffrey, R. B. ELSEVIER SCIENCE BV. 1999: 463–468

  View details for Web of Science ID 000084227800068

• Detection of colonic polyps in a phantom model: Implications for virtual colonoscopy data acquisition JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Beaulieu, C. F., Napel, S., Daniel, B. L., Ch'en, I. Y., Rubin, G. D., Johnstone, I. M., Jeffrey, R. B. 1998; 22 (4): 656-663

  Abstract

  Virtual colonoscopy is a new method of colon examination in which computer-aided 3D visualization of spiral CT simulates fiberoptic colonoscopy. We used a colon phantom containing various-sized spheres to determine the influence of CT acquisition parameters on lesion detectability and sizing.Spherical plastic beads with diameters of 2.5, 4, 6, 8 and 10 mm were randomly attached to the inner wall of segments of plastic tubing. Groups of three sealed tubes were scanned at 3/1, 3/2, 5/1 collimation (mm)/pitch settings in orientations perpendicular and parallel to the scanner gantry. For each acquisition, image sets were reconstructed at intervals from 0.5 to 5.0 mm. Two blinded reviewers assessed transverse cross-sections of the phantoms for bead detection, using source CT images for images for acquisitions obtained with the tubes oriented perpendicular to the gantry and using orthogonal reformatted images for scans oriented parallel to the gantry.Detection of beads of > or = 4 mm was 100% for both tube orientations and for all collimator/pitch settings and reconstruction intervals. For the 2.5 mm beads, detection decreased to 78-94% for 5 mm collimation/pitch 2 scans when the phantom sections were oriented parallel to the gantry (p = 0.01). Apparent elongation of beads in the slice direction occurred as the collimation and pitch increased. The majority of the elongation (approximately 75%) was attributable to changing the collimator from 3 to 5 mm, with the remainder of the elongation due to doubling the pitch from 1 to 2.CT scanning at 5 mm collimation and up to pitch 2 is adequate for detection of high contrast lesions as small as 4 mm in this model. However, lesion size and geometry are less accurately depicted than at narrower collimation and lower pitch settings.

  View details for Web of Science ID 000074812400028

  View details for PubMedID 9676463

• Automated flight path planning for virtual endoscopy MEDICAL PHYSICS Paik, D. S., Beaulieu, C. F., Jeffrey, R. B., Rubin, G. D., Napel, S. 1998; 25 (5): 629-637

  Abstract

  In this paper, a novel technique for rapid and automatic computation of flight paths for guiding virtual endoscopic exploration of three-dimensional medical images is described. While manually planning flight paths is a tedious and time consuming task, our algorithm is automated and fast. Our method for positioning the virtual camera is based on the medial axis transform but is much more computationally efficient. By iteratively correcting a path toward the medial axis, the necessity of evaluating simple point criteria during morphological thinning is eliminated. The virtual camera is also oriented in a stable viewing direction, avoiding sudden twists and turns. We tested our algorithm on volumetric data sets of eight colons, one aorta and one bronchial tree. The algorithm computed the flight paths in several minutes per volume on an inexpensive workstation with minimal computation time added for multiple paths through branching structures (10%-13% per extra path). The results of our algorithm are smooth, centralized paths that aid in the task of navigation in virtual endoscopic exploration of three-dimensional medical images.

  View details for Web of Science ID 000073650800004

  View details for PubMedID 9608471

• Measurement of the aorta and its branches with helical CT RADIOLOGY Rubin, G. D., Paik, D. S., Johnston, P. C., Napel, S. 1998; 206 (3): 823-829

  Abstract

  Contiguous orthonormal arterial cross sections, segment lengths, and curvature were semiautomatically quantified from helical computed tomographic (CT) angiographic data in phantoms and two patients. Measurements of mean diameter and curvature correlated with reference values (r2 = .99), and mean fractional errors were 0.07 and 0.06 for mean diameter and curvature measurements, respectively. Volumetric measurement showed a potential to increase the accuracy, precision, and diagnostic utility of CT angiography.

  View details for Web of Science ID 000072128000042

  View details for PubMedID 9494508

• A new frame-based registration algorithm MEDICAL PHYSICS Yan, C. H., Whalen, R. T., Beaupre, G. S., Sumanaweera, T. S., Yen, S. Y., Napel, S. 1998; 25 (1): 121-128

  Abstract

  This paper presents a new algorithm for frame registration. Our algorithm requires only that the frame be comprised of straight rods, as opposed to the N structures or an accurate frame model required by existing algorithms. The algorithm utilizes the full 3D information in the frame as well as a least squares weighting scheme to achieve highly accurate registration. We use simulated CT data to assess the accuracy of our algorithm. We compare the performance of the proposed algorithm to two commonly used algorithms. Simulation results show that the proposed algorithm is comparable to the best existing techniques with knowledge of the exact mathematical frame model. For CT data corrupted with an unknown in-plane rotation or translation, the proposed technique is also comparable to the best existing techniques. However, in situations where there is a discrepancy of more than 2 mm (0.7% of the frame dimension) between the frame and the mathematical model, the proposed technique is significantly better (p < or = 0.05) than the existing techniques. The proposed algorithm can be applied to any existing frame without modification. It provides better registration accuracy and is robust against model mis-match. It allows greater flexibility on the frame structure. Lastly, it reduces the frame construction cost as adherence to a concise model is not required.

  View details for Web of Science ID 000071661600015

  View details for PubMedID 9472834

• Accuracy of detection and measurement of renal calculi: In vitro comparison of three-dimensional spiral CT, radiography, and nephrotomography RADIOLOGY Olcott, E. W., Sommer, F. G., Napel, S. 1997; 204 (1): 19-25

  Abstract

  To compare accuracy of three-dimensional (3D) spiral computed tomography (CT) performed without administration of contrast material with that of radiography and linear nephrotomography in detection and measurement of renal calculi.Fifty renal calculi within an abdominal phantom were imaged with 3D spiral CT, radiography, and linear nephrotomography. Spiral CT data were analyzed with workstation-based 3D imaging software, with a thresholding procedure based on the maximally attenuating voxel within each calculus during measurement. Measurement accuracy and detection rates were compared according to modality. Conventional and magnification-corrected measurements from radiography and linear nephrotomography were included.Spiral CT depicted calculi and allowed determination of the collective two-dimensional and 3D linear measurements statistically significantly more accurately than the other techniques; the mean linear measurement errors along individual axes did not exceed 3.6%. With 3D spiral CT, calculus volumes were determined with a mean error of -4.8%.3D spiral CT enabled highly accurate determination of the volumes and all three linear dimensions of renal calculi. In addition, 3D spiral CT depicted calculi more sensitively than traditional techniques and provided new information and improved accuracy in the evaluation of nephrolithiasis.

