My research group develops technologies for advanced x-ray and CT imaging, including artificial intelligence for CT acquisition, reconstruction, and image processing; spectral imaging, including photon counting CT (PCCT) and dual-layer flat-panel detectors; novel system and detector designs; and their applications in diagnostic imaging and image-guided procedures. I am also the Director of the Photon Counting CT Lab, Zeego Lab, and Tabletop X-Ray Lab.

I completed my PhD in Electrical Engineering at Stanford, developing strategies for maximizing the information content of dual energy CT and photon counting detectors. I then pursued a postdoctoral fellowship at Johns Hopkins in the I-STAR Lab, developing reconstruction and registration methods for x-ray based image-guided surgery. I was then a Senior Scientist at Varian Medical Systems, developing x-ray/CT methods for image-guided radiation therapy, before returning to Stanford in 2018, where I now lead a comprehensive research program in advanced x-ray and CT imaging systems and methods, with funding from NIH, DOD, DOE, and industry partners.

Academic Appointments

Honors & Awards

  • Council of Early Career Investigators in Imaging, Academy for Radiology & Biomedical Imaging Research (2020)
  • Early Career Investigator in Imaging Travel Award, AAPM (2020)
  • Featured Cover Article, Medical Physics journal (2018)
  • Best in Physics (Imaging) abstract, American Association of Physicists in Medicine (AAPM) Annual Meeting (2016)
  • Featured Cover Article, Medical Physics journal (2015)
  • Jack Fowler Junior Investigator, winner, AAPM Annual Meeting (2014)
  • AAPM Research Seed Grant, American Association of Physicists in Medicine (2013)
  • NRSA Postdoctoral Fellowship, NIH (2013)
  • Graduate Community Award, Stanford Asian American Activities Center (2011)
  • Skilling Award for Outstanding Teaching Assistant in Electrical Engineering, Stanford University (2011)

Boards, Advisory Committees, Professional Organizations

  • Member, American Association of Physicists in Medicine (AAPM) (2013 - Present)

Professional Education

  • Postdoctoral Fellow, Johns Hopkins University, Biomedical Engineering (2014)
  • PhD, Stanford University, Electrical Engineering (2012)
  • MS, Stanford University, Electrical Engineering (2008)
  • BS, University of Texas at Austin, Electrical Engineering (2006)


  • Gerhard Kleinszig, Jeffrey Siewerdsen, Sebastian Vogt, Adam Wang. "United States Patent 10,022,098 Method and device for generating a low-dose X-ray image preview, imaging system and computer program product"
  • Adam Wang, Jeffrey Siewerdsen. "United States Patent 10,064,591 System, method and computer readable medium for preview of low-dose x-ray projection and tomographic images"
  • Josh Star-Lack, Adam Wang, Alexander Maslowski. "United States Patent 10,098,606 Automatic organ-dose-estimation for patient-specific computed tomography scans"
  • Alexander Maslowski, Adam Wang, Josh Star-Lack, Mingshan Sun, Todd Wareing. "United States Patent 10,327,727 Automatic estimating and reducing scattering in computed tomography scans"
  • Daniel Shedlock, Josh Star-Lack, Adam Wang. "United States Patent 10,330,798 Scintillating glass pixelated imager"
  • Jeffrey Siewerdsen, Yoshito Otake, Joseph Webster Stayman, Ali Uneri, Adam Wang, Sarah Ouadah. "United States Patent 10,478,148 Self-calibrating projection geometry for volumetric image reconstruction"
  • Pavlo Baturin, Adam Wang, Liangjia Zhu. "United States Patent 10,739,473 Image fusion in multi-layer flat panel imager"
  • Blake Gaderlund, Josh Star-Lack, John Van Heteren, Adam Wang. "United States Patent 10,960,232 Single-pass imaging and radiation treatment delivery via an extended rotation gantry"
  • John Van Heteren, Petr Jordan, Adam Wang, Josh Star-Lack. "United States Patent 10,967,202 Adaptive image filtering for volume reconstruction using partial image data"
  • Daniel Shedlock, Josh Star-Lack, Adam Wang. "United States Patent 11,079,499 Scintillating glass pixelated imager"
  • Pascal Paysan, Marcus Brehm, Adam Wang, Dieter Seghers, Josh Star-Lack. "United States Patent 11,173,324 Iterative image reconstruction in image-guided radiation therapy"
  • Pavlo Baturin, Adam Wang, Liangjia Zhu. "United States Patent 11,340,358 Image fusion in multi-layer flat panel imager"
  • Adam Wang, Norbert Pelc. "United States Patent 8,194,820 Optimal weights for measuring spectral x-ray data"

2023-24 Courses

Stanford Advisees

All Publications

  • Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS). Medical physics Deng, Y., Zhou, H., Wang, Z., Wang, A. S., Gao, H. 2024


    Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections.To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method.Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values ( E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, the E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively.We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

    View details for DOI 10.1002/mp.17022

    View details for PubMedID 38477717

  • Synthesizing High-Resolution Dual-Energy Radiographs from Coronary Artery Calcium CT Images Shaker, K., Shi, L., Hsieh, S., Swaby, A., Abbaszadeh, S., Wang, A. S., Fahrig, R., Sabol, J. M., Li, K. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3006250

    View details for Web of Science ID 001223517100117

  • Printing anthropomorphic multi-energy CT phantoms for spectral imaging with office laser printers SPIE Medical Imaging Yang, Y., Kim, N., Bennett, R., Wang, A. S. 2024

    View details for DOI 10.1117/12.3006581

  • Denoising X-Ray Images with Deep Learning: Impact of Spatially Correlated Noise Ku, A., Wang, S., Wang, A., Fahrig, R., Sabol, J. M., Li, K. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3006556

    View details for Web of Science ID 001223517100027

  • Optimal Weighting Strategies for Maximizing Contrast-to-Noise Ratio in Photon Counting CT Images Yang, Y., Wang, S., Stevens, G., Fan, J., Wang, A. S., Fahrig, R., Sabol, J. M., Li, K. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3006847

    View details for Web of Science ID 001223517100001

  • Personalized, Scout-Based Dose Estimation for Prospective Optimization of CT Tube Current Modulation Medrano, M., Wang, S., Imran, A., Stevens, G., Tse, J., Wang, A., Fahrig, R., Sabol, J. M., Li, K. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3006268

    View details for Web of Science ID 001223517100043

  • Retrospective Tube Current Modulation Optimization of Individualized Organ-Level CT Dose and Image Quality Wang, S., Medrano, M., Imran, A., Stevens, G., Tse, J., Wang, A. S., Fahrig, R., Sabol, J. M., Li, K. SPIE-INT SOC OPTICAL ENGINEERING. 2024

    View details for DOI 10.1117/12.3006870

    View details for Web of Science ID 001223517100036

  • Single-shot quantitative x-ray imaging using a primary modulator and dual-layer detector. Medical physics Shi, L., Bennett, N. R., Vezeridis, A., Kothary, N., Wang, A. S. 2023


    Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual-energy (DE) imaging, with its capability to quantify area density of specific materials, is well-suited to address such limitations, but only if the dual-energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter.In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free DE imaging with scatter correction in a single exposure.The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. The concept was first demonstrated using simulation of chest x-ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0-22.5 cm; copper: 0-0.9 mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast.Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13 cm for acrylic and 0.04 mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material-specific images by 38%-92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background.We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real-time image guidance and cone-beam CT).

    View details for DOI 10.1002/mp.16789

    View details for PubMedID 37843975

  • X-Ray Imaging in the Simulated Microgravity Environment of Parabolic Flight AEROSPACE MEDICINE AND HUMAN PERFORMANCE Lerner, D., Pohlen, M., Wang, A., Walter, J., Cairnie, M., Gifford, S. 2023; 94 (10): 786-791


    INTRODUCTION: The advancement of human spaceflight has made urgent the need to develop medical imaging technology to ensure a high level of in-flight care. To date, only ultrasound has been used in spaceflight. Radiography has multiple advantages over ultrasound, including lower operator dependence, more rapid acquisition, typically higher spatial resolution, and characterization of tissue with acoustic impedance precluding ultrasound. This proof-of-concept work demonstrates for the first time the feasibility of performing human radiographs in microgravity.METHODS: Radiographs of a phantom and human subject's hand, knee, chest, cervical spine, and pelvis were obtained aboard a parabolic flight in microgravity and simulated lunar gravity with various subject and operator positions. Control radiographs were acquired with the same system on the ground. These radiographs were performed with a Food and Drug Administration-approved ultra-portable, wireless, battery-powered, digital x-ray system.RESULTS: The radiographs of the phantom acquired in reduced gravity were qualitatively and quantitatively compared to the ground controls and found to exhibit similar diagnostic adequacy. There was no statistically significant difference in contrast resolution or spatial resolution with a spatial resolution across all imaging environments up to the Nyquist frequency of 3.6 line-pairs/mm and an average contrast-to-noise ratio of 2.44.DISCUSSION: As mass, power, and volume limitations lessen over the coming decades and the miniaturization of imaging equipment continues, in-flight implementation of nonsonographic modalities will become practical. Given the demonstrated ease of use and satisfactory image quality, portable radiography is ready to be the new frontier of space medical imaging.Lerner D, Pohlen M, Wang A, Walter J, Cairnie M, Gifford S. X-ray imaging in the simulated microgravity environment of parabolic flight. Aerosp Med Hum Perform. 2023; 94(10):786-791.

    View details for DOI 10.3357/AMHP.6286.2023

    View details for Web of Science ID 001087544200007

    View details for PubMedID 37726905

  • Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence. Abdominal radiology (New York) Jeong, J., Wentland, A., Mastrodicasa, D., Fananapazir, G., Wang, A., Banerjee, I., Patel, B. N. 2023


    To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial intelligence-based image translation.In this retrospective study, 734 patients (389F; 62.8 years ± 14.9) who underwent enhanced DECT of the chest, abdomen, and pelvis between January 2018 and June 2019 were included. Using 70-keV as the input images (n = 141,009) and 50-keV, iodine, and virtual unenhanced (VUE) images as outputs, separate models were trained using Pix2PixHD and CycleGAN. Model performance on the test set (n = 17,839) was evaluated using mean squared error, structural similarity index, and peak signal-to-noise ratio. To objectively test the utility of these models, synthetic iodine material density and 50-keV images were generated from SECT images of 16 patients with gastrointestinal bleeding performed at another institution. The conspicuity of gastrointestinal bleeding using sDECT was compared to portal venous phase SECT. Synthetic VUE images were generated from 37 patients who underwent a CT urogram at another institution and model performance was compared to true unenhanced images.sDECT from both Pix2PixHD and CycleGAN were qualitatively indistinguishable from true DECT by a board-certified radiologist (avg accuracy 64.5%). Pix2PixHD had better quantitative performance compared to CycleGAN (e.g., structural similarity index for iodine: 87% vs. 46%, p-value < 0.001). sDECT using Pix2PixHD showed increased bleeding conspicuity for gastrointestinal bleeding and better removal of iodine on synthetic VUE compared to CycleGAN.sDECT from SECT using Pix2PixHD may afford some of the advantages of DECT.

    View details for DOI 10.1007/s00261-023-04004-x

    View details for PubMedID 37665385

  • Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors. Medical physics Yang, Y., Wang, S., Pal, D., Yin, Z., Pelc, N. J., Wang, A. S. 2023


    BACKGROUND: Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store.PURPOSE: This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors.METHODS: We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramer-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area density rho A , m ${\rho }_{A,m}$ (0-40g/cm2 water, 0-2.16g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively.RESULTS: The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively.CONCLUSIONS: We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

    View details for DOI 10.1002/mp.16590

    View details for PubMedID 37401203

  • Task-specific self-supervision for CT image denoising COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION Haque, A., Wang, A., Imran, A. 2023
  • High resolution imaging with focused kV x-rays for small animal radio-neuromodulation. Medical physics Shi, L., Bennett, N. R., Nguyen, E., MacDonald, C., Wang, A., Liu, W. 2023


    High precision radiotherapy with small irradiator size has potential in many treatment applications involving small shallow targets, with small animal radio-neuromodulation as an intriguing example. A focused kV technique based on novel usage of polycapillary x-ray lenses can focus x-ray beams to <0.2 mm in diameter, which is ideal for such uses.Such an application also requires high resolution CT images for treatment planning and setup. In this work, we demonstrate the feasibility of using a virtual focal spot generated with an x-ray lens to perform high-resolution CBCT acquisition.The experiment with x-ray lens was set up on an x-ray tabletop system to generate a virtual focal spot. The flood field images with and without the x-ray lens were first compared. A pinhole image was acquired for the virtual focal spot and compared with the one acquired with the conventional focal spot without the lens. The planar imaging resolution with and without the lens were evaluated using a line pair resolution phantom. The spatial resolution of the two settings were estimated by reconstructing a 0.15-mm wire phantom and comparing its full width half maximum (FWHM). A CBCT scan of a rodent head was also acquired to further demonstrate the improved resolution using the x-ray lens.The proposed imaging setup with x-ray lens had a limited exposure area of 5 cm by 5 cm on the detector, which was suitable for guiding radio-neuromodulation to a small target in rodent brain. Compared to conventional imaging acquisition with a measured x-ray focal spot of 0.395 mm FWHM, the virtual focal spot size was measured at 0.175 mm. The reduction in focal spot size with lens leads to an almost doubled planar imaging resolution and a 26% enhancement in 3D spatial resolution. A realistic CBCT acquisition of a rodent head mimicked the imaging acquisition step for radio-neuromodulation and further showed the improved visualization for fine structures.This work demonstrated that the focused kV x-ray technique was capable of generating small focal spot size of <0.2 mm, which substantially improved x-ray imaging resolution for small animal imaging.

    View details for DOI 10.1002/mp.16413

    View details for PubMedID 37060293

  • Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology Koetzier, L. R., Mastrodicasa, D., Szczykutowicz, T. P., van der Werf, N. R., Wang, A. S., Sandfort, V., van der Molen, A. J., Fleischmann, D., Willemink, M. J. 2023: 221257


    Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

    View details for DOI 10.1148/radiol.221257

    View details for PubMedID 36719287

  • Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage. Journal of the Royal Society, Interface Hall, M. E., Wang, A. S., Gold, G. E., Levenston, M. E. 2022; 19 (193): 20220403


    The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.

