Honors & Awards

  • Stanford Molecular Imaging Scholar (SMIS Fellow), Stanford (2018-2021)
  • Industry Selected Poster Award, World Molecular Imaging Congress (WMIC) (2018)
  • Student Travel Award, World Molecular Imaging Congress (WMIC) (2017)
  • Outstanding Translational Research Award, Department of Biomedical Engineering at Georgia Institute of Technology and Emory University (2016)
  • Rising Stars in Biomedical Engineering and Science, Massachusetts Institute of Technology, Cambridge (2016)
  • Outstanding Research Poster Award, Inauguration Workshop and Launch of the Integrative Cancer Imaging Research Initiative, Emory&Gatech (2016)
  • Coulter Fellowship, The Wallace H. Coulter Foundation (2009-2012)
  • Excellent Academic Scholarship, Shanghai Jiao Tong University (2009-2012)
  • BOSCH Academic Scholarship, Shanghai Jiao Tong University (2011)
  • BlackBerry Academic Scholarship, Shanghai Jiao Tong University (2011)
  • National Scholarship of China, Ministry of Education of the P. R. China (2005)

Professional Education

  • Doctor of Philosophy, Georgia Institute of Technology and Emory University, Biomedical Engineering
  • Master of Science, Georgia Institute of Technology, Electrical and Computer Engineering
  • Master of Science, Shanghai Jiao Tong University, Precision Instrument and Machinery

Stanford Advisors

All Publications

  • Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging. Clinical cancer research : an official journal of the American Association for Cancer Research Lu, G., Little, J. V., Wang, X., Zhang, H., Patel, M. R., Griffith, C. C., El-Deiry, M. W., Chen, A. Y., Fei, B. 2017; 23 (18): 5426–36


    Purpose: This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers.Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens.Results: Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection.Conclusions: This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future. Clin Cancer Res; 23(18); 5426-36. ©2017 AACR.

    View details for DOI 10.1158/1078-0432.CCR-17-0906

    View details for PubMedID 28611203

  • Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. Journal of biomedical optics Halicek, M., Lu, G., Little, J. V., Wang, X., Patel, M., Griffith, C. C., El-Deiry, M. W., Chen, A. Y., Fei, B. 2017; 22 (6): 60503


    Surgical cancer resection requires an accurate and timely diagnosis of the cancer margins in order to achieve successful patient remission. Hyperspectral imaging (HSI) has emerged as a useful, noncontact technique for acquiring spectral and optical properties of tissue. A convolutional neural network (CNN) classifier is developed to classify excised, squamous-cell carcinoma, thyroid cancer, and normal head and neck tissue samples using HSI. The CNN classification was validated by the manual annotation of a pathologist specialized in head and neck cancer. The preliminary results of 50 patients indicate the potential of HSI and deep learning for automatic tissue-labeling of surgical specimens of head and neck patients.

    View details for DOI 10.1117/1.JBO.22.6.060503

    View details for PubMedID 28655055

    View details for PubMedCentralID PMC5482930

  • Detection and delineation of squamous neoplasia with hyperspectral imaging in a mouse model of tongue carcinogenesis. Journal of biophotonics Lu, G., Wang, D., Qin, X., Muller, S., Wang, X., Chen, A. Y., Chen, Z. G., Fei, B. 2017


    yperspectral imaging (HSI) holds the potential for the noninvasive detection of cancers. Oral cancers are often diagnosed at a late stage when treatment is less effective and the mortality and morbidity rates are high. Early detection of oral cancer is, therefore, crucial in order to improve the clinical outcomes. To investigate the potential of HSI as a non-invasive diagnostic tool, an animal study was designed to acquire hyperspectral images of in vivo and ex vivo mouse tongues from a chemically induced tongue carcinogenesis model. A variety of machine-learning algorithms, including discriminant analysis, ensemble learning, and support vector machines, were evaluated for tongue neoplasia detection using HSI and were validated by the reconstructed pathological gold-standard maps. The diagnostic performance of HSI, autofluorescence imaging, and fluorescence imaging were compared in this study. Color-coded prediction maps were generated to display the predicted location and distribution of premalignant and malignant lesions. This study suggests that hyperspectral imaging combined with machine-learning techniques can provide a non-invasive tool for the quantitative detection and delineation of squamous neoplasia. Detection and delineation of tongue neoplasia with hyperspectral imaging validated by the pathological gold standard.