  View details for Web of Science ID A1997XF19400008

  View details for PubMedID 9205217

• Comparison and evaluation of retrospective intermodality brain image registration techniques Medical Imaging 1996 Meeting West, J., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer, C. R., Kessler, R. M., MACIUNAS, R. J., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., VANDENELSEN, P. A., Napel, S., Sumanaweera, T. S., Harkness, B., Hemler, P. F., Hill, D. L., Hawkes, D. J., Studholme, C., Maintz, J. B., Viergever, M. A., Malandain, G., Pennec, X., Noz, M. E., Maguire, G. Q., Pollack, M., Pelizzari, C. A., Robb, R. A., Hanson, D., Woods, R. P. LIPPINCOTT WILLIAMS & WILKINS. 1997: 554–66

  Abstract

  The primary objective of this study is to perform a blinded evaluation of a group of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method. To ensure blindedness, all retrospective registrations were performed by participants who had no knowledge of the gold standard results until after their results had been submitted. A secondary goal of the project is to evaluate the importance of correcting geometrical distortion in MR images by comparing the retrospective registration error in the rectified images, i.e., those that have had the distortion correction applied, with that of the same images before rectification.Image volumes of three modalities (CT, MR, and PET) were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/ or from PET to MR. These investigators communicated their transformations again via the Internet to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple volumes of interest (VOIs), i.e., areas in the brain that would commonly be areas of neurological interest. A VOI is defined in the MR image and its centroid c is determined. Then, the prospective registration is used to obtain the corresponding point c' in CT or PET. To this point, the retrospective registration is then applied, producing c" in MR. Statistics are gathered on the target registration error (TRE), which is the distance between the original point c and its corresponding point c".This article presents statistics on the TRE calculated for each registration technique in this study and provides a brief description of each technique and an estimate of both preparation and execution time needed to perform the registration.Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

  View details for Web of Science ID A1997XH71300007

  View details for PubMedID 9216759

• Helical CT angiography of renal artery stenosis AMERICAN JOURNAL OF ROENTGENOLOGY Rubin, G. D., Napel, S. 1997; 168 (4): 1109-1110

  View details for Web of Science ID A1997WQ21100051

  View details for PubMedID 9124125

• Virtual endoscopy of the paranasal sinuses using perspective volume rendered helical sinus computed tomography Meeting of the Western Section of the American-Laryngological-Rhinological-and-Otological-Society Gilani, S., Norbash, A. M., Ringl, H., Rubin, G. D., Napel, S., Terris, D. J. LIPPINCOTT-RAVEN PUBL. 1997: 25–29

  Abstract

  Our goal was to use three-dimensional information obtained from helical computed tomographic (CT) data to explore and evaluate the nasal cavity, nasopharynx, and paranasal sinuses by simulated virtual endoscopy (VE). This was done by utilizing a new image reconstruction method known as perspective volume rendering (PVR). Thin-section helical CT of the nasal cavity, nasopharynx, and paranasal sinuses was performed on a conventional CT scanner. The data were transferred to a workstation to create views similar to those seen with endoscopy. Additional views not normally accessible by conventional endoscopy were generated. Key perspectives were selected, and a video "flight" model was choreographed and synthesized through the nasal cavity and sinuses based on the CT data. VE allows evaluation of the nasal cavity, nasopharynx, and paranasal sinuses with appreciation of the relationships of these spatially complex structures. In addition, this technique allows structural visualization with unconventional angles, perspectives, and locations not conventionally accessible. Although biopsies, cultures, and lavages routinely done with endoscopy cannot be performed with VE, this technique holds promise for improving the diagnostic evaluation of the nasal cavity, the nasopharynx, and the paranasal sinuses. The unconventional visual perspectives and very low morbidity may complement many applications of simple diagnostic endoscopy.

  View details for Web of Science ID A1997WD68900008

  View details for PubMedID 9001261

• MRI of pulmonary embolism using Gd-DTPA-polyethylene glycol polymer enhanced 3D fast gradient echo technique in a canine model MAGNETIC RESONANCE IMAGING Li, K. C., Pelc, L. R., NAPEL, S. A., Goris, M. L., Lin, D. T., SONG, C. K., Leung, A. N., Rubin, G. D., HOLLETT, M. D., Harris, D. P. 1997; 15 (5): 543-550

  Abstract

  This study was to evaluate the accuracy of MR angiography (MRA) using a Gd-DTPA-polyethylene glycol polymer (Gd-DTPA-PEG) with a 3D fast gradient echo (3D fgre) technique in diagnosing pulmonary embolism in a canine model. Pulmonary emboli were created in six mongrel dogs (20-30 kg) by injecting tantalum oxide-doped autologous blood clots into the femoral veins via cutdowns. MRI was performed with a 1.5 T GE Signa imager using a 3D fgre sequence (11.9/2.3/15 degrees) following intravenous injection of 0.06 mmol Gd/kg of Gd-DTPA-PEG. The dogs were euthanized and spiral CT of the lungs were then obtained on the deceased dogs. The MRI images were reviewed independently and receiver-operating-characteristic (ROC) curves were used for statistical analysis using spiral CT results as the gold standard. The pulmonary emboli were well visualized on spiral CT. Out of 108 pulmonary segments in the six dogs, 24 contained emboli >2 mm and 27 contained emboli < or = 2 mm. With unblinded review, MRI detected 79% of emboli >2 mm and only 48% of emboli < or = 2 mm. The blinded review results were significantly worse. Gd-DTPA-PEG enhanced 3D fgre MRI is potentially able to demonstrate pulmonary embolism with fairly high degree of accuracy, but specialized training for the interpretations will be required.

  View details for Web of Science ID A1997XM76800004

  View details for PubMedID 9253998

• Volumetric, analysis of volumetric data: Achieving a paradigm shift RADIOLOGY Rubin, G. D., Napel, S., Leung, A. N. 1996; 200 (2): 312-317

  View details for Web of Science ID A1996UY07800003

  View details for PubMedID 8685316

• Perspective volume rendering of CT and MR images: Applications for endoscopic imaging RADIOLOGY Rubin, G. D., Beaulieu, C. F., Argiro, V., Ringl, H., Norbash, A. M., Feller, J. F., Dake, M. D., Jeffrey, R. B., Napel, S. 1996; 199 (2): 321-330

  Abstract

  To use perspective volume rendering (PVR) of computed tomographic (CT) and magnetic resonance (MR) imaging data sets to simulate endoscopic views of human organ systems.Perspective views of helical CT and MR images were reconstructed from the data, and tissues were classified by assigning color and opacity based on their CT attenuation or MR signal intensity. "Flight paths" were constructed through anatomic regions by defining key views along a spline path. Twelve movies of the thoracic aorta (n=3), tracheobronchial tree (n=4), colon (n=3), paranasal sinuses (n=1), and shoulder joint (n=1) were generated to display images along the flight path. All abnormal results were confirmed at surgery.PVR fly-through enabled evaluation of the full range of tissue densities, signal intensities, and their three-dimensional spatial relationships.PVR is a novel way to present volumetric data and may enable noninvasive diagnostic endoscopy and provide an alternate method to analyze volumetric imaging data for primary diagnosis.