    View details for DOI 10.1098/rsif.2022.0403

    View details for PubMedID 35919981

  • Technical note: Evaluation of a V-Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability and application to patient-specific CT dosimetry. Medical physics Adamson, P. M., Bhattbhatt, V., Principi, S., Beriwal, S., Strain, L. S., Offe, M., Wang, A. S., Vo, N., Schmidt, T. G., Jordan, P. 2022


    PURPOSE: This study developed and evaluated a Fully Convolutional Network (FCN) for pediatric CT organ segmentation, and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation.METHODS: A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation.RESULTS: Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice Similarity Coefficient (DSC) and Mean Surface Distance results are presented for 19 organ structures, for example median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%.CONCLUSIONS: Overall these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT doseestimation. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15521

    View details for PubMedID 35128672

  • Science and practice of imaging physics through 50 years of SPIE Medical Imaging conferences. Journal of medical imaging (Bellingham, Wash.) Wang, A., Cunningham, I., Danielsson, M., Fahrig, R., Flohr, T., Hoeschen, C., Noo, F., Sabol, J. M., Siewerdsen, J. H., Tingberg, A., Yorkston, J., Zhao, W., Samei, E. 2022; 9 (Suppl 1): 012205


    Purpose: For 50 years now, SPIE Medical Imaging (MI) conferences have been the premier forum for disseminating and sharing new ideas, technologies, and concepts on the physics of MI. Approach: Our overarching objective is to demonstrate and highlight the major trajectories of imaging physics and how they are informed by the community and science present and presented at SPIE MI conferences from its inception to now. Results: These contributions range from the development of image science, image quality metrology, and image reconstruction to digital x-ray detectors that have revolutionized MI modalities including radiography, mammography, fluoroscopy, tomosynthesis, and computed tomography (CT). Recent advances in detector technology such as photon-counting detectors continue to enable new capabilities in MI. Conclusion: As we celebrate the past 50 years, we are also excited about what the next 50 years of SPIE MI will bring to the physics of MI.

    View details for DOI 10.1117/1.JMI.9.S1.012205

    View details for PubMedID 35309720

  • Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours. Medical physics Jordan, P., Adamson, P. M., Bhattbhatt, V., Beriwal, S., Shen, S., Radermecker, O., Bose, S., Strain, L. S., Offe, M., Fraley, D., Principi, S., Ye, D. H., Wang, A. S., Van Heteren, J., Vo, N., Schmidt, T. G. 1800


    PURPOSE: Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging.ACQUISITION AND VALIDATION METHODS: The dataset collection consists of axial CT images in DICOM format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of seven years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise.DATA FORMAT AND USAGE NOTES: The data are available on TCIA ( under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contours are names as listed in Table 2.POTENTIAL APPLICATIONS: This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15485

    View details for PubMedID 35067940

  • Structural analysis of biomass pyrolysis and oxidation using in-situ X-ray computed tomography COMBUSTION AND FLAME Boigne, E., Bennett, N., Wang, A., Ihme, M. 2022; 235
  • Noise2Quality: Non-reference, pixel-wise assessment of low dose CT image quality SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment Haque, A., Wang, A., Imran, A. 2022

    View details for DOI 10.1117/12.2611254

  • Fast kV Switching for Improved Material Decomposition with Photon Counting X-ray Detectors Wang, S., Yang, Y., Pal, D., Pelc, N. J., Wang, A. S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611601

    View details for Web of Science ID 000836294000014

  • Empirical Optimization of Energy Bin Weights for Compressing Measurements with Photon Counting X-ray Detectors Yang, Y., Wang, S., Pal, D., Pelc, N. J., Wang, A. S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611555

    View details for Web of Science ID 000836294000013

  • Multimodal Contrastive Learning for Prospective Personalized Estimation of CT Organ Dose Imran, A., Wang, S., Pal, D., Dutta, S., Zucker, E., Wang, A., Wang, L., Dou, Q., Fletcher, P. T., Speidel, S., Li, S. SPRINGER INTERNATIONAL PUBLISHING AG. 2022: 634-643
  • Dual-layer flat panel detector with a-Se top layer for opportunistic screening of coronary artery calcium: a simulation study Swaby, A., Wang, A. S., Willemink, M. J., Abbaszadeh, S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611831

    View details for Web of Science ID 000836294000145

  • Personalized CT organ noise estimation from scout images SPIE Medical Imaging: Physics of Medical Imaging Imran, A., Pal, D., Wang, S., Dutta, S., Zucker, E., Wang, A. 2022

    View details for DOI 10.1117/12.2610986

  • Design of a digital, motion-free mechanism for fluence field modulation Hsieh, S. S., Leng, S., Yu, L., McCollough, C. H., Wang, A. S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611559

    View details for Web of Science ID 000836294000023

  • Single-Shot Quantitative X-ray Imaging Using a Primary Modulator and Dual-Layer Detector: Simulation and Phantom Studies Shi, L., Bennett, N., Wang, A. S., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611591

    View details for Web of Science ID 000836294000005

  • Quantitative X-ray computed tomography: Prospects for detailed in-situ imaging in bench-scale fire measurements FIRE SAFETY JOURNAL Boigne, E., Bennett, N., Wang, A., Ihme, M. 2021; 126
  • Deep learning-based reconstruction of interventional tools and devices from four X-ray projections for tomographic interventional guidance. Medical physics Eulig, E., Maier, J., Knaup, M., Bennett, N. R., Horndler, K., Wang, A. S., KachelrieSS, M. 2021


    PURPOSE: Image guidance for minimally invasive interventions is usually performed by acquiring fluoroscopic images using a monoplanar or a biplanar C-arm system. However, the projective data provide only limited information about the spatial structure and position of interventional tools and devices such as stents, guide wires or coils. In this work we propose a deep learning-based pipeline for real-time tomographic (four-dimensional) interventional guidance at conventional doselevels.METHODS: Our pipeline is comprised of two steps. In the first one, interventional tools are extracted from four cone-beam CT projections using a deep convolutional neural network. These projections are then Feldkamp reconstructed and fed into a second network, which is trained to segment the interventional tools and devices in this highly undersampled reconstruction. Both networks are trained using simulated CT data and evaluated on both simulated data and C-arm cone-beam CT measurements of stents, coils and guidewires RESULTS: The pipeline is capable of reconstructing interventional tools from only four x-ray projections without the need for a patient prior. At an isotropic voxel size of 100 m our methods achieves a precision/recall within a 100 m environment of the ground truth of 93 %/98 %, 90 %/71 %, and 93 %/76 % for guide wires, stents and coils,respectively.CONCLUSIONS: A deep learning-based approach for four-dimensional interventional guidance is able to overcome the drawbacks of today's interventional guidance by providing full spatiotemporal (4D) information about the interventional tools at dose levels comparable to conventionalfluoroscopy.

    View details for DOI 10.1002/mp.15160

    View details for PubMedID 34387362

  • Single-pass metal artifact reduction using a dual-layer flat panel detector. Medical physics Shi, L., Bennett, N. R., Shiroma, A., Sun, M., Zhang, J., Colbeth, R., Star-Lack, J., Lu, M., Wang, A. S. 2021


    PURPOSE: Metal artifact remains a challenge in cone-beam CT images. Many image domain-based segmentation methods have been proposed for metal artifact reduction (MAR), which require two-pass reconstruction. Such methods first segment metal from a first-pass reconstruction and then forward-project the metal mask to identify them in projections. These methods work well in general but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Single-pass methods that detect metal in single-energy projections have also been developed, but often have imperfect metal detection that leads to residual artifacts. In this work, we develop a MAR method using a dual-layer (DL) flat panel detector, which improves performance for single-pass reconstruction.METHODS: In this work, we directly detect metal objects in projections using dual-energy (DE) imaging that generates material-specific images (e.g., soft tissue and bone), where the metal stands out in bone images when nonuniform soft tissue background is removed. Metal is detected via simple thresholding, and entropy filtration is further applied to remove false-positive detections. A DL detector provides DE images with superior temporal and spatial registration and was used to perform the task. Scatter correction was first performed on DE raw projections to improve the accuracy of material decomposition. One phantom mimicking a liver biopsy setup and a cadaver head were used to evaluate the metal reduction performance of the proposed method and compared with that of a standard two-pass reconstruction, a previously published sinogram-based method using a Markov random field (MRF) model, and a single-pass projection-domain method using single-energy imaging. The phantom has a liver steering setup placed in a hollow chest phantom, with embedded metal and a biopsy needle crossing the phantom boundary. The cadaver head has dental fillings and a metal tag attached to its surface. The identified metal regions in each projection were corrected by interpolation using surrounding pixels, and the images were reconstructed using filtered backprojection.RESULTS: Our current approach removes metal from the projections, which is robust to FOV truncation during imaging acquisition. In case of FOV truncation, the method outperformed the two-pass reconstruction method. The proposed method using DE renders better accuracy in metal segmentation than the MRF method and single-energy method, which were prone to false-positive errors that cause additional streaks. For the liver steering phantom, the average spatial nonuniformity was reduced from 0.127 in uncorrected images to 0.086 using a standard two-pass reconstruction and to 0.077 using the proposed method. For the cadaver head, the average standard deviation within selected soft tissue regions ( sigma s ) was reduced from 209.1 HU in uncorrected images to 69.1 HU using a standard two-pass reconstruction and to 46.8 HU using our proposed method. The proposed method reduced the processing time by 31% as compared with the two-pass method.CONCLUSIONS: We proposed a MAR method that directly detects metal in the projection domain using DE imaging, which is robust to truncation and superior to that of single-energy imaging. The method requires only a single-pass reconstruction that substantially reduces processing time compared with the standard two-pass metal reduction method.

    View details for DOI 10.1002/mp.15131

    View details for PubMedID 34374461

  • Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment. IEEE transactions on radiation and plasma medical sciences Wang, A. S., Pelc, N. J. 2021; 5 (4): 453-464


    Photon counting x-ray detectors (PCDs) with spectral capabilities have the potential to revolutionize computed tomography (CT) for medical imaging. The ideal PCD provides accurate energy information for each incident x-ray, and at high spatial resolution. This information enables material-specific imaging, enhanced radiation dose efficiency, and improved spatial resolution in CT images. In practice, PCDs are affected by non-idealities, including limited energy resolution, pulse pileup, and cross talk due to charge sharing, K-fluorescence, and Compton scattering. In order to maximize their performance, PCDs must be carefully designed to reduce these effects and then later account for them during correction and post-acquisition steps. This review article examines algorithms for using PCDs in spectral CT applications, including how non-idealities impact image quality. Performance assessment metrics that account for spatial resolution and noise such as the detective quantum efficiency (DQE) can be used to compare different PCD designs, as well as compare PCDs with conventional energy integrating detectors (EIDs). These methods play an important role in enhancing spectral CT images and assessing the overall performance of PCDs.

    View details for DOI 10.1109/trpms.2020.3007380

    View details for PubMedID 35419500

    View details for PubMedCentralID PMC9000208

  • Impact of Upstream Medical Image Processing on Downstream Performance of a Head CT Triage Neural Network. Radiology. Artificial intelligence Hooper, S. M., Dunnmon, J. A., Lungren, M. P., Mastrodicasa, D., Rubin, D. L., Re, C., Wang, A., Patel, B. N. 2021; 3 (4): e200229


    Purpose: To develop a convolutional neural network (CNN) to triage head CT (HCT) studies and investigate the effect of upstream medical image processing on the CNN's performance.Materials and Methods: A total of 9776 HCT studies were retrospectively collected from 2001 through 2014, and a CNN was trained to triage them as normal or abnormal. CNN performance was evaluated on a held-out test set, assessing triage performance and sensitivity to 20 disorders to assess differential model performance, with 7856 CT studies in the training set, 936 in the validation set, and 984 in the test set. This CNN was used to understand how the upstream imaging chain affects CNN performance by evaluating performance after altering three variables: image acquisition by reducing the number of x-ray projections, image reconstruction by inputting sinogram data into the CNN, and image preprocessing. To evaluate performance, the DeLong test was used to assess differences in the area under the receiver operating characteristic curve (AUROC), and the McNemar test was used to compare sensitivities.Results: The CNN achieved a mean AUROC of 0.84 (95% CI: 0.83, 0.84) in discriminating normal and abnormal HCT studies. The number of x-ray projections could be reduced by 16 times and the raw sensor data could be input into the CNN with no statistically significant difference in classification performance. Additionally, CT windowing consistently improved CNN performance, increasing the mean triage AUROC by 0.07 points.Conclusion: A CNN was developed to triage HCT studies, which may help streamline image evaluation, and the means by which upstream image acquisition, reconstruction, and preprocessing affect downstream CNN performance was investigated, bringing focus to this important part of the imaging chain.Keywords Head CT, Automated Triage, Deep Learning, Sinogram, DatasetSupplemental material is available for this article.©RSNA, 2021.

    View details for DOI 10.1148/ryai.2021200229

    View details for PubMedID 34350412

  • An analysis of scatter characteristics in x-ray CT spectral correction. Physics in medicine and biology Zhang, T., Chen, Z., Zhou, H., Bennett, N. R., Wang, A. S., Gao, H. 2021


    X-ray scatter remains a major physics challenge in volumetric computed tomography (CT), whose physical and statistical behaviors have been commonly leveraged in order to eliminate its impact on CT image quality. In this work, we conduct an in-depth derivation of how the scatter distribution and scatter to primary ratio (SPR) will change during the spectral correction, leading to an interesting finding on the property of scatter. Such a characterization of scatter's behavior provides an analytic approach of compensating for the SPR as well as approximating the change of scatter distribution after spectral correction, even though both of them might be significantly distorted as the linearization mapping function in spectral correction could vary a lot from one detector pixel to another. We conduct an evaluation of SPR compensations on a Catphan phantom and an anthropomorphic chest phantom to validate the characteristics of scatter. In addition, this scatter property is also directly adopted into CT imaging using a spectral modulator with flying focal spot technology (SMFFS) as an example to demonstrate its potential in practical applications. For cone-beam CT scans at both 80 and 120 kVp, CT images with accurate CT numbers can be achieved after spectral correction followed by the appropriate SPR compensation based on our presented scatter property. In the case of the SMFFS based cone-beam CT scan of the Catphan phantom at 120 kVp, after a scatter correction using an analytic algorithm derived from the scatter property, CT image quality was significantly improved, with the averaged root mean square error reduced from 297.9 to 6.5 Hounsfield units (HU).