    View details for DOI 10.1002/jbio.201700078

    View details for PubMedID 28921845

  • A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Pike, R., Lu, G., Wang, D., Chen, Z. G., Fei, B. 2016; 63 (3): 653-663


    The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model.An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information.The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images.Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.

    View details for DOI 10.1109/TBME.2015.2468578

    View details for Web of Science ID 000371933800021

    View details for PubMedID 26285052

    View details for PubMedCentralID PMC4791052

  • Framework for hyperspectral image processing and quantification for cancer detection during animal tumor surgery JOURNAL OF BIOMEDICAL OPTICS Lu, G., Wang, D., Qin, X., Halig, L., Muller, S., Zhang, H., Chen, A., Pogue, B. W., Chen, Z. G., Fei, B. 2015; 20 (12)


    Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.

    View details for DOI 10.1117/1.JBO.20.12.126012

    View details for Web of Science ID 000368440300037

    View details for PubMedID 26720879

    View details for PubMedCentralID PMC4691647

  • Spectral-spatial classification for noninvasive cancer detection using hyperspectral imaging JOURNAL OF BIOMEDICAL OPTICS Lu, G., Halig, L., Wang, D., Qin, X., Chen, Z. G., Fei, B. 2014; 19 (10)


    Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.

    View details for DOI 10.1117/1.JBO.19.10.106004

    View details for Web of Science ID 000345837200021

    View details for PubMedID 25277147

    View details for PubMedCentralID PMC4183763

  • Medical hyperspectral imaging: a review JOURNAL OF BIOMEDICAL OPTICS Lu, G., Fei, B. 2014; 19 (1)


    Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

    View details for DOI 10.1117/1.JBO.19.1.010901

    View details for Web of Science ID 000331892700001

    View details for PubMedID 24441941

    View details for PubMedCentralID PMC3895860

  • Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Champion, A., Lu, G., Walker, M., Kothari, S., Osunkoya, A. O., Wang, M. D. 2014; 2014: 6446–49


    Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological images. To address this challenge, we develop a clinical decision support system for automated Fuhrman grading of renal carcinoma biopsy images. We extract 1316 color, shape, texture and topology features and develop one vs. all models for four Fuhrman grades. Our models are highly accurate with 90.4% accuracy in a four-class prediction. Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation. We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists' grading criteria. Our study identifies novel imaging features that are semantically linked to Fuhrman grading criteria.

    View details for DOI 10.1109/EMBC.2014.6945104

    View details for PubMedID 25571472

    View details for PubMedCentralID PMC4983417

  • The Clinical Application of Fluorescence-Guided Surgery in Head and Neck Cancer JOURNAL OF NUCLEAR MEDICINE van Keulen, S., Nishio, N., Fakurnejad, S., Birkeland, A., Martin, B. A., Lu, G., Zhou, Q., Chirita, S. U., Forouzanfar, T., Colevas, A., van den Berg, N. S., Rosenthal, E. L. 2019; 60 (6): 758–63
  • Optimal Dosing Strategy for Fluorescence-Guided Surgery with Panitumumab-IRDye800CW in Head and Neck Cancer. Molecular imaging and biology : MIB : the official publication of the Academy of Molecular Imaging Nishio, N., van den Berg, N. S., van Keulen, S., Martin, B. A., Fakurnejad, S., Zhou, Q., Lu, G., Chirita, S. U., Kaplan, M. J., Divi, V., Colevas, A. D., Rosenthal, E. L. 2019


    PURPOSE: To identify the optimal dosing strategy for fluorescence-guided surgery in patients with head and neck squamous cell carcinoma, we conducted a dose-ranging study evaluating the anti-epidermal growth factor receptor (EGFR) therapeutic antibody, panitumumab, that was fluorescently labeled with the near-infrared dye IRDye800CW.PROCEDURES: Patients (n=24) received either 0.5 or 1.0mg/kg panitumumab-IRDye800CW in the weight-based dosing group or 25 or 50mg panitumumab-IRDye800CW in the fixed dosing group. Following surgery, whole primary specimens were imaged in a closed-field device and the mean fluorescence intensity (MFI) and tumor-to-background ratio (TBR) were assessed. Clinical variables, including dose, time of infusion-to-surgery, age, unlabeled dose, gender, primary tumor site, and tumor size, were analyzed to evaluate the factors affecting the fluorescence intensity in order to identify the optimal dose for intraoperative fluorescence imaging.RESULTS: A total of 24 primary tumor specimens were imaged and analyzed in this study. Although no correlations between TBR and dose of panitumumab-IRDye800CW were found, there were moderate-strong correlations between the primary tumor MFI and panitumumab-IRDye800CW dose for fixed dose (mg) (R2=0.42) and for dose/weight (mg/kg) (R2=0.54). Results indicated that the optimal MFI was at approximately 50mg for fixed dose and 0.75mg/kg for dose/weight. No significant differences were found for the primary tumor MFI and TBRs between the weight-based dosing and the fixed dosing groups. MFIs significantly increased when the infusion-to-surgery window was reduced to within 2days (vs. 3days or more, p<0.05).CONCLUSIONS: Antibody-based imaging for surgical resection is under investigation in multiple clinical trials. Our data suggests that a fixed dose of 50mg is an appropriate diagnostic dose for successful surgical fluorescence imaging.