  View details for Web of Science ID A1996UG01100006

  View details for PubMedID 8668772

• Semiautomated editing of computed tomography sections for visualization of vasculature Conference on Image Display Shiffman, S., Rubin, G. B., Napel, S. SPIE - INT SOC OPTICAL ENGINEERING. 1996: 140–151

  View details for Web of Science ID A1996BF60W00014

• Optimizing the choice of an image interpolating function Conference on Image Processing - Medical Imaging 1996 Yan, C. H., Napel, S. SPIE - INT SOC OPTICAL ENGINEERING. 1996: 376–389

  View details for Web of Science ID A1996BF76L00037

• Surface rendering versus volume rendering in medical imaging: Techniques and applications 7th Annual IEEE Conference on Visualization (Visualization 96) Shahidi, R., Lorensen, B., Kikinis, R., Flynn, J., Kaufman, A., Napel, S. IEEE COMPUTER SOC. 1996: 439–440

  View details for Web of Science ID A1996BG87S00068

• Quantified registration error versus the accuracy of registered surfaces for a multimodality surface-based registration system Conference on Image Processing - Medical Imaging 1996 Hemler, P. F., Sumanaweera, T. S., VANDENELSEN, P. A., Napel, S., Adler, J. R. SPIE - INT SOC OPTICAL ENGINEERING. 1996: 348–357

  View details for Web of Science ID A1996BF76L00034

• Fast spill echo image distortion correction for MR-guided stereotactic pallidotomy 1996 Annual Meeting on the Physics of Medical Imaging Sumanaweera, T. S., Lim, K. O., Glover, G. H., Hemler, P. F., Heit, G., VANDENELSEN, P. A., Silverberg, G., Napel, S. SPIE - INT SOC OPTICAL ENGINEERING. 1996: 718–726

  View details for Web of Science ID A1996BF52P00066

• Fast sliding thin slab volume visualization 1996 Symposium on Volume Visualization Yen, S. Y., Napel, S., Rubin, G. D. ASSOC COMPUTING MACHINERY. 1996: 79–86

  View details for Web of Science ID A1996BG69J00010

• Perspective volume rendering of cross-sectional images for simulated endoscopy and intra-parenchymal viewing Conference on Image Display Napel, S., Rubin, G. D., Beaulieu, C. F., Jeffrey, R. B., Argiro, V. SPIE - INT SOC OPTICAL ENGINEERING. 1996: 75–86

  View details for Web of Science ID A1996BF60W00008

• Assessment of several virtual endoscopy techniques using computed tomography and perspective volume rendering 4th International Conference on Visualization in Biomedical Computing (VBC 96) Shahidi, R., Argiro, V., Napel, S., Gray, L., McAdams, H. P., Rubin, G. D., Beaulieu, C. F., Jeffrey, R. B., Johnson, A. SPRINGER-VERLAG BERLIN. 1996: 521–528

  View details for Web of Science ID A1996BH80E00064

• Comparison and evaluation of retrospective intermodality image registration techniques Conference on Image Processing - Medical Imaging 1996 West, J., Fitzpatrick, J. M., Wang, M. Y., Dawant, B. M., Maurer, C. R., Kessler, R. M., MACIUNAS, R. J., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., VANDENELSEN, P. A., Hemler, P. F., Napel, S., Sumanaweera, T. S., Harkness, B., Hill, D. L., Studholme, C., Malandain, G., Pennec, X., Noz, M. E., Maguire, G. Q., Pollack, M., Pelizzari, C. A., Robb, R. A., Hanson, D., Woods, R. P. SPIE - INT SOC OPTICAL ENGINEERING. 1996: 332–347

  View details for Web of Science ID A1996BF76L00033

• INCREASED SCAN PITCH FOR VASCULAR AND THORACIC SPIRAL CT RADIOLOGY Rubin, G. D., Napel, S. 1995; 197 (1): 316-317

  View details for Web of Science ID A1995RV89900059

  View details for PubMedID 7568848

• DETECTION OF URETERAL CALCULI IN PATIENTS WITH SUSPECTED RENAL COLIC - VALUE OF REFORMATTED NONCONTRAST HELICAL CT AMERICAN JOURNAL OF ROENTGENOLOGY Sommer, F. G., Jeffrey, R. B., Rubin, G. D., Napel, S., RIMMER, S. A., Benford, J., Harter, P. M. 1995; 165 (3): 509-513

  Abstract

  The purpose of this study was to determine the value of reformatted noncontrast helical CT in patients with suspected renal colic. We hoped to determine whether this technique might create images acceptable to both radiologists and clinicians and replace our current protocol of sonography and abdominal plain film.Thirty-four consecutive patients with signs and symptoms of renal colic were imaged with both noncontrast helical CT and a combination of plain film of the abdomen and renal sonography. Reformatting of the helical CT data was performed on a workstation to create a variety of reformatted displays. The correlative studies were interpreted by separate blinded observers. Clinical data, including the presence of hematuria and the documentation of stone passage or removal, were recorded.Findings on 18 CT examinations were interpreted as positive for the presence of ureteral calculi; 16 of these cases were determined to be true positives on the basis of later-documented passage of a calculus. Thirteen of the 16 cases proved to be positive were interpreted as positive for renal calculi using the combination of abdominal plain film and renal sonography. The most useful CT reformatting technique was curved planar reformatting of the ureters to determine whether a ureteral calculus was present.In this study, noncontrast helical CT was a rapid and accurate method for determining the presence of ureteral calculi causing renal colic. The reformatted views produced images similar in appearance to excretory urograms, aiding greatly in communicating with clinicians. Limitations on the technique include the time and equipment necessary for reformatting and the suboptimal quality of reformatted images when little retroperitoneal fat is present.