    View details for DOI 10.1088/1361-6560/abebab

    View details for PubMedID 33657536

  • Characterization of x-ray focal spots using a rotating edge. Journal of medical imaging (Bellingham, Wash.) Shi, L., Bennett, N. R., Wang, A. S. 2021; 8 (2): 023502


    Purpose: The focal spot size and shape of an x-ray system are critical factors to the spatial resolution. Conventional approaches to characterizing the focal spot use specialized tools that usually require careful calibration. We propose an alternative to characterize the x-ray source's focal spot, simply using a rotating edge and flat-panel detector. Methods: An edge is moved to the beam axis, and an edge spread function (ESF) is obtained at a specific angle. Taking the derivative of the ESF provides the line spread function, which is the Radon transform of the focal spot in the direction parallel to the edge. By rotating the edge about the beam axis for 360 deg, we obtain a complete Radon transform, which is used for reconstructing the focal spot. We conducted a study on a clinical C-arm system with three focal spot sizes (0.3, 0.6, and 1.0 mm nominal size), then compared the focal spot imaged using the proposed method against the conventional pinhole approach. The full width at half maximum (FWHM) of the focal spots along the width and height of the focal spot were used for quantitative comparisons. Results: Using the pinhole method as ground truth, the proposed method accurately characterized the focal spot shapes and sizes. Quantitatively, the FWHM widths were 0.37, 0.65, and 1.14 mm for the pinhole method and 0.33, 0.60, and 1.15 mm for the proposed method for the 0.3, 0.6, and 1.0 mm nominal focal spots, respectively. Similar levels of agreement were found for the FWHM heights. Conclusions: The method uses a rotating edge to characterize the focal spot and could be automated in the future using a system's built-in collimator. The method could be included as part of quality assurance tests of image quality and tube health.

    View details for DOI 10.1117/1.JMI.8.2.023502

    View details for PubMedID 34368391

    View details for PubMedCentralID PMC8330836

  • Densely Sampled Spectral Modulation for X-Ray CT Using a Stationary Modulator with Flying Focal Spot: A Conceptual and Feasibility Study. Medical physics Gao, H., Zhang, T., Bennett, N. R., Wang, A. 2021


    PURPOSE: Modulation of the X-ray source in computed tomography (CT) by a designated filter to achieve a desired distribution of photon flux has been greatly advanced in recent years. In this work, we present a densely sampled spectral modulation (DSSM) as a promising low-cost solution to quantitative CT imaging in the presence of scatter. By leveraging a special stationary filter (namely a spectral modulator) and a flying focal spot, DSSM features a strong correlation in scatter distributions across focal spot positions and sees no substantial projection sparsity or misalignment in data sampling, making it possible to simultaneously correct for scatter and spectral effects in a unified framework.METHODS: The concept of DSSM is first introduced, and followed by an analysis of the design and benefits of using the stationary spectral modulator with flying focal spot (SMFFS) that dramatically changes the data sampling and its associated data processing. With an assumption that the scatter distributions across focal spot positions have strong correlation, a scatter estimation and spectral correction algorithm from DSSM is then developed, where a dual-energy modulator along with two flying focal spot positions is of interest. Finally, a phantom study on a tabletop cone-beam CT system is conducted to understand the feasibility of DSSM by SMFFS, using a copper modulator and by moving the X-ray tube position in the X direction to mimic the flying focal spot.RESULTS: Based on our analytical analysis of the DSSM by SMFFS, the misalignment of low- and high-energy projection rays can be reduced by a factor of more than 10 when compared with a stationary modulator only. With respect to modulator design, metal materials such as copper, molybdenum, silver and tin could be good candidates in terms of energy separation at a given attenuation of photon flux. Physical experiments using a Catphan phantom as well as an anthropomorphic chest phantom demonstrate the effectiveness of DSSM by SMFFS with much better CT number accuracy and less image artifacts. The root mean squared error was reduced from 297.9 to 6.5 Hounsfield units (HU) for the Catphan phantom, and from 409.3 to 39.2 HU for the chest phantom.CONCLUSIONS: The concept of densely sampled spectral modulation using a stationary modulator with flying focal spot is proposed. Phantom results on its scatter estimation and spectral correction performance validate our main ideas and key assumptions, demonstrating its potential and feasibility for quantitative CT imaging.

    View details for DOI 10.1002/mp.14704

    View details for PubMedID 33420741

  • Dual energy chest x-ray for improved COVID-19 detection using a dual-layer flat-panel detector: Simulation and phantom studies Shi, L., Bennett, N., Lu, M., Sun, M., Zhang, J., Star-Lack, J., Tsai, E. B., Guo, H., Wang, A. S., Bosmans, H., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2021

    View details for DOI 10.1117/12.2581317

    View details for Web of Science ID 000672731900069

  • Personalized CT Organ Dose Estimation from Scout Images International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Imran, A., Wang, S., Pal, D., Dutta, S., Patel, B., Zucker, E., Wang, A. 2021
  • SSIQA: Multi-task learning for non-reference CT image quality assessment with self-supervised noise level prediction 18th International Symposium on Biomedical Imaging (ISBI) Imran, A., Pal, D., Patel, B., Wang, A. IEEE. 2021: 1962–1965
  • MultiMix: Sparingly Supervised, Extreme Multitask Learning From Medical Images 18th International Symposium on Biomedical Imaging (ISBI) Haque, A., Imran, A., Wang, A., Terzopoulos, D. IEEE. 2021
  • Upstream Machine Learning in Radiology. Radiologic clinics of North America Sandino, C. M., Cole, E. K., Alkan, C., Chaudhari, A. S., Loening, A. M., Hyun, D., Dahl, J., Imran, A. A., Wang, A. S., Vasanawala, S. S. 2021; 59 (6): 967-985


    Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.

    View details for DOI 10.1016/j.rcl.2021.07.009

    View details for PubMedID 34689881

  • Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data Journal of Machine Learning for Biomedical Imaging Haque, A., Imran, A., Wang, A., Terzopoulos, D. 2021
  • Single-Shot Quantitative X-ray Imaging from Simultaneous Scatter and Dual Energy Measurements: A Simulation Study Wang, A. S., Bosmans, H., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2021

    View details for DOI 10.1117/12.2580728

    View details for Web of Science ID 000672731900068

  • Analytical model for pulse pileup in photon counting detectors with seminonparalyzable behavior Yang, Y., Pelc, N. J., Wang, A. S., Bosmans, H., Zhao, W., Yu, L. SPIE-INT SOC OPTICAL ENGINEERING. 2021

    View details for DOI 10.1117/12.2581145

    View details for Web of Science ID 000672731900060

  • Validation of a deterministic linear Boltzmann transport equation solver for rapid CT dose computation using physical dose measurements in pediatric phantoms. Medical physics Principi, S., Lu, Y., Liu, Y., Wang, A., Maslowski, A., Wareing, T., Van Heteren, J., Schmidt, T. G. 2021


    The risk of inducing cancer to patients undergoing CT examinations has motivated efforts for CT dose estimation, monitoring and reduction, especially among pediatric population. The method investigated in this study is Acuros CTD (Varian Medical Systems, Palo Alto, CA), a deterministic linear Boltzmann transport equation (LBTE) solver aimed at generating rapid and reliable dose maps of CT exams. By applying organ contours, organ doses can also be obtained, thus patient-specific organ dose estimates can be provided. This study experimentally validated Acuros against measurements performed on a clinical CT system using a range of physical pediatric anthropomorphic phantoms and acquisition protocols.The study consisted of: (1) the acquisition of dose measurements on a clinical CT scanner through thermoluminescent dosimetry (TLD) chips, and (2) the modeling in the Acuros platform of the measurement set up, which includes the modeling of the CT scanner and of the anthropomorphic phantoms. For the measurements, 1-year-old, 5-year-old, and 10-year-old anthropomorphic phantoms of the CIRS ATOM family were used. TLDs were placed in selected organ locations such as stomach, liver, lungs, and heart. The pediatric phantoms were scanned helically with the GE Discovery 750 HD clinical scanner for several examination protocols. For the simulations in Acuros, scanner-specific input, such as bowtie filters, overrange collimation and tube current modulation schemes, were modeled. These scanner complexities were implemented by defining discretized x-ray beams whose spectral distribution, defined in Acuros by only six energy bins, varied across fan angle, cone angle, and slice position. The images generated during the CT acquisitions were used to create the geometrical models, by applying thresholding algorithms and assigning materials to the HU values. The TLD chips were contoured in the phantom models as sensitive cylindrical volumes at the locations selected for dosimeters placement, to provide dose estimates, in terms of dose per unit photon. To compare measured doses with dose estimates, a calibration factor was derived from the CTDIvol displayed by the scanner, to account for the number of photons emitted by the x-ray tube during the procedure.The differences of the measured and estimated doses, in terms of absolute % errors, were within 13% for 153 TLD locations, with an error of 17% at the stomach for one study with the 10-year-old phantom. Root-mean-squared-errors (RMSE) across all TLD locations for all configurations were in the range of 3% - 8%, with Acuros providing dose estimates in a time range of a few seconds up to two minutes.An overall good agreement between measurements and simulations was achieved, with average RMSE of 6% across all cases. The results demonstrate that Acuros can model a specific clinical scanner despite the required discretization in spatial and energy domains. The proposed deterministic tool has the potential to be part of a near real-time individualized dosimetry monitoring system for CT applications, providing patient-specific organ dose estimates. This article is protected by copyright. All rights reserved.

    View details for DOI 10.1002/mp.15301

    View details for PubMedID 34669975

  • Abbreviated on-treatment CBCT using roughness penalized mono-energization of kV-MV data and a multi-layer MV imager. Physics in medicine and biology Jacobson, M. W., Lehmann, M. n., Huber, P. n., Wang, A. S., Myronakis, M. E., Shi, M. n., Ferguson, D. n., Valencia Lozano, I. n., Hu, Y. H., Baturin, P. n., Harris, T. C., Fueglistaller, R. n., Williams, C. n., Morf, D. n., Berbeco, R. n. 2021


    Simultaneous acquisition of cone beam CT (CBCT) projections using both the kV and MV imagers of an image guided radiotherapy (IGRT) system reduces set-up scan times -- a benefit to lung cancer radiation oncology patients -- but increases noise in the 3D reconstruction. In this article, we present a kV-MV scan time reduction technique that uses two noise-reducing measures to achieve superior performance. The first is a high DQE multi-layer MV imager prototype. The second is a beam hardening correction algorithm which combines poly-energetic modeling with edge-preserving, regularized smoothing of the projections. Performance was tested in real acquisitions of the Catphan 604 and a thorax phantom. Percent noise was quantified from voxel values in a soft tissue volume of interest (VOI) while edge blur was quantified from a VOI straddling a boundary between air and soft material. Comparisons in noise/resolution performance trade-off were made between our proposed approach, a dose-equivalent kV-only scan, and a kV-MV reconstruction technique previously published by Yin et al. (2005 Med. Phys. (32) 9). The proposed technique demonstrated lower noise as a function of spatial resolution than the baseline kV-MV method, notably a 50% noise reduction at typical edge blur levels. Our proposed method also exhibited fainter non-uniformity artifacts and in some cases superior contrast. Overall, we find that the combination of a multi-layer MV imager, acquiring at a LINAC source energy of 2.5 MV, and a denoised beam hardening correction algorithm enables noise, resolution, and dose performance comparable to standard kV-imager only set-up CBCT, but with nearly half the gantry rotation time.

    View details for DOI 10.1088/1361-6560/abddd2

    View details for PubMedID 33472189

  • Deterministic Boltzmann Transport Equation Solver for Patient-Specific CT Dose Estimation: Comparison Against a Monte Carlo Benchmark for Realistic Scanner Configurations and Patient Models. Medical physics Principi, S., Wang, A., Maslowski, A., Wareing, T., Jordan, P., Schmidt, T. G. 2020


    PURPOSE: Epidemiological evidence suggests an increased risk of cancer related to CT scans, with children exposed to greater risk. The purpose of this work is to test the reliability of a linear Boltzmann Transport Equation (LBTE) solver for rapid and patient-specific CT dose estimation. This includes building a flexible LBTE framework for modeling modern clinical CT scanners and to validate the resulting dose maps across a range of realistic scanner configurations and patient models.METHODS: In this study, computational tools were developed for modeling CT scanners, including a bowtie filter, overrange collimation, and tube current modulation. The LBTE solver requires discretization in the spatial, angular, and spectral dimensions, which may affect the accuracy of scanner modeling. To investigate these effects, this study evaluated the LBTE dose accuracy for different discretization parameters, scanner configurations, and patient models (male, female, adults, pediatric). The method used to validate the LBTE dose maps was the Monte Carlo code Geant4, which provided ground truth dose maps. LBTE simulations were implemented on a GeForce GTX 1080 graphic unit, while Geant4 was implemented on a distributed cluster of CPUs.RESULTS: The agreement between Geant4 and the LBTE solver quantifies the accuracy of the LBTE, which was similar across the different protocols and phantoms. The results suggest that 18 views per rotation provides sufficient accuracy, as no significant improvement in the accuracy was observed by increasing the number of projection views. Considering this discretization, the LBTE solver average simulation time was approximately 30 seconds. However, in the LBTE solver the phantom model was implemented with a lower voxel resolution with respect to Geant4, as it is limited by the memory of the GPU. Despite this discretization the results showed a good agreement between the LBTE and Geant4, with root mean square error of the deposited energy in organs of approximately 3.5% for most of the studied configurations.CONCLUSIONS: The LBTE solver is proposed as an alternative to Monte Carlo for patient-specific organ dose estimation. This study demonstrated accurate organ dose estimates for the rapid LBTE solver when considering realistic aspects of CT scanners and a range of phantom models. Future plans will combine the LBTE framework with deep-learning autosegmentation algorithms to provide near real-time patient-specific organ dose estimation.

    View details for DOI 10.1002/mp.14494

    View details for PubMedID 32981038

  • Characterization of Markerless Tumor Tracking Using the On-Board Imager of a Commercial Linear Accelerator Equipped With Fast-kV Switching Dual-Energy Imaging. Advances in radiation oncology Roeske, J. C., Mostafavi, H., Haytmyradov, M., Wang, A., Morf, D., Cortesi, L., Surucu, M., Patel, R., Cassetta, R., Zhu, L., Lehmann, M., Harkenrider, M. M. 2020; 5 (5): 1006–13


    Purpose: To describe and characterize fast-kV switching, dual-energy (DE) imaging implemented within the on-board imager of a commercial linear accelerator for markerless tumor tracking (MTT).Methods and Materials: Fast-kV switching, DE imaging provides for rapid switching between programmed tube voltages (ie, 60 and 120 kVp) from one image frame to the next. To characterize this system, the weighting factor used for logarithmic subtraction and signal difference-to-noise ratio were analyzed as a function of time and frame rate. MTT was evaluated using a thorax motion phantom and fast kV, DE imaging was compared versus single energy (SE) imaging over 360 degrees of rotation. A template-based matching algorithm was used to track target motion on both DE and SE sequences. Receiver operating characteristics were used to compare tracking results for both modalities.Results: The weighting factor was inversely related to frame rate and stable over time. After applying the frame rate-dependent weighting factor, the signal difference-to-noise ratio was consistent across all frame rates considered for simulated tumors ranging from 5 to 25 mm in diameter. An analysis of receiver operating characteristics curves showed improved tracking with DE versus SE imaging. The area under the curve for the 10-mm target ranged from 0.821 to 0.858 for SE imaging versus 0.968 to 0.974 for DE imaging. Moreover, the residual tracking errors for the same target size ranged from 2.02 to 2.18 mm versus 0.79 to 1.07 mm for SE and DE imaging, respectively.Conclusions: Fast-kV switching, DE imaging was implemented on the on-board imager of a commercial linear accelerator. DE imaging resulted in improved MTT accuracy over SE imaging. Such an approach may have application for MTT of patients with lung cancer receiving stereotactic body radiation therapy, particularly for small tumors where MTT with SE imaging may fail.