    View details for PubMedID 31054001

  • The Clinical Application of Fluorescence-Guided Surgery in Head and Neck Cancer. Journal of nuclear medicine : official publication, Society of Nuclear Medicine van Keulen, S., Nishio, N., Fakurnejad, S., Birkeland, A., Martin, B. A., Lu, G., Zhou, Q., Chirita, S. U., Forouzanfar, T., Colevas, D., van den Berg, N. S., Rosenthal, E. L. 2019


    Although surgical resection has been the primary treatment modality of solid tumors for decades, surgeons still rely on visual cues and palpation to delineate healthy from cancerous tissue. This may contribute to the high rate (up to 30%) of positive margins in head and neck cancer resections. Margin status in these patients is the most important prognostic factor for overall survival. In addition, second primary lesions may be present at the time of surgery. Although often unnoticed by the medical team, these lesions can have significant survival ramifications. We hypothesize that real-time fluorescence imaging can enhance intraoperative decision-making by aiding the surgeon in detecting close or positive margins and visualizing unanticipated regions of primary disease. The purpose of this study was to assess the clinical utility of real-time fluorescence imaging for intraoperative decision-making. Methods: Head and neck cancer patients (n = 14) scheduled for curative resection were enrolled in a clinical trial evaluating panitumumab-IRDye800CW for surgical guidance (NCT02415881). Open-field fluorescence imaging was performed throughout the surgical procedure. The fluorescence signal was quantified as signal-to-background ratios to characterize the fluorescence contrast of regions of interest relative to background. Results: Fluorescence imaging was able to improve surgical decision-making in three cases (21.4%); identification of a close margin (n = 1) and unanticipated regions of primary disease (n = 2). Conclusion: This study demonstrates the clinical applications of fluorescence imaging on intraoperative decision-making. This information is required for designing phase III clinical trials using this technique. Furthermore, this study is the first to demonstrate this application for intraoperative decision-making during resection of primary tumors.

    View details for PubMedID 30733319

  • Rapid, non-invasive fluorescence margin assessment: Optical specimen mapping in oral squamous cell carcinoma. Oral oncology van Keulen, S., van den Berg, N. S., Nishio, N., Birkeland, A., Zhou, Q., Lu, G., Wang, H., Middendorf, L., Forouzanfar, T., Martin, B. A., Colevas, A. D., Rosenthal, E. L. 2019; 88: 58–65


    OBJECTIVE: Surgical resection remains the primary treatment for the majority of solid tumors. Despite efforts to obtain wide margins, close or positive surgical margins (<5 mm) are found in 15-30% of head and neck cancer patients. Obtaining negative margins requires immediate, intraoperative feedback of margin status. To this end, we propose optical specimen mapping of resected tumor specimens immediately after removal.MATERIALS AND METHODS: A first-in-human pilot study was performed in patients (n = 8) after infusion of fluorescently labeled antibody, panitumumab-IRDye800 to allow surgical mapping of the tumor specimen. Patients underwent standard of care surgical resection for head and neck squamous cell carcinoma (HNSCC). Optical specimen mapping was performed on the primary tumor specimen and correlated with pathological findings after tissue processing.RESULTS: Optical mapping of the specimen had a 95% sensitivity and 89% specificity to detect cancer within 5 mm (n = 160) of the cut surface. To detect tumor within 2 mm of the specimen surface, the sensitivity of optical specimen mapping was 100%. The maximal observed penetration depth of panitumumab-IRDye800 through human tissue in our study was 6.3 mm.CONCLUSION: Optical specimen mapping is a highly sensitive and specific method for evaluation of margins within <5 mm of the tumor mass in HNSCC specimens. This technology has potentially broad applications for ensuring adequate tumor resection and negative margins in head and neck cancers.