  View details for Web of Science ID A1995RQ00600003

  View details for PubMedID 7645461

• REGISTRATION ERROR QUANTIFICATION OF A SURFACE-BASED MULTIMODALITY IMAGE FUSION SYSTEM MEDICAL PHYSICS Hemler, P. F., Napel, S., Sumanaweera, T. S., PICHUMANI, R., VANDENELSEN, P. A., Martin, D., Drace, J., Adler, J. R., Perkash, I. 1995; 22 (7): 1049-1056

  Abstract

  This paper presents a new reference data set and associated quantification methodology to assess the accuracy of registration of computerized tomography (CT) and magnetic-resonance (MR) images. Also described is a new semiautomatic surface-based system for registering and visualizing CT and MR images. The registration error of the system was determined using a reference data set that was obtained from a cadaver in which rigid fiducial tubes were inserted prior to imaging. Registration error was measured as the distance between an analytic expression for each fiducial tube in one image set and transformed samples of the corresponding tube obtained from the other. Registration was accomplished by first identifying surfaces of similar anatomic structures in each image set. A transformation that best registered these structures was determined using a nonlinear optimization procedure. Even though the root-mean-square (rms) distance at the registered surfaces was similar to that reported by other groups, it was found that rms distances for the tubes were significantly larger than the final rms distances between the registered surfaces. It was also found that minimizing rms distance at the surface did not minimize rms distance for the tubes.

  View details for Web of Science ID A1995RK26900003

  View details for PubMedID 7565379

• MR GEOMETRIC DISTORTION CORRECTION FOR IMPROVED FRAME-BASED STEREOTAXIC TARGET LOCALIZATION ACCURACY MAGNETIC RESONANCE IN MEDICINE Sumanaweera, T. S., Glover, G. H., Hemler, P. F., VANDENELSEN, P. A., Martin, D., Adler, J. R., Napel, S. 1995; 34 (1): 106-113

  Abstract

  We present a method to correct the geometric distortion caused by field inhomogeneity in MR images of patients wearing MR-compatible stereotaxic frames. Our previously published distortion correction method derives patient-dependent error maps by computing the phase-difference of 3D images acquired at different TEs. The time difference (delta TE = 4.9 ms at 1.5 T) is chosen such that the water and fat signals are in phase. However, delta TE is long enough to permit phase wraps in the difference images for frequency offsets greater than 205 Hz. Phase unwrapping techniques resolve these only for connected structures; therefore, the phase difference for fiducial rods may be off by multiples of 2 pi relative to the head. We remove this uncertainty by using an additional single 2D phase-different image with delta TE = 1 ms (during which time no phase-wraps are typically expected) to determine the correct multiple of 2 pi for each rod. We tested our method in a cadaver and in a patient using CT as the gold standard. Targets in the frame coordinates were chosen from CT and compared with their locations in MR. Localizing errors using MR compared with CT were as large as 3.7 mm before correction and were reduced to less than 1.11 mm after correction.

  View details for Web of Science ID A1995RF76800015

  View details for PubMedID 7674887

• PHASE UNWRAPPING OF MR PHASE IMAGES USING POISSON EQUATION IEEE TRANSACTIONS ON IMAGE PROCESSING Song, S. M., Napel, S., Pelc, N. J., Glover, G. H. 1995; 4 (5): 667-676

  Abstract

  The authors have developed a technique based on a solution of the Poisson equation to unwrap the phase in magnetic resonance (MR) phase images. The method is based on the assumption that the magnitude of the inter-pixel phase change is less than pi per pixel. Therefore, the authors obtain an estimate of the phase gradient by "wrapping" the gradient of the original phase image. The problem is then to obtain the absolute phase given the estimate of the phase gradient. The least-squares (LS) solution to this problem is shown to be a solution of the Poisson equation allowing the use of fast Poisson solvers. The absolute phase is then obtained by mapping the LS phase to the nearest multiple of 2 K from the measured phase. The proposed technique is evaluated using MR phase images and is proven to be robust in the presence of noise. An application of the proposed method to the 3-point Dixon technique for water and fat separation is demonstrated.

  View details for Web of Science ID A1995QV84500012

  View details for PubMedID 18290015

• CIRCLE OF WILLIS - EVALUATION WITH SPIRAL CT ANGIOGRAPHY, MR-ANGIOGRAPHY, AND CONVENTIONAL ANGIOGRAPHY RADIOLOGY Katz, D. A., Marks, M. P., NAPEL, S. A., Bracci, P. M., Roberts, S. L. 1995; 195 (2): 445-449

  Abstract

  To evaluate the use of spiral computed tomographic (CT) angiography in the analysis of the arteries of the circle of Willis and compare these results with magnetic resonance (MR) angiography and conventional angiography.The results in 17 patients who underwent examination were prospectively studied in a blinded fashion. The presence or absence of the arteries of the circle of Willis was determined by using maximum intensity projection reconstructions from CT angiography and MR angiography. These results were compared with results from conventional angiography.Similar sensitivities were determined for CT angiography (88.5%) and MR angiography (85.5%); however, MR angiography was found to differ significantly (P = .005) from conventional angiography. No significant differences (P > .05) were found between the two modalities and conventional angiography in the detection of the anterior, middle, or posterior cerebral arteries or the anterior communicating artery.Spiral CT angiography is highly sensitive in the detection of arterial anatomy in the circle of Willis and is a reliable alternative to MR angiography.

  View details for Web of Science ID A1995QU71700026

  View details for PubMedID 7724764

• Method for correcting magnetic resonance image distortion for frame-based stereotactic surgery, with preliminary results. Journal of image guided surgery Sumanaweera, T. S., Adler, J. R., Glover, G. H., Hemler, P. F., van den Elsen, P. A., Martin, D., Napel, S. 1995; 1 (3): 151-157

  Abstract

  We previously described a technique for correcting patient-specific magnetic field inhomogeneity spatial distortion in magnetic resonance images (MRI), which was not applicable to patients fitted with MRI-compatible stereotactic fiducial frames. Here we describe an improvement to the technique that permits application for these patients. Measurements with a cadaver head show that this method achieves MRI stereotactic localization accuracy of 1 mm.

  View details for PubMedID 9079440

• A quantitative comparison of residual error for three different multimodality registration techniques 14th International Conference on Information Processing in Medical Imaging Hemler, P. F., VANDENELSEN, P. A., Sumanaweera, T. S., Napel, S., Drace, J., Adler, J. R. KLUWER ACADEMIC PUBL. 1995: 251–262

  View details for Web of Science ID A1995BD56U00021

• A versatile system for multimodality image fusion. Journal of image guided surgery Hemler, P. F., Sumanaweera, T. S., van den Elsen, P. A., Napel, S., Adler, J. 1995; 1 (1): 35-45

  Abstract

  This paper presents a versatile system for registering and visualizing computed tomography and magnetic resonance images. The system utilizes a semi-automatic, surface-based registration strategy which has proven useful for registering a number of different anatomical structures. A triangular mesh approximates surfaces in one image set while a set of surface points is used as a surface approximation in the other set. A non-linear optimization procedure determines the transformation that minimizes the total sum-squared perpendicular distance between triangles of the mesh and surface points. This system has been used without modification to successfully register images of the brain, spine and calcaneus.