    View details for DOI 10.1016/j.adro.2020.01.008

    View details for PubMedID 33089019

  • Detective quantum efficiency of photon-counting CdTe and Si detectors for computed tomography: a simulation study. Journal of medical imaging (Bellingham, Wash.) Persson, M., Wang, A., Pelc, N. J. 2020; 7 (4): 043501


    Purpose: Developing photon-counting CT detectors requires understanding the impact of parameters, such as converter material, thickness, and pixel size. We apply a linear-systems framework, incorporating spatial and energy resolution, to study realistic silicon (Si) and cadmium telluride (CdTe) detectors at a low count rate. Approach: We compared CdTe detector designs with 0.5 * 0.5 mm 2 and 0.225 * 0.225 mm 2 pixels and Si detector designs with 0.5 * 0.5 mm 2 pixels of 30 and 60mm active thickness, with and without tungsten scatter blockers. Monte-Carlo simulations of photon transport were used together with Gaussian charge sharing models fitted to published data. Results: For detection in a 300-mm-thick object at 120kVp, the 0.5- and 0.225-mm pixel CdTe systems have 28% to 41% and 5% to 29% higher detective quantum efficiency (DQE), respectively, than the 60-mm Si system with tungsten, whereas the corresponding numbers for two-material decomposition are 2% lower to 11% higher DQE and 31% to 54% lower DQE compared to Si. We also show that combining these detectors with dual-spectrum acquisition is beneficial. Conclusions: In the low-count-rate regime, CdTe detector systems outperform the Si systems for detection tasks, whereas silicon outperforms one or both of the CdTe systems for material decomposition.

    View details for DOI 10.1117/1.JMI.7.4.043501

    View details for PubMedID 32715022

  • Characterization and Potential Applications of a Dual-Layer Flat-Panel Detector. Medical physics Shi, L., Lu, M., Bennett, N. R., Shapiro, E., Zhang, J., Colbeth, R., Star-Lack, J., Wang, A. S. 2020


    PURPOSE: Dual energy (DE) x-ray imaging has many clinical applications in radiography, fluoroscopy, and CT. This work characterizes a prototype dual layer (DL) flat panel detector (FPD) and investigates its DE imaging capabilities for applications in 2D radiography/fluoroscopy and quantitative 3D cone-beam CT. Unlike other DE methods like kV switching, a DL FPD obtains DE images from a single exposure, making it robust against patient and system motion.METHODS: The DL FPD consists of a top layer with a 200 m-thick CsI scintillator coupled to an amorphous silicon (aSi) FPD of 150 m pixel size and a bottom layer with a 550 m thick CsI scintillator coupled to an identical aSi FPD. The two layers are separated by a 1 mm Cu filter to increase spectral separation. Images (43*43 cm2 active area) can be read out in 2*2 binning mode (300 m pixels) at up to 15 frames per second. Detector performance was first characterized by measuring the MTF, NPS, and DQE for the top and bottom layers. For 2D applications, a qualitative study was conducted using an anthropomorphic thorax phantom containing a porcine heart with barium-filled coronary arteries (similar to iodine). Additionally, fluoroscopic lung tumor tracking was investigated by superimposing a moving tumor phantom on the thorax phantom. Tracking accuracies of single energy (SE) and DE fluoroscopy were compared against the ground truth motion of the tumor. For 3D quantitative imaging, a phantom containing water, iodine, and calcium inserts was used to evaluate overall DE material decomposition capabilities. Virtual monoenergetic (VM) images ranging from 40 to 100 keV were generated, and the optimal VM image energy which achieved the highest image uniformity and maximum contrast-to-noise ratio (CNR) was determined.RESULTS: The spatial resolution of the top layer was substantially higher than that of the bottom layer (top layer 50% MTF = 2.2 mm-1 , bottom layer = 1.2 mm-1 ). A substantial increase in NNPS and reduction in DQE was observed for the bottom layer mainly due to photon loss within the top layer and Cu filter. For 2D radiographic and fluoroscopic applications, the DL FPD was capable of generating high-quality material-specific images separating soft tissue from bone and barium. For lung tumor tracking, DE fluoroscopy yielded more accurate results than SE fluoroscopy, with an average reduction in the root-mean-square error (RMSE) of over 10*. For the DE CBCT studies, accurate basis material decompositions were obtained. The estimated material densities were 294.68 ± 17.41 and 92.14 ± 15.61 mg/ml for the 300 and 100 mg/ml calcium inserts respectively, and 8.93 ± 1.45, 4.72 ± 1.44, and 2.11 ± 1.32 mg/ml for the 10, 5, and 2 mg/ml iodine inserts respectively, with an average error of less than 5%. The optimal VM image energy was found to be 60 keV.CONCLUSIONS: We characterized a prototype DL FPD and demonstrated its ability to perform accurate single-exposure DE radiography/fluoroscopy and DE-CBCT. The merits of the dual layer detector approach include superior spatial and temporal registration between its constituent images, and less complicated acquisition sequences.

    View details for DOI 10.1002/mp.14211

    View details for PubMedID 32347561

  • Low-dose megavoltage cone-beam computed tomography using a novel multi-layer imager (MLI). Medical physics Myronakis, M., Huber, P., Lehmann, M., Fueglistaller, R., Jacobson, M., Hu, Y., Baturin, P., Wang, A., Shi, M., Harris, T., Morf, D., Berbeco, R. 2020


    PURPOSE: The feasibility of low-dose megavoltage cone-beam acquisition (MVCBCT) using a novel, high detective quantum efficiency (DQE) multi-layer imager (MLI) was investigated. The aim of this work was to reconstruct MVCBCT images using the MLI at different total dose levels, assess Hounsfield Unit (HU) accuracy, noise and CNR for low dose megavoltage cone-beam acquisition.METHODS: The MLI has four stacked layers; each layer contains a combination of copper filter/converter, gadolinium oxysulfide (GOS) scintillator and a-Si detector array. In total, 720 projections of a CATPHAN phantom were acquired over 360 degrees at 2.5MV, 6 MV and 6 MV FFF beam energies on a Varian TrueBeam LINAC. The dose per projection was 0.01 MU, 0.0167 MU and 0.05 MU for 2.5 MV, 6 MV and 6 MV FFF respectively. MVCBCT images were reconstructed with varying numbers of projections to provide a range of doses for evaluation. Hounsfield Unit (HU) uniformity, accuracy, noise and CNR were estimated. Improvements were quantified relative to the standard AS1200 single-layer imager.RESULTS: Average HU uniformity for the MLI reconstructions was within a range of 95% to 99% for all of the energies studied. Relative electron density estimation from HU values was within 0.4%± 1.8% from nominal values. The CNR for MVCBCT based on MLI projections was 2-4x greater than from AS1200 projections. The 2.5 MV beam acquisition with the MLI exhibited the lowest noise and the best balance between CNR and dose for low dose reconstructions.CONCLUSIONS: MVCBCT imaging with a novel MLI prototype mounted on a clinical linear accelerator was demonstrated the MLI provided substantial improvement over the standard AS1200 EPID. Further optimization of MVCBCT reconstruction, particularly for 2.5 MV acquisitions, will improve image metrics. Overall, the MLI improves CNR at substantially lower doses than currently required by conventional detectors. This new high DQE detector could provide high quality MVCBCT at clinically acceptable doses.

    View details for DOI 10.1002/mp.14017

    View details for PubMedID 31930516

  • Spectral modulator with flying focal spot for cone-beam CT: a feasibility study. SPIE Medical Imaging 2020: Physics of Medical Imaging Gao, H., Zhou, H., Zhu, L., Pelc, N., Bennett, R., Wang, A. 2020

    View details for DOI 10.1117/12.2548954

  • Comparative study of dual energy cone-beam CT using a dual-layer detector and kVp switching for material decomposition. SPIE Medical Imaging 2020: Physics of Medical Imaging Shi, L., Bennett, N. R., Shapiro, E., Colbeth, R. E., Star-Lack, J., Lu, M., Wang, A. S. 2020


    Cone-beam CT (CBCT) is widely used in diagnostic imaging and image-guided procedures, leading to an increasing need for advanced CBCT techniques, such as dual energy (DE) imaging. Previous studies have shown that DE-CBCT can perform quantitative material decomposition, including quantification of contrast agents, electron density, and virtual monoenergetic images. Currently, most CBCT systems perform DE imaging using a kVp switching technique. However, the disadvantages of this method are spatial and temporal misregistration as well as total scan time increase, leading to errors in the material decomposition. DE-CBCT with a dual layer flat panel detector potentially overcomes these limitations by acquiring the dual energy images simultaneously. In this work, we investigate the DE imaging performance of a prototype dual layer detector by evaluating its material decomposition capability and comparing its performance to that of the kVp switching method. Two sets of x-ray spectra were used for kVp switching: 80/120 kVp and 80/120 kVp + 1 mm Cu filtration. Our results show the dual layer detector outperforms kVp switching at 80/120 kVp with matched dose. The performance of kVp switching was better by adding 1 mm copper filtration to the high energy images (80/120 kVp + 1 mm Cu), though the dual layer detector still provided comparable performance for material decomposition tasks. Overall, both the dual layer detector and kVp switching methods provided quantitative material decomposition images in DE-CBCT, with the dual layer detector having additional potential advantages.

    View details for DOI 10.1117/12.2549781

    View details for PubMedCentralID PMC8268997

  • Simultaneous in-situ measurements of gas temperature and pyrolysis of biomass smoldering via X-ray computed tomography. Proceedings of the Combustion Institute Boigne, E., Bennett, N. R., Wang, A., Mohri, K., Ihme, M. 2020
  • Reconstruction of x-ray focal spot distribution using a rotating edge. SPIE Medical Imaging 2020: Physics of Medical Imaging Shi, L., Bennett, N. R., Wang, A. S. 2020


    The size and shape of an x-ray source's focal spot is a critical factor in the imaging system's overall spatial resolution. The conventional approach to imaging the focal spot uses a pinhole camera, but this requires careful, manual measurements. Instead, we propose a novel alternative, simply using the collimator available on many x-ray systems. After placing the edge of a collimator blade in the center of the beam, we can obtain an image of its edge spread function (ESF). Each ESF provides information about the focal spot distribution - specifically, the parallel projection of the focal spot in the direction parallel to the edge. If the edge is then rotated about the beam axis, each image provides a different parallel projection of the focal spot until a complete Radon transform of the focal spot distribution is obtained. The focal spot can then be reconstructed by the inverse Radon transform, or parallel-beam filtered backprojection. We conducted a study on a clinical C-arm system with 3 focal spot sizes (0.3, 0.6, 1.0 mm nominal size), comparing the focal spot obtained using the rotating edge method against the conventional pinhole approach. Our results demonstrate accurate characterization of the size and shape of the focal spot.

    View details for DOI 10.1117/12.2549740

    View details for PubMedCentralID PMC8292135

  • Deep learning-aided CBCT image reconstruction of interventional material from four x-ray projections. SPIE Medical Imaging 2020: Physics of Medical Imaging Eulig, E., Mier, J., Bennett, N. R., Knaup, M., Hörndler, K., Wang, A., Kachelrieß, M. 2020

    View details for DOI 10.1117/12.2548662

  • Evaluation of deep learning segmentation for rapid, patient-specific CT organ dose estimation using an LBTE solver. SPIE Medical Imaging 2020: Physics of Medical Imaging Offe, M., Fraley, D., Adamson, P. M., Principi, S., Wang, A. S., Jordan, P., Schmidt, T. G. 2020

    View details for DOI 10.1117/12.2550314

  • Projection-domain metal artifact correction using a dual layer detector. SPIE Medical Imaging 2020: Physics of Medical Imaging Shi, L., Bennett, N. R., Star-Lack, J., Lu, M., Wang, A. S. 2020


    Metal artifact remains a challenge in cone-beam CT images. Many two-pass metal artifact reduction methods have been proposed, which work fairly well, but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Furthermore, two-pass methods increase the total reconstruction time. In this study, a projection-based metal detection and correction method with a dual layer detector is investigated. The dual layer detector provides dual energy images with perfect temporal and spatial registration in each projection, which aid in the identification of metal. A simple phantom with metal wires (copper) and a needle (steel) is used to evaluate the projection-based metal artifact reduction method from a dual layer scan and compared with that of a single layer scan. Preliminary results showed enhanced ability to identify metal regions, leading to substantially reduced metal artifact in reconstructed images. In summary, an effective single-pass, projection-domain method using a dual layer detector has been demonstrated, and it is expected to be robust against truncation and motion.

    View details for DOI 10.1117/12.2547936

    View details for PubMedCentralID PMC8268992

  • Markerless tumor tracking using fast-kV switching dual-energy fluoroscopy on a benchtop system MEDICAL PHYSICS Haytmyradov, M., Mostafavi, H., Wang, A., Zhu, L., Surucu, M., Patel, R., Ganguly, A., Richmond, M., Cassetta, R., Harkenrider, M. M., Roeske, J. C. 2019; 46 (7): 3235–44

    View details for DOI 10.1002/mp.13573

    View details for Web of Science ID 000475671900028

  • A novel method for fast image simulation of flat panel detectors. Physics in medicine and biology Shi, M., Myronakis, M. E., Hu, Y., Jacobson, M. W., Lehmann, M., Fueglistaller, R., Huber, P., Baturin, P., Wang, A. S., Ferguson, D., Harris, T., Morf, D., Berbeco, R. I. 2019


    We have developed a novel method for fast image simulation of flat panel detectors, based on the photon energy deposition efficiency and the optical spread function (OSF). The proposed method, FastEPID, determines the photon detection using photon energy deposition and replaces particle transport within the detector with precalculated OSFs. The FastEPID results are validated against experimental measurement and conventional Monte Carlo simulation in terms of modulation transfer function (MTF), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast, and relative difference of pixel value, obtained with a slanted slit image, Las Vegas phantom, and anthropomorphic pelvis phantom. Excellent agreement is observed between simulation and measurement in all cases. Without degrading image quality, the FastEPID method is capable of reducing simulation time up to a factor of 150. Multiple applications, such as imager design optimization for planar and volumetric imaging, are expected to benefit from the implementation of the FastEPID method.