    View details for PubMedID 30616798

  • Selective modification of fluciclovine (F-18) transport in prostate carcinoma xenografts AMINO ACIDS Tade, F., Wiles, W. G., Lu, G., Bilir, B., Akin-Akintayo, O., Lee, J. S., Patil, D., Yu, W., Gherasim, C., Fei, B., Moreno, C. S., Osunkoya, A. O., Teoh, E. J., Oka, S., Okudaira, H., Goodman, M. M., Schuster, D. M. 2018; 50 (9): 1301–5


    We investigated if previously demonstrated inhibition of fluciclovine (18F) in vitro could be replicated in a PC3-Luc xenograft mouse model. Following intratumoral injection of 2-aminobicyclo-(2,2,1)-heptane-2-carboxylic acid (BCH), alpha-(methylamino)isobutyric acid (MeAIB) or saline, fluciclovine PET tumor-to-background activity was 43.6 (± 5.4)% and 25.3 (± 5.2)% lower in BCH (n = 6) and MeAIB (n = 5) injected PC3 Luc xenografts, respectively, compared to saline-injected controls (n = 2). Partial inhibition of fluciclovine uptake by BCH and MeAIB can be demonstrated in vivo similar to previous in vitro modeling.

    View details for PubMedID 29905905

  • Determination of Tumor Margins with Surgical Specimen Mapping Using Near-Infrared Fluorescence. Cancer research Gao, R. W., Teraphongphom, N. T., van den Berg, N. S., Martin, B. A., Oberhelman, N. J., Divi, V., Kaplan, M. J., Hong, S. S., Lu, G., Ertsey, R., Tummers, W. S., Gomez, A. J., Holsinger, F. C., Kong, C. S., Colevas, A. D., Warram, J. M., Rosenthal, E. L. 2018


    For many solid tumors, surgical resection remains the gold standard and tumor-involved margins are associated with poor clinical outcomes. Near-infrared (NIR) fluorescence imaging using molecular agents has shown promise for in situ imaging during resection. However, for cancers with difficult imaging conditions, surgical value may lie in tumor-mapping of surgical specimens. We thus evaluated a novel approach for real-time, intraoperative tumor margin assessment. 21 adult patients with biopsy-confirmed squamous cell carcinoma arising from the head and neck (HNSCC) scheduled for standard-of-care surgery were enrolled. Cohort 1 (n=3) received panitumumab-IRDye800CW at an intravenous microdose of 0.06 mg/kg, cohort 2A (n=5) received 0.5mg/kg, cohort 2B (n=7) received 1mg/kg, and cohort 3 (n=6) received 50 mg. Patients were followed 30 days post-infusion and adverse events were recorded. Imaging was performed using several closed- and wide-field devices. Fluorescence was histologically correlated to determine sensitivity and specificity. In situ imaging demonstrated tumor-to-background ratio (TBR) of 2-3, compared to ex vivo specimen imaging TBR of 5-6. We obtained clear differentiation between tumor and normal tissue, with a three-fold signal difference between positive and negative specimens (p<0.05). We achieved high correlation of fluorescence intensity with tumor location with sensitivities and specificities >89%; fluorescence predicted distance of tumor tissue to the cut surface of the specimen. This novel method of detecting tumor-involved margins in surgical specimens using a cancer-specific agent provides highly sensitive and specific, real-time, intraoperative surgical navigation in resections with complex anatomy which are otherwise less amenable to image guidance.

    View details for PubMedID 29967260

  • Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. Ultrasound in medicine & biology Guo, R., Lu, G., Qin, B., Fei, B. 2018; 44 (1): 37–70


    Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.

    View details for DOI 10.1016/j.ultrasmedbio.2017.09.012

    View details for PubMedID 29107353

  • Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients. Journal of biomedical optics Fei, B., Lu, G., Wang, X., Zhang, H., Little, J. V., Patel, M. R., Griffith, C. C., El-Diery, M. W., Chen, A. Y. 2017; 22 (8): 1–7


    A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.