  View details for PubMedID 9079425

• CHARACTERIZATION OF SPATIAL DISTORTION IN MAGNETIC-RESONANCE-IMAGING AND ITS IMPLICATIONS FOR STEREOTAXIC SURGERY NEUROSURGERY Sumanaweera, T. S., Adler, J. R., Napel, S., Glover, G. H. 1994; 35 (4): 696-703

  Abstract

  The different sources of spatial distortion in magnetic resonance images are reviewed from the point of view of stereotactic target localization. The extents of the two most complex sources of spatial distortion, gradient field nonlinearities and magnetic field inhomogeneities, are discussed both qualitatively and quantitatively. Several ways by which the spatial distortion resulting from these sources can be minimized are discussed. The clinical relevance of the spatial distortion along with some strategies to minimize the localization errors in magnetic resonance-guided stereotaxy are presented.

  View details for Web of Science ID A1994PH68700046

  View details for PubMedID 7808613

• SINGLE BREATH-HOLD PULMONARY MAGNETIC-RESONANCE ANGIOGRAPHY - OPTIMIZATION AND COMPARISON OF 3 IMAGING STRATEGIES INVESTIGATIVE RADIOLOGY Rubin, G. D., Herfkens, R. J., Pelc, N. J., Foo, T. K., Napel, S., Shimakawa, A., Steiner, R. M., Bergin, C. J. 1994; 29 (8): 766-772

  Abstract

  Ultrafast gradient-recalled-echo techniques for obtaining high-quality pulmonary magnetic resonance angiograms within a single breath-hold were optimized.Fourteen subjects were imaged with both the body coil and a phased-array surface coil, using three gradient-recalled-echo pulse sequences: 1) two-dimensional sequential; 2) two-dimensional interleaved; and 3) volumetric acquisitions. Image quality was assessed with varied flip angle, receiver bandwidth, slice thickness/number, and matrix size. Cardiac compensation diminished ghost artifacts in the interleaved sequence. Individual sagittal sections and maximum intensity projections were reviewed.Pulmonary magnetic resonance angiograms acquired with volumetric and two-dimensional interleaved gradient-recalled-echo pulse sequences benefit greatest from intravenous gadolinium and result in greater pulmonary arterial visualization than traditional time-of-flight techniques. Phased-array coils result in improved vessel detection.High-quality breath-held pulmonary magnetic resonance angiography can be obtained with an intravenous contrast-enhanced gradient-recalled-echo acquisition; however, image quality is dependent on the pulse sequence.

  View details for Web of Science ID A1994PE81100006

  View details for PubMedID 7960627

• DETERMINING CARDIAC VELOCITY-FIELDS AND INTRAVENTRICULAR PRESSURE DISTRIBUTION FROM A SEQUENCE OF ULTRAFAST CT CARDIAC IMAGES IEEE TRANSACTIONS ON MEDICAL IMAGING Song, S. M., Leahy, R. M., Boyd, D. P., Brundage, B. H., Napel, S. 1994; 13 (2): 386-397

  Abstract

  A method of computing the velocity field and pressure distribution from a sequence of ultrafast CT (UFCT) cardiac images is demonstrated. UFCT multi-slice cine imaging gives a series of tomographic slices covering the volume of the heart at a rate of 17 frames per second. The complete volume data set can be modeled using equations of continuum theory and through regularization, velocity vectors of both blood and tissue can be determined at each voxel in the volume. The authors present a technique to determine the pressure distribution throughout the volume of the left ventricle using the computed velocity field. A numerical algorithm is developed by discretizing the pressure Poisson equation (PPE), which Is based on the Navier-Stokes equation. The algorithm is evaluated using a mathematical phantom of known velocity and pressure-Couette flow. It is shown that the algorithm based on the PPE can reconstruct the pressure distribution using only the velocity data. Furthermore, the PPE is shown to be robust in the presence of noise. The velocity field and pressure distribution derived from a UFCT study of a patient are also presented.

  View details for Web of Science ID A1994NV75300017

  View details for PubMedID 18218514

• DOSIMETRY COMPUTATION FROM TISSUE DISTRIBUTION DATA ENTERED IN AN ELECTRONIC MOUSE MODEL WITH CONVOLUTION OF THE BETA-RAY DEPOSITION PROFILE Goris, M. L., Nielsen, K. K., NAPEL, S. A. SOC NUCLEAR MEDICINE INC. 1994: P161–P161

  View details for Web of Science ID A1994NK90900646

• SPIRAL CT OF RENAL-ARTERY STENOSIS - COMPARISON OF 3-DIMENSIONAL RENDERING TECHNIQUES RADIOLOGY Rubin, G. D., Dake, M. D., Napel, S., Jeffrey, R. B., McDonnell, C. H., Sommer, F. G., Wexler, L., Williams, D. M. 1994; 190 (1): 181-189

  Abstract

  To evaluate the accuracy of computed tomographic (CT) angiography in the detection of renal artery stenosis (RAS).CT angiography was performed in 31 patients undergoing conventional renal arteriography. CT angiographic data were reconstructed with shaded surface display (SSD) and maximum-intensity projection (MIP). Stenosis was graded with a four-point scale (grades 0-3). The presence of mural calcification, poststenotic dilatation, and nephrographic abnormalities was also noted.CT angiography depicted all main (n = 62) and accessory (n = 11) renal arteries that were seen at conventional arteriography. MIP CT angiography was 92% sensitive and 83% specific for the detection of grade 2-3 stenoses (> or = 70% stenosis). SSD CT angiography was 59% sensitive and 82% specific for the detection of grade 2-3 stenoses. The accuracy of stenosis grading was 80% with MIP and 55% with SSD CT angiography. Poststenotic dilatation and the presence of an abnormal nephrogram were 85% and 98% specific, respectively.CT angiography shows promise in the diagnosis of RAS. The accuracy of CT angiography varies with the three-dimensional rendering technique employed.