    View details for DOI 10.1088/1361-6560/ab12aa

    View details for PubMedID 30901759

  • Characterizing a novel scintillating glass for application to megavoltage cone-beam computed tomography MEDICAL PHYSICS Hu, Y., Shedlock, D., Wang, A., Rottmann, J., Baturin, P., Myronakis, M., Huber, P., Fueglistaller, R., Shi, M., Morf, D., Star-Lack, J., Berbeco, R. I. 2019; 46 (3): 1323–30

    View details for DOI 10.1002/mp.13355

    View details for Web of Science ID 000461095300020

  • Fast shading correction for cone-beam CT via partitioned tissue classification. Physics in medicine and biology Shi, L., Wang, A. S., Wei, J., Zhu, L. 2019


    The quantitative use of cone beam computed tomography (CBCT) in radiation therapy is limited by severe shading artifacts, even with system embedded correction. We recently proposed effective shading correction methods, using planning CT (pCT) as prior information to estimate low-frequency errors in either the projection domain or the image domain. In this work, we further improve the clinical practicality of our previous methods by removing the requirement of prior pCT images. Clinical CBCT images are typically composed of a limited number of tissue types. By utilizing the low-frequency characteristic of shading distribution, we first generate a "shading-free" template image by enforcing uniformity on CBCT voxels of the same tissue type via a technique named partitioned tissue classification. Only a small subset of voxels on the template image is used to generate sparse samples of shading errors. Local filtration, a Fourier transform based algorithm, is employed to efficiently process the sparse errors to compute a full-field distribution of shading errors for CBCT correction. We evaluate the method performance on an anthropomorphic pelvis phantom and 6 pelvis patients. The proposed method improves the image quality of CBCT on both phantom and patients to a level matching that of pCT. On phantom, the signal non-uniformity (SNU) is reduced from 12.11 to 3.11% and 8.40 to 2.21% on fat and muscle, respectively. The maximum CT number error is reduced from 70 to 10 HU and 73 to 11 HU on fat and muscle, respectively. On patients, the average SNU is reduced from 9.22% to 1.06% and 11.41% to 1.67% on fat and muscle, respectively. The maximum CT number error is reduced from 95 to 9 HU and 88 to 8 HU on fat and muscle, respectively. The typical processing time for one CBCT dataset is about 45 seconds on a standard PC. .

    View details for PubMedID 30721886

  • A fast, linear Boltzmann transport equationsolver for computed tomography dose calculation (Acuros CTD) MEDICAL PHYSICS Wang, A., Maslowski, A., Wareing, T., Star-Lack, J., Schmidt, T. 2019; 46 (2): 925–33

    View details for DOI 10.1002/mp.13305

    View details for Web of Science ID 000459616200048

  • Fast-switching dual energy cone beam computed tomography using the on-board imager of a commercial linear accelerator. Physics in medicine and biology Cassetta, F. R., Lehmann, M. n., Haytmyradov, M. n., Patel, R. n., Wang, A. S., Cortesi, L. n., Morf, D. n., Seghers, D. n., Surucu, M. n., Mostafavi, H. n., Roeske, J. C. 2019


    To evaluate fast-kV switching (FS) dual energy (DE) cone beam computed tomography (CBCT) using the on-board imager (OBI) of a commercial linear accelerator to produce virtual monoenergetic (VM) and relative electron density (RED) images.Using an analytical model, CBCT phantom projections obtained at 80 and 140 kVp with FS imaging, were decomposed into equivalent thicknesses of Al and PMMA. All projections were obtained with the titanium foil and bowtie filter in place. Basis material projections were then recombined to create VM images by using the linear attenuation coefficients at the specified energy for each material. Similarly, RED images were produced by replacing the linear attenuation values of Al and PMMA by their respective RED values in the projection space. VM and RED images were reconstructed using Feldkamp-Davis-Kress (FDK) and iterative algorithms. Hounsfield units, contrast-to-noise ratio (CNR) and RED values were compared against known values.The results after VM-CBCT production showed good material decomposition and consistent HUVM values, with measured root mean square errors (RMSE) from theoretical values, after FDK reconstruction, of 20.5, 5.7, 12.8 and 21.7 HU for 50, 80, 100 and 150 keV, respectively. The largest CNR improvements were observed for the 50 keV VM images. Image noise was reduced up to 28% in the VM-CBCT images after iterative image reconstruction. Relative electron density values measured for our method resulted in a mean percentage error of 0.0 ± 1.8%.This study describes a method to generate VM-CBCT and RED images using FS-DE scans obtained using the OBI of a linac, including the effects of the bowtie filter. The creation of VM and RED images increases the dynamic range of CBCT images, and provides additional data that may be used for adaptive radiotherapy, and on table verification for radiotherapy treatments.

    View details for DOI 10.1088/1361-6560/ab5c35

    View details for PubMedID 31775131

  • Dual Energy Imaging with a Dual Layer Flat Panel Detector Lu, M., Wang, A., Shapiro, E., Shiroma, A., Zhang, J., Steiger, J., Star-Lack, J., Schmidt, T. G., Chen, G. H., Bosmans, H. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2513499

    View details for Web of Science ID 000483585700037

  • Toward quantitative short-scan cone beam CT using shift-invariant filtered-backprojection with equal weighting and image domain shading correction Shi, L., Zhu, L., Wang, A., Matej, S., Metzler, S. D. SPIE-INT SOC OPTICAL ENGINEERING. 2019

    View details for DOI 10.1117/12.2534900

    View details for Web of Science ID 000535354300068

  • A novel phantom for characterization of dual energy imaging using an on-board imaging system. Physics in medicine and biology Haytmyradov, M., Patel, R., Mostafavi, H., Surucu, M., Wang, A. S., Harkenrider, M. M., Roeske, J. C. 2018


    Dual-energy (DE) imaging using an on-board imager (OBI) is being considered for real-time tumor tracking purposes. We describe here a custom phantom designed to optimize DE imaging parameters using the OBI of a commercial linear accelerator. The phantom was constructed of lung-, tissue- and bone-equivalent material slabs. Five simulated tumors located at two different depths were encased in the lung-equivalent materials. Two slabs with bone-equivalent material inserts were constructed to simulate ribs, which overlap the simulated tumors. DE bone suppression was performed using a weighted logarithmic subtraction based on an iterative method that minimized the contrast between simulated bone- and lung-equivalent materials. The phantom was subsequently used to evaluate different combinations of high-low energy pairs based on the signal-difference-to-noise ratio (SDNR) metric. The results show a strong correlation between tumor visibility and selected energy pairs, where higher energy separation leads to larger SDNR values. To evaluate the effect of image post-processing methods on tumor visibility, an anti-correlated noise reduction (ACNR) and adaptive kernel scatter correction methods were applied to subsequent DE images. Application of the ACNR technique approximately doubled the SDNR values, hence increasing tumor visibility, while scatter correction had little effect on SDNR values. This phantom allows for quick image acquisition and optimization of imaging parameters and weighting factors. Optimized DE imaging increases soft tissue visibility and may enhance automated lung tumor tracking allowing for real-time adaptive radiotherapy.

    View details for DOI 10.1088/1361-6560/aaf9dd

    View details for PubMedID 30566913

  • Feasibility of closed-MLC tracking using high sensitivity and multi-layer electronic portal imagers PHYSICS IN MEDICINE AND BIOLOGY Hu, Y., Jacobson, M. W., Shi, M., Myronakis, M., Wang, A., Baturin, P., Huber, P., Fueglistaller, R., Morf, D., Star-Lack, J., Berbeco, R. 2018; 63 (23): 235030


    In radiation therapy, improvements in treatment conformality are often limited by movement of target tissue. To better treat the target, tumor tracking strategies involving beam's-eye-view (BEV) have been explored. However, localization surrogates like implanted fiducial markers may sometimes leave the field-of-view (FOV), as defined by the linear accelerator (LINAC) multi-leaf collimator (MLC). Radiation leakage through the MLC has been measured previously at approximately 1%-2%. High sensitivity prototype detectors imagers may improve the ability to visualize objects outside of the MLC FOV during treatment. The present study presents a proof-of-concept for tracking fiducial markers outside the MLC FOV by employing high sensitivity detectors using a high-efficiency, prototype scintillating glass called LKH-5 and also investigates the impact of multi-layer imager (MLI) architecture. It was found that by improving the detector efficiency, using either of these methods results in a reduction of dose required for fiducial marker visibility. Further, image correction by a rectangular median filter will improve fiducial marker representation in the MLC blocked images. Quantified by measuring the peak-to-sidelobe ratio (PSR) of the normalized cross correlation (NCC) between a template of the fiducial marker with the blocked MLC acquisition, visibility has been found at a threshold of roughly 5 for all configurations with a 3  ×  3 cm2 ROI. For typical gadolinium oxysulfide (GOS) detectors in single and simulated 4-layer configurations, the minimum dose required for visualization was 20 and 10 MU, respectively. For LKH-5 detectors in single and simulated 4-layer configurations, this minimum dose was reduced to 4 and 2 MU, respectively. With a 6 MV flattening filter free (FFF) beam dose rate of 1400 MU min-1, the maximum detector frame rate while maintaining fiducial visibility is approximately 12 fps for a 4-layer LKH-5 configuration.

    View details for DOI 10.1088/1361-6560/aaef60

    View details for Web of Science ID 000452388800004

    View details for PubMedID 30520416

  • Investigation of combined kV/MV CBCT imaging with a high-DQE MV detector. Medical physics Lindsay, C., Bazalova-Carter, M., Wang, A., Shedlock, D., Wu, M., Newson, M., Xing, L., Ansbacher, W., Fahrig, R., Star-Lack, J. 2018


    Combined kV-MV cone-beam tomography (CBCT) imaging has been proposed for two potentially important image-guided radiotherapy applications: (a) scan time reduction (STR) and (b) metal artifact reduction (MAR). However, the feasibility of these techniques has been in question due to the low detective quantum efficiencies (DQEs) of commercially available electronic portal imagers (EPIDs). The goal of the work was to test whether a prototype high DQE MV detector can be used to generate acceptable quality pretreatment CBCT images at acceptable dose levels.6MV and 100 kVp projection data were acquired on a Truebeam system (Varian, Palo Alto, CA). The MV data were acquired using a prototype EPID containing two scintillators (a) a standard copper-gadolinium oxysulfide (Cu-GOS) screen having a zero-frequency DQE (DQE(0)) value of 1.4%, and (b) a prototype-focused cadmium tungstate (CWO) pixelated "strip" with a DQE(0) = 22%. The kV data were acquired using the standard onboard imager (DQE(0) = 70%). The angular spacing of the MV projections was 0.81° and the source output was 0.03 MU/projection while the kV projections were acquired with an angular spacing of 0.4° at 0.3 mAs/projection. Image quality was evaluated using (a) an 18-cm diameter electron density phantom (CIRS, Norfolk, VA) with nine contrast inserts and (b) the resolution section of the 20-cm diameter Catphan phantom (The Phantom Laboratory, Greenwich, NY). For the MAR studies, two opposing CIRS phantom inserts were replaced by steel rods. The reconstruction methods were based on combining MV and kV data into one sinogram. The MAR reconstruction utilized mostly kV raw data with only those rays corrupted by metal requiring replacement with MV data (total absorbed dose = 0.7 cGy). For the STR study, projections from partially overlapping 105°kV and MV acquisitions were combined to create a complete dataset that could have been acquired in 18 sec (absorbed dose = 2.5 cGy). MV-only (4.3 cGy) and kV-only (0.3 cGy) images were also reconstructed.The average signal-to-noise ratio (SNR) of the inserts in the MV-only CWO and GOS CIRS phantom images were 0.62× and 0.12× the SNR of the inserts in kV-only image, respectively. The limiting spatial resolutions in the MV-only GOS, MV-only CWO, and kV-only Catphan images were 3, 6, and 8 lp/cm, respectively. In the combined kV/CWO STR reconstruction, all contrast inserts were visible while only two were detectable in the kV/Cu-GOS image due to high levels of noise (average SNRs of kV/CWO and kV/GOS inserts were 0.97× and 0.18× the SNR of the kV-only inserts, respectively). In the kV-MV MAR reconstructions, streaking artifacts were substantially reduced with all inserts becoming clearly visible in the kV/CWO image while only two were visible in the kV/Cu-GOS image (average SNRs of the kV/CWO and kV/Cu-GOS CIRS with metal inserts were 0.94× and 0.35× the SNRs of the kV-only CIRS without metal inserts).We have demonstrated that a high-DQE MV detector can be applied to generating high-quality combined kV-MV images for SRT and MAR. Clinically acceptable doses were utilized.

    View details for DOI 10.1002/mp.13291

    View details for PubMedID 30428131

  • A modified McKinnon-Bates (MKB) algorithm for improved 4D cone-beam computed tomography (CBCT) of the lung MEDICAL PHYSICS Star-Lack, J., Sun, M., Oelhafen, M., Berkus, T., Pavkovich, J., Brehm, M., Arheit, M., Paysan, P., Wang, A., Munro, P., Seghers, D., Carvalho, L., Verbakel, W. R. 2018; 45 (8): 3783–99


    Four-dimensional (4D) cone-beam computed tomography (CBCT) of the lung is an effective tool for motion management in radiotherapy but presents a challenge because of slow gantry rotation times. Sorting the individual projections by breathing phase and using an established technique such as Feldkamp-Davis-Kress (FDK) to generate corresponding phase-correlated (PC) three-dimensional (3D) images results in reconstructions (FDK-PC) that often contain severe streaking artifacts due to the sparse angular sampling distributions. These can be reduced by further slowing down the gantry at the expense of incurring unwanted increases in scan times and dose. A computationally efficient alternative is the McKinnon-Bates (MKB) reconstruction algorithm that has shown promise in reducing view aliasing-induced streaking but can produce ghosting artifacts that reduce contrast and impede the determination of motion trajectories. The purpose of this work was to identify and correct shortcomings in the MKB algorithm.In the general MKB approach, a time-averaged 3D prior image is first reconstructed. The prior is then forward-projected at the same angles as the original projection data creating time-averaged reprojections. These reprojections are subsequently subtracted from the original (unblurred) projections to create motion-encoded difference projections. The difference projections are reconstructed into PC difference images that are added to the well-sampled 3D prior to create the higher quality 4D image. The cause of the ghosting in the traditional 4D MKB images was studied and traced to motion-induced streaking in the prior that, when reprojected, has the undesirable effect of re-encoding for motion in what should be a purely time-averaged reprojection. A new method, designated as the modified McKinnon-Bates (mMKB) algorithm, was developed based on destreaking the prior. This was coupled with a postprocessing 4D bilateral filter for noise suppression and edge preservation (mMKBbf ). The algorithms were tested with the 4D XCAT phantom using four simulated scan times (57, 60, 120, 180 s) and with two in vivo thorax studies (acquisition time of 60 and 90 s). Contrast-to-noise ratios (CNRs) of the target lesions and overall visual quality of the images were assessed.Prior destreaking (mMKB algorithm) reduced ghosting artifacts and increased CNRs for all cases, with the biggest impacts seen in the end inhale (EI) and end exhale (EE) phases of the respiratory cycle. For the XCAT phantom, mMKB lesion CNR was 44% higher than the MKB lesion CNR and was 81% higher than the FDK-PC lesion CNR (EI and EE phases). The bilateral filter provided a further average CNR improvement of 87% with the highest increases associated with longer scan times. Across all phases and scan times, the maximum mMKBbf -to-FDK-PC CNR improvement was over 300%. In vivo results agreed with XCAT results. Significantly less ghosting was observed throughout the mMKB images including near the lesions-of-interest and the diaphragm allowing for, in one case, visualization of a small tumor with nearly 30 mm of motion. The maximum FDK-PC-to-MKBbf CNR improvement for Patient 1's lesion was 261% and for Patient 2's lesion was 318%.The 4D mMKB algorithm yields good quality coronal and sagittal images in the thorax that may provide sufficient information for patient verification.