    View details for DOI 10.1117/1.JBO.22.8.086009

    View details for PubMedID 28849631

    View details for PubMedCentralID PMC5572439

  • Functional MRI of the Eustachian Tubes in Patients With Nasopharyngeal Carcinoma: Correlation With Middle Ear Effusion and Tumor Invasion AMERICAN JOURNAL OF ROENTGENOLOGY Mo, Y., Zhuo, S., Tian, L., Zhou, J., Lu, G., Zhang, Y., Liu, L. 2016; 206 (3): 617-622


    We sought to combine the Valsalva maneuver with MRI to evaluate eustachian tube function in patients with nasopharyngeal carcinoma (NPC) and to correlate the extent of tumor invasion with the presence of middle ear effusion (MEE) and eustachian tube dysfunction (ETD).We performed MRI along the lengths of the eustachian tubes, before and after the Valsalva maneuver was performed, in 53 patients with untreated NPC. The images were reviewed by two radiologists.A total of 106 eustachian tubes and middle ears were studied. There was dysfunction in 37 eustachian tubes, which was always ipsilateral to the NPC. There was MEE in 26 ears of 22 patients. In all cases of MEE, there was ipsilateral ETD. ETD was correlated with tumor invasion of the ipsilateral pharyngeal recess (p < 0.001), pharyngeal opening of the eustachian tube (p < 0.001), the cartilaginous eustachian tube (p < 0.001), the eustachian cartilage (p < 0.001), Ostmann fat pad (p < 0.001), the levator veli palatine muscle (p < 0.001), and the tensor veli palatine muscle (p < 0.001). There was a strong correlation between the grade of parapharyngeal space invasion and ETD (r = 0.809; p < 0.001) and MEE (r = 0.693; p < 0.001).Combining the Valsalva maneuver with MRI is helpful in assessing the function of the eustachian tube in patients with NPC. The cause of MEE in patients with NPC is dysfunction of the eustachian tube opening, which is associated with tumor invasion around the eustachian tube.

    View details for DOI 10.2214/AJR.15.14751

    View details for Web of Science ID 000370848400028

    View details for PubMedID 26901020

  • Simulating cardiac ultrasound image based on MR diffusion tensor imaging MEDICAL PHYSICS Qin, X., Wang, S., Shen, M., Lu, G., Zhang, X., Wagner, M. B., Fei, B. 2015; 42 (9): 5144-5156


    Cardiac ultrasound simulation can have important applications in the design of ultrasound systems, understanding the interaction effect between ultrasound and tissue and setting the ground truth for validating quantification methods. Current ultrasound simulation methods fail to simulate the myocardial intensity anisotropies. New simulation methods are needed in order to simulate realistic ultrasound images of the heart.The proposed cardiac ultrasound image simulation method is based on diffusion tensor imaging (DTI) data of the heart. The method utilizes both the cardiac geometry and the fiber orientation information to simulate the anisotropic intensities in B-mode ultrasound images. Before the simulation procedure, the geometry and fiber orientations of the heart are obtained from high-resolution structural MRI and DTI data, respectively. The simulation includes two important steps. First, the backscatter coefficients of the point scatterers inside the myocardium are processed according to the fiber orientations using an anisotropic model. Second, the cardiac ultrasound images are simulated with anisotropic myocardial intensities. The proposed method was also compared with two other nonanisotropic intensity methods using 50 B-mode ultrasound image volumes of five different rat hearts. The simulated images were also compared with the ultrasound images of a diseased rat heart in vivo. A new segmental evaluation method is proposed to validate the simulation results. The average relative errors (AREs) of five parameters, i.e., mean intensity, Rayleigh distribution parameter σ, and first, second, and third quartiles, were utilized as the evaluation metrics. The simulated images were quantitatively compared with real ultrasound images in both ex vivo and in vivo experiments.The proposed ultrasound image simulation method can realistically simulate cardiac ultrasound images of the heart using high-resolution MR-DTI data. The AREs of their proposed method are 19% for the mean intensity, 17.7% for the scale parameter of Rayleigh distribution, 36.8% for the first quartile of the image intensities, 25.2% for the second quartile, and 19.9% for the third quartile. In contrast, the errors of the other two methods are generally five times more than those of their proposed method.The proposed simulation method uses MR-DTI data and realistically generates cardiac ultrasound images with anisotropic intensities inside the myocardium. The ultrasound simulation method could provide a tool for many potential research and clinical applications in cardiac ultrasound imaging.

    View details for DOI 10.1118/1.4927788

    View details for Web of Science ID 000360645000017

    View details for PubMedID 26328966

    View details for PubMedCentralID PMC4537486