  View details for Web of Science ID A1994MW25300036

  View details for PubMedID 8259402

• GREY VALUE CORRELATION TECHNIQUES USED FOR AUTOMATIC MATCHING OF CT AND MR BRAIN AND SPINE IMAGES 3rd Conference on Visualization in Biomedical Computing 1994 (VBC 94) VANDENELSEN, P. A., Pol, E. J., Sumanaweera, T. S., Hemler, P. F., Napel, S., Adler, J. R. SPIE - INT SOC OPTICAL ENGINEERING. 1994: 227–237

  View details for Web of Science ID A1994BB64H00024

• VOLUMETRIC APPLICATIONS FOR SPIRAL CT IN THE THORAX Conference on Physiology and Function from Multidimensional Images Rubin, G. D., Napel, S., Leung, A. SPIE - INT SOC OPTICAL ENGINEERING. 1994: 353–360

  View details for Web of Science ID A1994BA56T00032

• A SYSTEM FOR MULTIMODALITY IMAGE FUSION 7th Annual IEEE Symposium on Computer-Based Medical Systems Hemler, P. F., Sumanaweera, T., VANDENELSEN, P. A., Napel, S., Adler, J. I E E E, COMPUTER SOC PRESS. 1994: 335–340

  View details for Web of Science ID A1994BB17X00060

• QUANTIFYING MRI GEOMETRIC DISTORTION IN TISSUE MAGNETIC RESONANCE IN MEDICINE Sumanaweera, T., Glover, G., Song, S., Adler, T., Napel, S. 1994; 31 (1): 40-47

  Abstract

  We present a method to quantify the MR field inhomogeneity geometric distortion to subpixel accuracy without using objects of known dimensions and without using an external standard such as CT. Our method may be used to quantify the geometric accuracy of MR images of anatomical structures of unknown geometry and also to test any geometry correction scheme. We have quantified the distortion in a tissue phantom and found the largest error to be approximately 2.8 pixels (1.8 mm) for Bo = 1.5 T, G = 3.13 mT/m and FOV = 160 x 160 x 70.7 mm3. We also found that our previously published correction technique reduced the largest error to 0.3 pixels (mu = 0.02 and sigma = 0.07 pixels).

  View details for Web of Science ID A1994MP54200005

  View details for PubMedID 8121267

• 3-DIMENSIONAL SPIRAL COMPUTED TOMOGRAPHIC ANGIOGRAPHY - AN ALTERNATIVE IMAGING MODALITY FOR THE ABDOMINAL-AORTA AND ITS BRANCHES 8TH ANNUAL MEETING OF THE WESTERN-VASCULAR-SOC Rubin, G. D., Walker, P. J., Dake, M. D., Napel, S., Jeffrey, R. B., McDonnell, C. H., Mitchell, R. S., Miller, D. C. MOSBY-ELSEVIER. 1993: 656–65

  Abstract

  We sought to apply a new technique of computed tomographic angiography (CTA) to the preoperative and postoperative assessment of the abdominal aorta and its branches.After a peripheral intravenous contrast injection, the patient is continuously advanced through a spiral CT scanner, while maintaining a 30-second breath-hold. Thirty-five patients with abdominal aortic, renal, and visceral arterial disease have undergone CTA.Diagnostic three-dimensional images were obtained in patients with aortic aneurysms (n = 9), aortic dissections (n = 4), and mesenteric artery stenoses (n = 4). The technique has also been used to assess vessels after operative reconstruction or endovascular intervention in 18 patients. These preliminary studies have correlated well with conventional arteriographic findings. In aneurysmal disease both the lumen and mural thrombus and associated renal artery stenoses are visualized. The true and false channels of aortic dissections and the perfusion source of the visceral vessels are clearly shown; patency of visceral and renal reconstruction or stent placement are confirmed. CTA is relatively noninvasive and can be completed in less time than conventional angiography with less radiation exposure.This initial experience suggests that CTA may be a valuable alternative to conventional arteriography in the evaluation of the aorta and its branches.

  View details for PubMedID 8411473

• STS-MIP - A NEW RECONSTRUCTION TECHNIQUE FOR CT OF THE CHEST JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Napel, S., Rubin, G. D., Jeffrey, R. B. 1993; 17 (5): 832-838

  Abstract

  The authors present sliding thin-slab maximum intensity projection (STS-MIP) as a technique for improved visualization of blood vessels and airways from rapidly acquired thin-section CT data. The STS-MIP reconstructions can be computed rapidly and without operator intervention directly from the transaxial sections. The resulting images retain the high contrast resolution of thin-section (1-3 mm) CT while providing vascular or airway visibility within a sequence of overlapping thin-slabs (3-10 mm). Examples are presented of pulmonary vessels and airways derived from spiral CT and of pulmonary vessels and coronary arteries derived from electron-beam CT.

  View details for Web of Science ID A1993LX47700036

  View details for PubMedID 8370848

• NOISE-REDUCTION IN 3-DIMENSIONAL PHASE-CONTRAST MR VELOCITY-MEASUREMENTS JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING Song, S. M., Napel, S., Glover, G. H., Pelc, N. J. 1993; 3 (4): 587-596

  Abstract

  The authors have developed a method to reduce noise in three-dimensional (3D) phase-contrast magnetic resonance (MR) velocity measurements by exploiting the property that blood is incompressible and, therefore, the velocity field describing its flow must be divergence-free. The divergence-free condition is incorporated by a projection operation in Hilbert space. The velocity field obtained with 3D phase-contrast MR imaging is projected onto the space of divergence-free velocity fields. The reduction of noise is achieved because the projection operation eliminates the noise component that is not divergence-free. Signal-to-noise ratio (S/N) gains on the order of 15%-25% were observed. The immediate effect of this noise reduction manifests itself in higher-quality phase-contrast MR angiograms. Alternatively, the S/N gain can be traded for a reduction in imaging time and/or improved spatial resolution.