    View details for PubMedID 29869784

  • Physics considerations in MV-CBCT multi-layer imager design PHYSICS IN MEDICINE AND BIOLOGY Hu, Y., Fueglistaller, R., Myronakis, M., Rottmann, J., Wang, A., Shedlock, D., Morf, D., Baturin, P., Huber, P., Star-Lack, J., Berbeco, R. 2018; 63 (12): 125016


    Megavoltage (MV) cone-beam computed tomography (CBCT) using an electronic portal imaging (EPID) offers advantageous features, including 3D mapping, treatment beam registration, high-z artifact suppression, and direct radiation dose calculation. Adoption has been slowed by image quality limitations and concerns about imaging dose. Developments in imager design, including pixelated scintillators, structured phosphors, inexpensive scintillation materials, and multi-layer imager (MLI) architecture have been explored to improve EPID image quality and reduce imaging dose. The present study employs a hybrid Monte Carlo and linear systems model to determine the effect of detector design elements, such as multi-layer architecture and scintillation materials. We follow metrics of image quality including modulation transfer function (MTF) and noise power spectrum (NPS) from projection images to 3D reconstructions to in-plane slices and apply a task based figure-of-merit, the ideal observer signal-to-noise ratio (d') to determine the effect of detector design on object detectability. Generally, detectability was limited by detector noise performance. Deploying an MLI imager with a single scintillation material for all layers yields improvement in noise performance and d' linear with the number of layers. In general, improving x-ray absorption using thicker scintillators results in improved DQE(0). However, if light yield is low, performance will be affected by electronic noise at relatively high doses, resulting in rapid image quality degradation. Maximizing image quality in a heterogenous MLI detector (i.e. multiple different scintillation materials) is most affected by limiting total noise. However, while a second-order effect, maximizing total spatial resolution of the MLI detector is a balance between the intensity contribution of each layer against its individual MTF. So, while a thinner scintillator may yield a maximal individual-layer MTF, its quantum efficiency will be relatively low in comparison to a thicker scintillator and thus, intensity contribution may be insufficient to noticeably improve the total detector MTF.

    View details for DOI 10.1088/1361-6560/aac8c6

    View details for Web of Science ID 000435938800001

    View details for PubMedID 29846180

    View details for PubMedCentralID PMC6042214

  • Multi-layer imager design for mega-voltage spectral imaging PHYSICS IN MEDICINE AND BIOLOGY Myronakis, M., Hu, Y., Fueglistaller, R., Wang, A., Baturin, P., Huber, P., Morf, D., Star-Lack, J., Berbeco, R. 2018; 63 (10): 105002


    The architecture of multi-layer imagers (MLIs) can be exploited to provide megavoltage spectral imaging (MVSPI) for specific imaging tasks. In the current work, we investigated bone suppression and gold fiducial contrast enhancement as two clinical tasks which could be improved with spectral imaging. A method based on analytical calculations that enables rapid investigation of MLI component materials and thicknesses was developed and validated against Monte Carlo computations. The figure of merit for task-specific imaging performance was the contrast-to-noise ratio (CNR) of the gold fiducial when the CNR of bone was equal to zero after a weighted subtraction of the signals obtained from each MLI layer. Results demonstrated a sharp increase in the CNR of gold when the build-up component or scintillation materials and thicknesses were modified. The potential for low-cost, prompt implementation of specific modifications (e.g. composition of the build-up component) could accelerate clinical translation of MVSPI.

    View details for DOI 10.1088/1361-6560/aabe21

    View details for Web of Science ID 000431949400002

    View details for PubMedID 29652670

    View details for PubMedCentralID PMC5991631

  • Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter - Part I: Core algorithms and validation MEDICAL PHYSICS Maslowski, A., Wang, A., Sun, M., Wareing, T., Davis, I., Star-Lack, J. 2018; 45 (5): 1899–1913


    To describe Acuros® CTS, a new software tool for rapidly and accurately estimating scatter in x-ray projection images by deterministically solving the linear Boltzmann transport equation (LBTE).The LBTE describes the behavior of particles as they interact with an object across spatial, energy, and directional (propagation) domains. Acuros CTS deterministically solves the LBTE by modeling photon transport associated with an x-ray projection in three main steps: (a) Ray tracing photons from the x-ray source into the object where they experience their first scattering event and form scattering sources. (b) Propagating photons from their first scattering sources across the object in all directions to form second scattering sources, then repeating this process until all high-order scattering sources are computed using the source iteration method. (c) Ray-tracing photons from scattering sources within the object to the detector, accounting for the detector's energy and anti-scatter grid responses. To make this process computationally tractable, a combination of analytical and discrete methods is applied. The three domains are discretized using the Linear Discontinuous Finite Elements, Multigroup, and Discrete Ordinates methods, respectively, which confer the ability to maintain the accuracy of a continuous solution. Furthermore, through the implementation in CUDA, we sought to exploit the parallel computing capabilities of graphics processing units (GPUs) to achieve the speeds required for clinical utilization. Acuros CTS was validated against Geant4 Monte Carlo simulations using two digital phantoms: (a) a water phantom containing lung, air, and bone inserts (WLAB phantom) and (b) a pelvis phantom derived from a clinical CT dataset. For these studies, we modeled the TrueBeam® (Varian Medical Systems, Palo Alto, CA) kV imaging system with a source energy of 125 kVp. The imager comprised a 600 μm-thick Cesium Iodide (CsI) scintillator and a 10:1 one-dimensional anti-scatter grid. For the WLAB studies, the full-fan geometry without a bowtie filter was used (with and without the anti-scatter grid). For the pelvis phantom studies, a half-fan geometry with bowtie was used (with the anti-scatter grid). Scattered and primary photon fluences and energies deposited in the detector were recorded.The Acuros CTS and Monte Carlo results demonstrated excellent agreement. For the WLAB studies, the average percent difference between the Monte Carlo- and Acuros-generated scattered photon fluences at the face of the detector was -0.7%. After including the detector response, the average percent differences between the Monte Carlo- and Acuros-generated scatter fractions (SF) were -0.1% without the grid and 0.6% with the grid. For the digital pelvis simulation, the Monte Carlo- and Acuros-generated SFs agreed to within 0.1% on average, despite the scatter-to-primary ratios (SPRs) being as high as 5.5. The Acuros CTS computation time for each scatter image was ~1 s using a single GPU.Acuros CTS enables a fast and accurate calculation of scatter images by deterministically solving the LBTE thus offering a computationally attractive alternative to Monte Carlo methods. Part II describes the application of Acuros CTS to scatter correction of CBCT scans on the TrueBeam system.

    View details for PubMedID 29509970

    View details for PubMedCentralID PMC5948176

  • Acuros CTS: A fast, linear Boltzmann transport equation solver for computed tomography scatter - Part II: System modeling, scatter correction, and optimization MEDICAL PHYSICS Wang, A., Maslowski, A., Messmer, P., Lehmann, M., Strzelecki, A., Yu, E., Paysan, P., Brehm, M., Munro, P., Star-Lack, J., Seghers, D. 2018; 45 (5): 1914–25


    To correct for scatter in kV cone-beam CT (CBCT) projection data on a clinical system using a new tool, Acuros® CTS, that estimates scatter images rapidly and accurately by deterministically solving the linear Boltzmann transport equation.Phantom and patient CBCT scans were acquired on TrueBeam® radiotherapy machines. A first-pass reconstruction was used to create water and bone density maps of the imaged object, which was updated to include a more accurate representation of the patient couch. The imaging system model accounted for the TrueBeam x-ray source (polychromatic spectrum, beam filtration, bowtie filter, and collimation hardware) and x-ray detection system (antiscatter grid, flat-panel imager). Acuros CTS then used the system and object models to estimate the scatter component of each projection image, which was subtracted from the measured projections. The corrected projections were then reconstructed to produce the final result. We examined the tradeoff between run time and accuracy using a Pareto optimization of key parameters, including the voxel size of the down-sampled object model, the number of pixels in the down-sampled detector, and the number of scatter images (angular down-sampling). All computations and reconstructions were performed on a research workstation containing two graphics processing units (GPUs). In addition, we established a method for selecting a subset of projections for which scatter images were calculated. The projections were selected to minimize interpolation errors in the remaining projections. Image quality improvement was assessed by measuring the accuracy of the reconstructed phantom and patient images.The Pareto optimization yielded a set of parameters with an average run time of 26 seconds for scatter correction while maintaining high accuracy of scatter estimation. This was achieved in part by means of optimizing the projection angles that were processed, thus favoring the use of more angles in the lateral (i.e., horizontal) direction and fewer angles in the AP direction. In a 40 cm solid water phantom reconstruction, nonuniformities were decreased from 217 HU without scatter correction to 51 HU with conventional (kernel-based) scatter correction to 17 HU with Acuros CTS-based scatter correction. In clinical pelvis scans, nonuniformities in the bladder were reduced from 85 HU with conventional scatter correction to 14 HU with Acuros CTS.Acuros CTS is a promising new tool for fast and accurate scatter correction for CBCT imaging. By carefully modeling the imaging chain and optimizing several parameters, we achieved high correction accuracies with computation times compatible with the clinical workflow. The improvement in image quality enables better soft-tissue visualization and potentially enables applications such as adaptive radiotherapy.

    View details for PubMedID 29509973

  • Leveraging multi-layer imager detector design to improve low-dose performance for megavoltage cone-beam computed tomography PHYSICS IN MEDICINE AND BIOLOGY Hu, Y., Rottmann, J., Fueglistaller, R., Myronakis, M., Wang, A., Huber, P., Shedlock, D., Morf, D., Baturin, P., Star-Lack, J., Berbeco, R. 2018; 63 (3): 035022


    While megavoltage cone-beam computed tomography (CBCT) using an electronic portal imaging device (EPID) provides many advantages over kilovoltage (kV) CBCT, clinical adoption is limited by its high doses. Multi-layer imager (MLI) EPIDs increase DQE(0) while maintaining high resolution. However, even well-designed, high-performance MLIs suffer from increased electronic noise from each readout, degrading low-dose image quality. To improve low-dose performance, shift-and-bin addition (ShiBA) imaging is proposed, leveraging the unique architecture of the MLI. ShiBA combines hardware readout-binning and super-resolution concepts, reducing electronic noise while maintaining native image sampling. The imaging performance of full-resolution (FR); standard, aligned binned (BIN); and ShiBA images in terms of noise power spectrum (NPS), electronic NPS, modulation transfer function (MTF), and the ideal observer signal-to-noise ratio (SNR)-the detectability index (d')-are compared. The FR 4-layer readout of the prototype MLI exhibits an electronic NPS magnitude 6-times higher than a state-of-the-art single layer (SLI) EPID. Although the MLI is built on the same readout platform as the SLI, with each layer exhibiting equivalent electronic noise, the multi-stage readout of the MLI results in electronic noise 50% higher than simple summation. Electronic noise is mitigated in both BIN and ShiBA imaging, reducing its total by ~12 times. ShiBA further reduces the NPS, effectively upsampling the image, resulting in a multiplication by a sinc2 function. Normalized NPS show that neither ShiBA nor BIN otherwise affects image noise. The LSF shows that ShiBA removes the pixilation artifact of BIN images and mitigates the effect of detector shift, but does not quantifiably improve the MTF. ShiBA provides a pre-sampled representation of the images, mitigating phase dependence. Hardware binning strategies lower the quantum noise floor, with 2  ×  2 implementation reducing the dose at which DQE(0) degrades by 10% from 0.01 MU to 0.004 MU, representing 20% improvement in d'.

    View details for DOI 10.1088/1361-6560/aaa160

    View details for Web of Science ID 000423829300005

    View details for PubMedID 29235440

    View details for PubMedCentralID PMC5824638

  • Spectral imaging using clinical megavoltage beams and a novel multi-layer imager PHYSICS IN MEDICINE AND BIOLOGY Myronakis, M., Fueglistaller, R., Rottmann, J., Hu, Y., Wang, A., Baturin, P., Huber, P., Morf, D., Star-Lack, J., Berbeco, R. 2017; 62 (23): 9127–39


    We assess the feasibility of clinical megavoltage (MV) spectral imaging for material and bone separation with a novel multi-layer imager (MLI) prototype. The MLI provides higher detective quantum efficiency and lower noise than conventional electronic portal imagers. Simulated experiments were performed using a validated Monte Carlo model of the MLI to estimate energy absorption and energy separation between the MLI components. Material separation was evaluated experimentally using solid water and aluminum (Al), copper (Cu) and gold (Au) for 2.5 MV, 6 MV and 6 MV flattening filter free (FFF) clinical photon beams. An anthropomorphic phantom with implanted gold fiducials was utilized to further demonstrate bone/gold separation. Weighted subtraction imaging was employed for material and bone separation. The weighting factor (w) was iteratively estimated, with the optimal w value determined by minimization of the relative signal difference ([Formula: see text]) and signal-difference-to-noise ratio (SDNR) between material (or bone) and the background. Energy separation between layers of the MLI was mainly the result of beam hardening between components with an average energy separation between 34 and 47 keV depending on the x-ray beam energy. The minimum average energy of the detected spectrum in the phosphor layer was 123 keV in the top layer of the MLI with the 2.5 MV beam. The w values that minimized [Formula: see text] and SDNR for Al, Cu and Au were 0.89, 0.76 and 0.64 for 2.5 MV; for 6 MV FFF, w was 0.98, 0.93 and 0.77 respectively. Bone suppression in the anthropomorphic phantom resulted in improved visibility of the gold fiducials with the 2.5 MV beam. Optimization of the MLI design is required to achieve optimal separation at clinical MV beam energies.

    View details for DOI 10.1088/1361-6560/aa94f9

    View details for Web of Science ID 000415302000003

    View details for PubMedID 29053107

    View details for PubMedCentralID PMC5724525

  • A novel method for quantification of beam's-eye-view tumor tracking performance MEDICAL PHYSICS Hu, Y., Myronakis, M., Rottmann, J., Wang, A., Morf, D., Shedlock, D., Baturin, P., Star-Lack, J., Berbeco, R. 2017; 44 (11): 5650–59


    In-treatment imaging using an electronic portal imaging device (EPID) can be used to confirm patient and tumor positioning. Real-time tumor tracking performance using current digital megavolt (MV) imagers is hindered by poor image quality. Novel EPID designs may help to improve quantum noise response, while also preserving the high spatial resolution of the current clinical detector. Recently investigated EPID design improvements include but are not limited to multi-layer imager (MLI) architecture, thick crystalline and amorphous scintillators, and phosphor pixilation and focusing. The goal of the present study was to provide a method of quantitating improvement in tracking performance as well as to reveal the physical underpinnings of detector design that impact tracking quality. The study employs a generalizable ideal observer methodology for the quantification of tumor tracking performance. The analysis is applied to study both the effect of increasing scintillator thickness on a standard, single-layer imager (SLI) design as well as the effect of MLI architecture on tracking performance.The present study uses the ideal observer signal-to-noise ratio (d') as a surrogate for tracking performance. We employ functions which model clinically relevant tasks and generalized frequency-domain imaging metrics to connect image quality with tumor tracking. A detection task for relevant Cartesian shapes (i.e., spheres and cylinders) was used to quantitate trackability of cases employing fiducial markers. Automated lung tumor tracking algorithms often leverage the differences in benign and malignant lung tissue textures. These types of algorithms (e.g., soft-tissue localization - STiL) were simulated by designing a discrimination task, which quantifies the differentiation of tissue textures, measured experimentally and fit as a power-law in trend (with exponent β) using a cohort of MV images of patient lungs. The modeled MTF and NPS were used to investigate the effect of scintillator thickness and MLI architecture on tumor tracking performance.Quantification of MV images of lung tissue as an inverse power-law with respect to frequency yields exponent values of β = 3.11 and 3.29 for benign and malignant tissues, respectively. Tracking performance with and without fiducials was found to be generally limited by quantum noise, a factor dominated by quantum detective efficiency (QDE). For generic SLI construction, increasing the scintillator thickness (gadolinium oxysulfide - GOS) from a standard 290 μm to 1720 μm reduces noise to about 10%. However, 81% of this reduction is appreciated between 290 and 1000 μm. In comparing MLI and SLI detectors of equivalent individual GOS layer thickness, the improvement in noise is equal to the number of layers in the detector (i.e., 4) with almost no difference in MTF. Further, improvement in tracking performance was slightly less than the square-root of the reduction in noise, approximately 84-90%. In comparing an MLI detector with an SLI with a GOS scintillator of equivalent total thickness, improvement in object detectability is approximately 34-39%.We have presented a novel method for quantification of tumor tracking quality and have applied this model to evaluate the performance of SLI and MLI EPID designs. We showed that improved tracking quality is primarily limited by improvements in NPS. When compared to very thick scintillator SLI, employing MLI architecture exhibits the same gains in QDE, but by mitigating the effect of optical Swank noise, results in more dramatic improvements in tracking performance.

    View details for PubMedID 28887836

    View details for PubMedCentralID PMC5689096

  • A novel multilayer MV imager computational model for component optimization MEDICAL PHYSICS Myronakis, M., Star-Lack, J., Baturin, P., Rottmann, J., Morf, D., Wang, A., Hu, Y., Shedlock, D., Berbeco, R. I. 2017; 44 (8): 4213–22


    A novel Megavoltage (MV) multilayer imager (MLI) design featuring higher detective quantum efficiency and lower noise than current conventional MV imagers in clinical use has been recently reported. Optimization of the MLI design for multiple applications including tumor tracking, MV-CBCT and portal dosimetry requires a computational model that will provide insight into the physics processes that affect the overall and individual components' performance. The purpose of the current work was to develop and validate a comprehensive computational model that can be used for MLI optimization.The MLI model was built using the Geant4 Application for Tomographic Emission (GATE) application. The model includes x-ray and charged-particle interactions as well as the optical transfer within the phosphor. A first prototype MLI device featuring a stack of four detection layers was used for model validation. Each layer of the prototype contains a copper buildup plate, a phosphor screen and photodiode array. The model was validated against measured data of Modulation Transfer Function (MTF), Noise-Power Spectrum (NPS), and Detective Quantum Efficiency (DQE). MTF was computed using a slanted slit with 2.3° angle and 0.1 mm width. NPS was obtained using the autocorrelation function technique. DQE was calculated from MTF and NPS data. The comparison metrics between simulated and measured data were the Pearson's correlation coefficient (r) and the normalized root-mean-square error (NRMSE).Good agreement between measured and simulated MTF and NPS values was observed. Pearson's correlation coefficient for the combined signal from all layers of the MLI was equal to 0.9991 for MTF and 0.9992 for NPS; NRMSE was 0.0121 for MTF and 0.0194 for NPS. Similarly, the DQE correlation coefficient for the combined signal was 0.9888 and the NRMSE was 0.0686.A comprehensive model of the novel MLI design was developed using the GATE toolkit and validated against measured MTF, NPS, and DQE data acquired with a prototype device featuring four layers. This model will be used for further optimization of the imager components and configuration for clinical radiotherapy applications.

    View details for PubMedID 28555935

    View details for PubMedCentralID PMC5553708

  • Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm JOURNAL OF MEDICAL IMAGING Schmidt, T., Wang, A. S., Coradi, T., Haas, B., Star-Lack, J. 2016; 3 (4): 043502


    The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

    View details for PubMedID 27921070

    View details for PubMedCentralID PMC5126039

  • Non-local total-variation (NLTV) minimization combined with reweighted L1-norm for compressed sensing CT reconstruction PHYSICS IN MEDICINE AND BIOLOGY Kim, H., Chen, J., Wang, A., Chuang, C., Held, M., Pouliot, J. 2016; 61 (18): 6878–91


    The compressed sensing (CS) technique has been employed to reconstruct CT/CBCT images from fewer projections as it is designed to recover a sparse signal from highly under-sampled measurements. Since the CT image itself cannot be sparse, a variety of transforms were developed to make the image sufficiently sparse. The total-variation (TV) transform with local image gradient in L1-norm was adopted in most cases. This approach, however, which utilizes very local information and penalizes the weight at a constant rate regardless of different degrees of spatial gradient, may not produce qualified reconstructed images from noise-contaminated CT projection data. This work presents a new non-local operator of total-variation (NLTV) to overcome the deficits stated above by utilizing a more global search and non-uniform weight penalization in reconstruction. To further improve the reconstructed results, a reweighted L1-norm that approximates the ideal sparse signal recovery of the L0-norm is incorporated into the NLTV reconstruction with additional iterates. This study tested the proposed reconstruction method (reweighted NLTV) from under-sampled projections of 4 objects and 5 experiments (1 digital phantom with low and high noise scenarios, 1 pelvic CT, and 2 CBCT images). We assessed its performance against the conventional TV, NLTV and reweighted TV transforms in the tissue contrast, reconstruction accuracy, and imaging resolution by comparing contrast-noise-ratio (CNR), normalized root-mean square error (nRMSE), and profiles of the reconstructed images. Relative to the conventional NLTV, combining the reweighted L1-norm with NLTV further enhanced the CNRs by 2-4 times and improved reconstruction accuracy. Overall, except for the digital phantom with low noise simulation, our proposed algorithm produced the reconstructed image with the lowest nRMSEs and the highest CNRs for each experiment.

    View details for DOI 10.1088/0031-9155/61/18/6878

    View details for Web of Science ID 000384317800002

    View details for PubMedID 27589006

  • Striped Ratio Grids for Scatter Estimation Hsieh, S. S., Wang, A. S., Star-Lack, J., Kontos, D., Flohr, T. G., Lo, J. Y. SPIE-INT SOC OPTICAL ENGINEERING. 2016

    View details for DOI 10.1117/12.2216896

    View details for Web of Science ID 000378352900018

  • Accuracy of patient specific organ-dose estimates obtained using an automated image segmentation algorithm Schmidt, T., Wang, A., Coradi, T., Haas, B., Star-Lack, J., Kontos, D., Flohr, T. G., Lo, J. Y. SPIE-INT SOC OPTICAL ENGINEERING. 2016

    View details for DOI 10.1117/12.2217374

    View details for Web of Science ID 000378352900035

  • A piecewise-focused high DQE detector for MV imaging MEDICAL PHYSICS Star-Lack, J., Shedlock, D., Swahn, D., Humber, D., Wang, A., Hirsh, H., Zentai, G., Sawkey, D., Kruger, I., Sun, M., Abel, E., Virshup, G., Shin, M., Fahrig, R. 2015; 42 (9): 5084-5099


    Electronic portal imagers (EPIDs) with high detective quantum efficiencies (DQEs) are sought to facilitate the use of the megavoltage (MV) radiotherapy treatment beam for image guidance. Potential advantages include high quality (treatment) beam's eye view imaging, and improved cone-beam computed tomography (CBCT) generating images with more accurate electron density maps with immunity to metal artifacts. One approach to increasing detector sensitivity is to couple a thick pixelated scintillator array to an active matrix flat panel imager (AMFPI) incorporating amorphous silicon thin film electronics. Cadmium tungstate (CWO) has many desirable scintillation properties including good light output, a high index of refraction, high optical transparency, and reasonable cost. However, due to the 0 1 0 cleave plane inherent in its crystalline structure, the difficulty of cutting and polishing CWO has, in part, limited its study relative to other scintillators such as cesium iodide and bismuth germanate (BGO). The goal of this work was to build and test a focused large-area pixelated "strip" CWO detector.A 361  ×  52 mm scintillator assembly that contained a total of 28 072 pixels was constructed. The assembly comprised seven subarrays, each 15 mm thick. Six of the subarrays were fabricated from CWO with a pixel pitch of 0.784 mm, while one array was constructed from BGO for comparison. Focusing was achieved by coupling the arrays to the Varian AS1000 AMFPI through a piecewise linear arc-shaped fiber optic plate. Simulation and experimental studies of modulation transfer function (MTF) and DQE were undertaken using a 6 MV beam, and comparisons were made between the performance of the pixelated strip assembly and the most common EPID configuration comprising a 1 mm-thick copper build-up plate attached to a 133 mg/cm(2) gadolinium oxysulfide scintillator screen (Cu-GOS). Projection radiographs and CBCT images of phantoms were acquired. The work also introduces the use of a lightweight edge phantom to generate MTF measurements at MV energies and shows its functional equivalence to the more cumbersome slit-based method.Measured and simulated DQE(0)'s of the pixelated CWO detector were 22% and 26%, respectively. The average measured and simulated ratios of CWO DQE(f) to Cu-GOS DQE(f) across the frequency range of 0.0-0.62 mm(-1) were 23 and 29, respectively. 2D and 3D imaging studies confirmed the large dose efficiency improvement and that focus was maintained across the field of view. In the CWO CBCT images, the measured spatial resolution was 7 lp/cm. The contrast-to-noise ratio was dramatically improved reflecting a 22 × sensitivity increase relative to Cu-GOS. The CWO scintillator material showed significantly higher stability and light yield than the BGO material.An efficient piecewise-focused pixelated strip scintillator for MV imaging is described that offers more than a 20-fold dose efficiency improvement over Cu-GOS.

    View details for DOI 10.1118/1.4927786

    View details for Web of Science ID 000360645000011

    View details for PubMedID 26328960

    View details for PubMedCentralID PMC4529442

  • Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method MEDICAL PHYSICS Wang, A. S., Stayman, J., Otake, Y., Vogt, S., Kleinszig, G., Siewerdsen, J. H. 2015; 42 (5): 2699–2708


    To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours.The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed.Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method.The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.

    View details for PubMedID 25979068

  • Automatic Localization of Target Vertebrae in Spine Surgery SPINE Lo, S. L., Otake, Y., Puvanesarajah, V., Wang, A. S., Uneri, A., De Silva, T., Vogt, S., Kleinszig, G., Elder, B. D., Goodwin, C., Kosztowski, T. A., Liauw, J. A., Groves, M., Bydon, A., Sciubba, D. M., Witham, T. F., Wolinsky, J., Aygun, N., Gokaslan, Z. L., Siewerdsen, J. H. 2015; 40 (8): E476–E483


    A 3-dimensional-2-dimensional (3D-2D) image registration algorithm, "LevelCheck," was used to automatically label vertebrae in intraoperative mobile radiographs obtained during spine surgery. Accuracy, computation time, and potential failure modes were evaluated in a retrospective study of 20 patients.To measure the performance of the LevelCheck algorithm using clinical images acquired during spine surgery.In spine surgery, the potential for wrong level surgery is significant due to the difficulty of localizing target vertebrae based solely on visual impression, palpation, and fluoroscopy. To remedy this difficulty and reduce the risk of wrong-level surgery, our team introduced a program (dubbed LevelCheck) to automatically localize target vertebrae in mobile radiographs using robust 3D-2D image registration to preoperative computed tomographic (CT) scan.Twenty consecutive patients undergoing thoracolumbar spine surgery, for whom both a preoperative CT scan and an intraoperative mobile radiograph were available, were retrospectively analyzed. A board-certified neuroradiologist determined the "true" vertebra levels in each radiograph. Registration of the preoperative CT scan to the intraoperative radiograph was calculated via LevelCheck, and projection distance errors were analyzed. Five hundred random initializations were performed for each patient, and algorithm settings (viz, the number of robust multistarts, ranging 50-200) were varied to evaluate the trade-off between registration error and computation time. Failure mode analysis was performed by individually analyzing unsuccessful registrations (>5 mm distance error) observed with 50 multistarts.At 200 robust multistarts (computation time of ∼26 s), the registration accuracy was 100% across all 10,000 trials. As the number of multistarts (and computation time) decreased, the registration remained fairly robust, down to 99.3% registration accuracy at 50 multistarts (computation time ∼7 s).The LevelCheck algorithm correctly identified target vertebrae in intraoperative mobile radiographs of the thoracolumbar spine, demonstrating acceptable computation time, compatibility with routinely obtained preoperative CT scans, and warranting investigation in prospective studies.N/A.