  View details for Web of Science ID A1993LM78100006

  View details for PubMedID 8347951

• DIAGNOSIS OF CAROTID-ARTERY DISEASE - PRELIMINARY EXPERIENCE WITH MAXIMUM-INTENSITY-PROJECTION SPIRAL CT ANGIOGRAPHY AMERICAN JOURNAL OF ROENTGENOLOGY Marks, M. P., Napel, S., Jordan, J. E., Enzmann, D. R. 1993; 160 (6): 1267-1271

  Abstract

  Spiral CT allows continuous data to be acquired rapidly, and if a correctly timed IV bolus of contrast material is given, spiral CT angiography can be performed. This study was designed to evaluate spiral CT angiography with maximum-intensity-projection reconstructions for assessing the degree of carotid artery stenosis.Spiral CT angiography (of 28 carotid bifurcations in 14 patients) was compared in a blinded fashion with conventional angiography (of 28 bifurcations) and with two-dimensional time-of-flight MR angiography (of 12 bifurcations) to assess degree of stenosis. A nonblinded comparison of the contour of the lumen at the site of stenosis was then made between conventional angiography, spiral CT angiography, and MR angiography. The degree of stenosis was measured in each internal carotid artery and categorized as mild (< 30%), moderate (30-69%), or severe (70-99%) stenosis or as occlusion. Maximum-intensity-projection images were used for the evaluations; however, if calcification was circumferential and the lumen of the carotid artery could not be analyzed in the area of the calcification, the axial source images were used.The results of CT angiography and conventional angiography agreed overall in 25 (89%) of 28 cases (r = .921, p = .05, Spearman rank correlation). The presence of severe stenosis or occlusion was correctly identified in seven of seven cases. In the moderate and mild stenosis categories, 18 (86%) of 21 were correctly identified (r = .802, p = .122). Three internal carotid arteries (11%) had circumferential calcification that necessitated evaluation of the axial source images, and the measurements obtained from the axial images agreed well with angiographic findings. MR angiography correlated well with the various categories of stenosis. However, when we compared MR angiography directly with CT angiography and conventional angiography, we found that the degree of stenosis was overestimated when MR angiography was used.Our results show that spiral CT angiography shows normal and abnormal carotid anatomy well when compared with conventional angiography. The short examination time and clear depiction of arterial caliber in areas of stenosis are significant advantages of spiral CT angiography compared with MR angiography.

  View details for PubMedID 8498231

• 3-DIMENSIONAL SPIRAL CT ANGIOGRAPHY OF THE ABDOMEN - INITIAL CLINICAL-EXPERIENCE RADIOLOGY Rubin, G. D., Dake, M. D., NAPEL, S. A., McDonnell, C. H., Jeffrey, R. B. 1993; 186 (1): 147-152

  Abstract

  Spiral computed tomography (CT) is a new technology that couples continuous tube rotation with continuous table feed. This allows compilation of a data set that has continuous anatomic information without the establishment of arbitrary boundaries at section interfaces as in conventional CT. The unique method of data collection of the spiral scanner has been combined with a dynamic intravenous contrast material bolus to image abdominal vasculature, specifically, the aorta, renal arteries, and splanchnic circulation. Through various techniques of image processing, including surface renderings and maximum-intensity projections, it is possible to obtain excellent anatomic detail of the aorta and its major branches. The authors applied this technique in 15 patients and reliably saw third-order aortic branches as well as third-order splenic-portal venous anatomic detail with remarkable clarity. Pathologic conditions detected include stenotic renal arteries, abdominal aortic dissection, abdominal aortic aneurysm, and celiac bypass graft occlusion.

  View details for Web of Science ID A1993KD15300033

  View details for PubMedID 8416556

• A LEAST-SQUARES BASED PHASE UNWRAPPING ALGORITHM FOR MRI Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 93) Song, S. M., Napel, S., Pelc, N. J., Glover, G. H. IEEE. 1993: 1784–1788

  View details for Web of Science ID A1993BA79S00365

• QUANTIFICATION OF THE GEOMETRIC ACCURACY OF MRI IN TISSUE - A NEW APPROACH USING MRI ITSELF Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 93) Sumanaweera, T. S., Napel, S., Glover, G. H., Song, S. M. IEEE. 1993: 1789–1793

  View details for Web of Science ID A1993BA79S00366

• CT ANGIOGRAPHY WITH SPIRAL CT AND MAXIMUM INTENSITY PROJECTION RADIOLOGY Napel, S., Marks, M. P., Rubin, G. D., Dake, M. D., McDonnell, C. H., Song, S. M., Enzmann, D. R., Jeffrey, R. B. 1992; 185 (2): 607-610

  Abstract

  The authors describe a technique for obtaining angiographic images by means of spiral computed tomography (CT), preprocessing of reconstructed three-dimensional sections to suppress bone, and maximum intensity projection. The technique has some limitations, but preliminary results in 48 patients have shown excellent anatomic correlation with conventional angiography in studies of the abdomen, the circle of Willis in the brain, and the extracranial carotid arteries. With continued development and evaluation, CT angiography may prove useful as a screening tool or replacement for conventional angiography in some patients.

  View details for PubMedID 1410382

• INTERACTIVE DISPLAY OF VOLUMETRIC DATA BY FAST FOURIER PROJECTION COMPUTERIZED MEDICAL IMAGING AND GRAPHICS Dunne, S., Napel, S., Rutt, B. 1992; 16 (4): 237-251

  Abstract

  This article describes a new algorithm for reprojection of volumetric data, called Fast Fourier Projection (FFP), which is one to two orders of magnitude faster than conventional methods such as ray casting. The theoretical basis of the new method is developed in a unified mathematical framework encompassing slice imaging and conventional volumetric reprojection methods. Software implementation is discussed in detail. The article closes with an account of experience with a prototype FFP implementation, and applications of the technique in medical visualization.

  View details for Web of Science ID A1992JJ48100001

  View details for PubMedID 1511397

• Visualizing three-dimensional flow with simulated streamlines and three-dimensional phase-contrast MR imaging. Journal of magnetic resonance imaging : JMRI Napel, S., Lee, D. H., Frayne, R., Rutt, B. K. 1992; 2 (2): 143-153

  Abstract

  Three-dimensional (3D) velocity maps acquired with 3D phase-contrast magnetic resonance (MR) imaging contain information regarding complex motions that occur during imaging. A technique called simulated streamlines, which facilitates the display and comprehension of these velocity data, is presented. Single or multiple seed points may be identified within blood vessels of interest and tracked through the velocity field. The resulting trajectories are combined with a 3D MR angiogram and displayed with 3D volume visualization software. Mathematical analysis highlights potential applications and pitfalls of the technique, which was implemented both in phantoms and in vivo with excellent results. For example, single streamlines reveal helical flow patterns in aneurysms, and multiple streamlines seeded in the common carotid artery reveal branch filling-time relationships and slow filling of the carotid bulb. The technique is helpful in understanding these complex flow patterns.

  View details for PubMedID 1562765

• VISUALIZING 3-DIMENSIONAL FLOW WITH SIMULATED STREAMLINES AND 3-DIMENSIONAL PHASE-CONTRAST MR IMAGING JMRI-JOURNAL OF MAGNETIC RESONANCE IMAGING Napel, S., Lee, D. H., Frayne, R., Rutt, B. K. 1992; 2 (2): 143-153

  Abstract

  Three-dimensional (3D) velocity maps acquired with 3D phase-contrast magnetic resonance (MR) imaging contain information regarding complex motions that occur during imaging. A technique called simulated streamlines, which facilitates the display and comprehension of these velocity data, is presented. Single or multiple seed points may be identified within blood vessels of interest and tracked through the velocity field. The resulting trajectories are combined with a 3D MR angiogram and displayed with 3D volume visualization software. Mathematical analysis highlights potential applications and pitfalls of the technique, which was implemented both in phantoms and in vivo with excellent results. For example, single streamlines reveal helical flow patterns in aneurysms, and multiple streamlines seeded in the common carotid artery reveal branch filling-time relationships and slow filling of the carotid bulb. The technique is helpful in understanding these complex flow patterns.

  View details for Web of Science ID A1992HK05600003

• FAST FOURIER PROJECTION FOR MR ANGIOGRAPHY MAGNETIC RESONANCE IN MEDICINE Napel, S., Dunne, S., Rutt, B. K. 1991; 19 (2): 393-405

  Abstract

  We have developed a technique called fast Fourier projection which rapidly produces projections through images and is particularly useful for generating MR angiograms. Based on the projection-slice theorem of Fourier transform theory, this method extracts planes from three-dimensional spatial frequency space and computes projections at arbitrary viewing angles by two-dimensional inverse Fourier transformation. Typical computation times are on the order of 1 s per projection. This performance makes possible interactive selection of optimal projection directions for visualizing the desired vasculature in single or stereo-pair angiographic images and drastically reduces the time required to generate sequences of projections for display in movie loops compared to the conventional ray-casting approach. The method is easily implemented on off-line workstations or directly on MRI computer systems.

  View details for Web of Science ID A1991FQ24300029

  View details for PubMedID 1881328

• MAGNETIC-RESONANCE TECHNIQUES FOR BLOOD-FLOW MEASUREMENT AND VASCULAR IMAGING JOURNAL OF THE CANADIAN ASSOCIATION OF RADIOLOGISTS-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES Rutt, B. K., Napel, S. 1991; 42 (1): 21-30

  Abstract

  The authors review the history and physical principles behind vascular magnetic resonance imaging (MRI) techniques, developed to measure blood flow noninvasively and to display images of the vasculature. All these techniques have been used to create magnetic resonance angiograms, in which the vasculature is shown in a projection format similar to x-ray angiography. Signal loss limits the effectiveness of "white-blood" magnetic resonance angiography techniques, since slow flow and complex flow often cause a drop in signal and consequently a loss of accuracy in depicting vessel anatomy. "Black-blood" magnetic resonance angiography is described as a method that avoids these problems of signal loss. Selective black-blood magnetic resonance angiography is introduced as a technique for improving the visualization of the vasculature when other signal-void structures are present in the volume of interest.

  View details for Web of Science ID A1991FC78400004

  View details for PubMedID 2001525

• PROJECTION PRESATURATION .2. SINGLE-SHOT LOCALIZATION OF MULTIPLE REGIONS OF INTEREST JOURNAL OF MAGNETIC RESONANCE Singh, S., Rutt, B. K., Napel, S. 1990; 90 (2): 313-329

  View details for Web of Science ID A1990EK48400008

• HIGH-SPEED COMPUTED-TOMOGRAPHY - SYSTEMS AND PERFORMANCE APPLIED OPTICS PESCHMANN, K. R., Napel, S., COUCH, J. L., Rand, R. E., ALEI, R., ACKELSBERG, S. M., Gould, R., Boyd, D. P. 1985; 24 (23): 4052-4060

  View details for Web of Science ID A1985AVY5200019

  View details for PubMedID 18224161

• HIGH-SPEED, MULTI-SLICE, X-RAY COMPUTED-TOMOGRAPHY PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS Boyd, D. P., COUCH, J. L., NAPEL, S. A., Parker, D. L., PESCHMANN, K. R., Rand, R. E., Herrmannsfeldt, W. B. 1982; 372: 139-150

  View details for Web of Science ID A1982RL28700019

• A COMPARISON BETWEEN THE INFORMATION IN GATED AND NON-GATED CARDIAC CT IMAGES JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY Ringertz, H. G., SKIOLDEBRAND, C. G., Refsum, H., Tyberg, J. V., NAPEL, S. A., Lipton, M. J. 1982; 6 (5): 933-938

  View details for Web of Science ID A1982PF82600010

  View details for PubMedID 7142508

• MEASUREMENT OF CARDIAC-OUTPUT BY COMPUTED TRANSMISSION TOMOGRAPHY INVESTIGATIVE RADIOLOGY Herfkens, R. J., Axel, L., Lipton, M. J., Napel, S., BERNINGER, W., REDINGTON, R. 1982; 17 (6): 550-553

  Abstract

  The capability of computed tomography (CT) scanning to measure cardiac output was explored using ten anesthetized dogs, and the results were compared with those obtained by thermodilution. Dynamic CT scans were performed at the level of the aortic root while small peripheral intravenous boluses of contrast medium were injected. Time/density curves were generated using a gamma variate fitting program. These were used to estimate cardiac output by applying indicator dilution principles. CT results correlated favorably (r = 0.86) with those of thermodilution. This feasibility study indicates the utility of CT for obtaining physiologic measurements of cardiac function and should encourage further studies to develop the potential of CT for cardiovascular diagnostic purposes.

  View details for Web of Science ID A1982PS51300005

  View details for PubMedID 7152858

• FUNCTIONAL IMAGING OF THE BRAIN USING COMPUTED-TOMOGRAPHY RADIOLOGY BERNINGER, W. H., Axel, L., Norman, D., Napel, S., REDINGTON, R. W. 1981; 138 (3): 711-716

  Abstract

  Data from rapid-sequence CT scans of the same cross section, obtained following bolus injection of contrast material, were analyzed by functional imaging. The information contained in a large number of images can be compressed into one or two gray-scale images which can be evaluated both qualitatively and quantitatively. The computational techniques are described and applied to the generation of images depicting bolus transit time, arrival time, peak time, and effective width.

  View details for Web of Science ID A1981LE80900029

  View details for PubMedID 7465851

• FETAL BLOOD VELOCITY WAVEFORMS AMERICAN JOURNAL OF OBSTETRICS AND GYNECOLOGY MCCALLUM, W. D., Williams, C. S., Napel, S., DAIGLE, R. E. 1978; 132 (4): 425-429

  Abstract

  In this paper a method is described for obtaining and characterizing fetal blood velocity waveforms. The signals were recorded with a range-gated Doppler instrument and characterized after spectral analysis. Preliminary observations indicate differences in the waveforms obtained during normal pregnancies compared with some complicated pregnancies.

  View details for Web of Science ID A1978FT42100015

  View details for PubMedID 707584