    View details for PubMedID 25646750

    View details for PubMedCentralID PMC4433144

  • 3D-2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation PHYSICS IN MEDICINE AND BIOLOGY Otake, Y., Wang, A. S., Uneri, A., Kleinszig, G., Vogt, S., Aygun, N., Lo, S. L., Wolinsky, J., Gokaslan, Z. L., Siewerdsen, J. H. 2015; 60 (5): 2075–90


    An image-based 3D-2D registration method is presented using radiographs acquired in the uncalibrated, unconstrained geometry of mobile radiography. The approach extends a previous method for six degree-of-freedom (DOF) registration in C-arm fluoroscopy (namely 'LevelCheck') to solve the 9-DOF estimate of geometry in which the position of the source and detector are unconstrained. The method was implemented using a gradient correlation similarity metric and stochastic derivative-free optimization on a GPU. Development and evaluation were conducted in three steps. First, simulation studies were performed that involved a CT scan of an anthropomorphic body phantom and 1000 randomly generated digitally reconstructed radiographs in posterior-anterior and lateral views. A median projection distance error (PDE) of 0.007 mm was achieved with 9-DOF registration compared to 0.767 mm for 6-DOF. Second, cadaver studies were conducted using mobile radiographs acquired in three anatomical regions (thorax, abdomen and pelvis) and three levels of source-detector distance (~800, ~1000 and ~1200 mm). The 9-DOF method achieved a median PDE of 0.49 mm (compared to 2.53 mm for the 6-DOF method) and demonstrated robustness in the unconstrained imaging geometry. Finally, a retrospective clinical study was conducted with intraoperative radiographs of the spine exhibiting real anatomical deformation and image content mismatch (e.g. interventional devices in the radiograph that were not in the CT), demonstrating a PDE = 1.1 mm for the 9-DOF approach. Average computation time was 48.5 s, involving 687 701 function evaluations on average, compared to 18.2 s for the 6-DOF method. Despite the greater computational load, the 9-DOF method may offer a valuable tool for target localization (e.g. decision support in level counting) as well as safety and quality assurance checks at the conclusion of a procedure (e.g. overlay of planning data on the radiograph for verification of the surgical product) in a manner consistent with natural surgical workflow.

    View details for PubMedID 25674851

    View details for PubMedCentralID PMC4640192

  • Asymmetric Scatter Kernels for Software-Based Scatter Correction of Gridless Mammography Wang, A., Shapiro, E., Yoon, S., Ganguly, A., Proano, C., Colbeth, R., Lehto, E., Star-Lack, J., Hoeschen, C., Kontos, D. SPIE-INT SOC OPTICAL ENGINEERING. 2015

    View details for DOI 10.1117/12.2081501

    View details for Web of Science ID 000355581700050

  • Known-Component 3D-2D Registration for Image Guidance and Quality Assurance in Spine Surgery Pedicle Screw Placement Uneri, A., Stayman, J. W., De Silva, T., Wang, A. S., Kleinszig, G., Vogt, S., Khanna, A. J., Wolinsky, J., Gokaslan, Z. L., Siewerdsen, J. H., Yaniv, Z. R., Webster, R. J. SPIE-INT SOC OPTICAL ENGINEERING. 2015

    View details for DOI 10.1117/12.2082210

    View details for Web of Science ID 000354365300049

  • Evaluation of low-dose limits in 3D-2D rigid registration for surgical guidance Uneri, A., Wang, A. S., Otake, Y., Kleinszig, G., Vogt, S., Khanna, A. J., Gallia, G. L., Gokaslan, Z. L., Siewerdsen, J. H. IOP PUBLISHING LTD. 2014: 5329–45


    An algorithm for intensity-based 3D-2D registration of CT and C-arm fluoroscopy is evaluated for use in surgical guidance, specifically considering the low-dose limits of the fluoroscopic x-ray projections. The registration method is based on a framework using the covariance matrix adaptation evolution strategy (CMA-ES) to identify the 3D patient pose that maximizes the gradient information similarity metric. Registration performance was evaluated in an anthropomorphic head phantom emulating intracranial neurosurgery, using target registration error (TRE) to characterize accuracy and robustness in terms of 95% confidence upper bound in comparison to that of an infrared surgical tracking system. Three clinical scenarios were considered: (1) single-view image+guidance, wherein a single x-ray projection is used for visualization and 3D-2D guidance; (2) dual-view image+guidance, wherein one projection is acquired for visualization, combined with a second (lower-dose) projection acquired at a different C-arm angle for 3D-2D guidance; and (3) dual-view guidance, wherein both projections are acquired at low dose for the purpose of 3D-2D guidance alone (not visualization). In each case, registration accuracy was evaluated as a function of the entrance surface dose associated with the projection view(s). Results indicate that images acquired at a dose as low as 4 μGy (approximately one-tenth the dose of a typical fluoroscopic frame) were sufficient to provide TRE comparable or superior to that of conventional surgical tracking, allowing 3D-2D guidance at a level of dose that is at most 10% greater than conventional fluoroscopy (scenario #2) and potentially reducing the dose to approximately 20% of the level in a conventional fluoroscopically guided procedure (scenario #3).

    View details for DOI 10.1088/0031-9155/59/18/5329

    View details for Web of Science ID 000341381900011

    View details for PubMedID 25146673

  • dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images PHYSICS IN MEDICINE AND BIOLOGY Dang, H., Wang, A. S., Sussman, M. S., Siewerdsen, J. H., Stayman, J. W. 2014; 59 (17): 4799–4826


    Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.

    View details for DOI 10.1088/0031-9155/59/17/4799

    View details for Web of Science ID 000341328200004

    View details for PubMedID 25097144

    View details for PubMedCentralID PMC4142353

  • Deformable image registration with local rigidity constraints for cone-beam CT-guided spine surgery PHYSICS IN MEDICINE AND BIOLOGY Reaungamornrat, S., Wang, A. S., Uneri, A., Otake, Y., Khanna, A. J., Siewerdsen, J. H. 2014; 59 (14): 3761–87


    Image-guided spine surgery (IGSS) is associated with reduced co-morbidity and improved surgical outcome. However, precise localization of target anatomy and adjacent nerves and vessels relative to planning information (e.g., device trajectories) can be challenged by anatomical deformation. Rigid registration alone fails to account for deformation associated with changes in spine curvature, and conventional deformable registration fails to account for rigidity of the vertebrae, causing unrealistic distortions in the registered image that can confound high-precision surgery. We developed and evaluated a deformable registration method capable of preserving rigidity of bones while resolving the deformation of surrounding soft tissue. The method aligns preoperative CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with constraints on rigid body motion imposed according to a simple intensity threshold of bone intensities. The constraints enforced three properties of a rigid transformation-namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments involving phantoms, an ovine spine, and a human cadaver as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (uFFD) and Demons registration. FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (D = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (S = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. The approach therefore overcomes distortions intrinsic to uFFD and could better facilitate high-precision IGSS.

    View details for DOI 10.1088/0031-9155/59/14/3761

    View details for Web of Science ID 000338771300008

    View details for PubMedID 24937093

    View details for PubMedCentralID PMC4118832

  • Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT MEDICAL PHYSICS Wang, A. S., Stayman, J., Otake, Y., Vogt, S., Kleinszig, G., Khanna, A., Gallia, G. L., Siewerdsen, J. H. 2014; 41 (7): 071915


    A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient- and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans.In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts.For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection ofuncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction.Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

    View details for PubMedID 24989393

  • Efficacy of fixed filtration for rapid kVp-switching dual energy x-ray systems. Medical physics Yao, Y., Wang, A. S., Pelc, N. J. 2014; 41 (3): 031914-?


    Dose efficiency of dual kVp imaging can be improved if the two beams are filtered to remove photons in the common part of their spectra, thereby increasing spectral separation. While there are a number of advantages to rapid kVp-switching for dual energy, it may not be feasible to have two different filters for the two spectra. Therefore, the authors are interested in whether a fixed added filter can improve the dose efficiency of kVp-switching dual energy x-ray systems.The authors hypothesized that a K-edge filter would provide the energy selectivity needed to remove overlap of the spectra and hence increase the precision of material separation at constant dose. Preliminary simulations were done using calcium and water basis materials and 80 and 140 kVp x-ray spectra. Precision of the decomposition was evaluated based on the propagation of the Poisson noise through the decomposition function. Considering availability and cost, the authors chose a commercial Gd2O2S screen as the filter for their experimental validation. Experiments were conducted on a table-top system using a phantom with various thicknesses of acrylic and copper and 70 and 125 kVp x-ray spectra. The authors kept the phantom exposure roughly constant with and without filtration by adjusting the tube current. The filtered and unfiltered raw data of both low and high energy were decomposed into basis material and the variance of the decomposition for each thickness pair was calculated. To evaluate the filtration performance, the authors measured the ratio of material decomposition variance with and without filtration.Simulation results show that the ideal filter material depends on the object composition and thickness, and ranges across the lanthanide series, with higher atomic number filters being preferred for more attenuating objects. Variance reduction increases with filter thickness, and substantial reductions (40%) can be achieved with a 2× loss in intensity. The authors' experimental results validate the simulations, yet were overall slightly worse than expectation. For large objects, conventional (non-K-edge) beam hardening filters perform well.This study demonstrates the potential of fixed K-edge filtration to improve the dose efficiency and material decomposition precision for rapid kVp-switching dual energy systems.

    View details for DOI 10.1118/1.4866381

    View details for PubMedID 24593732

  • Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction PHYSICS IN MEDICINE AND BIOLOGY Wang, A. S., Stayman, J., Otake, Y., Kleinszig, G., Vogt, S., Gallia, G. L., Khanna, A., Siewerdsen, J. H. 2014; 59 (4): 1005–26


    The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼ 40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2 × over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼ 1.7 mGy and benefits from 50% sparsity at dose below ∼ 1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.

    View details for PubMedID 24504126

    View details for PubMedCentralID PMC4046706

  • Dual-energy cone-beam CT with a flat-panel detector: Effect of reconstruction algorithm on material classification MEDICAL PHYSICS Zbijewski, W., Gang, G. J., Xu, J., Wang, A. S., Stayman, J. W., Taguchi, K., Carrino, J. A., Siewerdsen, J. H. 2014; 41 (2): 021908


    Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size.Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy.Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose.For iodine concentrations >5 mg/ml and detail size ∼ 20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).

    View details for DOI 10.1118/1.4863598

    View details for Web of Science ID 000331213300041

    View details for PubMedID 24506629

    View details for PubMedCentralID PMC3977791

  • 3D-2D registration for surgical guidance: effect of projection view angles on registration accuracy PHYSICS IN MEDICINE AND BIOLOGY Uneri, A., Otake, Y., Wang, A. S., Kleinszig, G., Vogt, S., Khanna, A. J., Siewerdsen, J. H. 2014; 59 (2): 271–87


    An algorithm for intensity-based 3D-2D registration of CT and x-ray projections is evaluated, specifically using single- or dual-projection views to provide 3D localization. The registration framework employs the gradient information similarity metric and covariance matrix adaptation evolution strategy to solve for the patient pose in six degrees of freedom. Registration performance was evaluated in an anthropomorphic phantom and cadaver, using C-arm projection views acquired at angular separation, Δθ, ranging from ∼0°-180° at variable C-arm magnification. Registration accuracy was assessed in terms of 2D projection distance error and 3D target registration error (TRE) and compared to that of an electromagnetic (EM) tracker. The results indicate that angular separation as small as Δθ ∼10°-20° achieved TRE <2 mm with 95% confidence, comparable or superior to that of the EM tracker. The method allows direct registration of preoperative CT and planning data to intraoperative fluoroscopy, providing 3D localization free from conventional limitations associated with external fiducial markers, stereotactic frames, trackers and manual registration.

    View details for DOI 10.1088/0031-9155/59/2/271

    View details for Web of Science ID 000332842000003

    View details for PubMedID 24351769

    View details for PubMedCentralID PMC4927006

  • Dual-Projection 3D-2D Registration for Surgical Guidance: Preclinical Evaluation of Performance and Minimum Angular Separation Uneri, A., Otake, Y., Wang, A. S., Kleinszig, G., Vogt, S., Gallia, G. L., Rigamonti, D., Wolinsky, J., Gokaslan, Z. L., Khanna, A. J., Siewerdsen, J. H., Yaniv, Z. R., Holmes, D. R. SPIE-INT SOC OPTICAL ENGINEERING. 2014

    View details for DOI 10.1117/12.2043561

    View details for Web of Science ID 000348029400084

  • Deformable Registration for Image-Guided Spine Surgery: Preserving Rigid Body Vertebral Morphology in Free-Form Transformations Reaungamornrat, S., Wang, A. S., Uneri, A., Otake, Y., Zhao, Z., Khanna, A. J., Siewerdsen, J. H., Yaniv, Z. R., Holmes, D. R. SPIE-INT SOC OPTICAL ENGINEERING. 2014

    View details for DOI 10.1117/12.2043474

    View details for Web of Science ID 000348029400027

  • Patient-Specific Minimum-Dose Imaging Protocols for Statistical Image Reconstruction in C-arm Cone-Beam CT Using Correlated Noise Injection Wang, A. S., Stayman, J. W., Otake, Y., Khanna, A. J., Gallia, G. L., Siewerdsen, J. H., Whiting, B. R., Hoeschen, C., Kontos, D. SPIE-INT SOC OPTICAL ENGINEERING. 2014

    View details for DOI 10.1117/12.2043083

    View details for Web of Science ID 000338775800058

  • Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation PHYSICS IN MEDICINE AND BIOLOGY Otake, Y., Wang, A. S., Stayman, J., Uneri, A., Kleinszig, G., Vogt, S., Khanna, A., Gokaslan, Z. L., Siewerdsen, J. H. 2013; 58 (23): 8535–53


    We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.

    View details for PubMedID 24246386

    View details for PubMedCentralID PMC4915373

  • Noise Reduction in Material Decomposition for Low-Dose Dual-Energy Cone-Beam CT Zbijewski, W., Gang, G., Wang, A. S., Stayman, J. W., Taguchi, K., Carrino, J. A., Siewerdsen, J. H., Nishikawa, R. M., Whiting, B. R., Hoeschen, C. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    View details for DOI 10.1117/12.2008431

    View details for Web of Science ID 000322002700041

  • Intraoperative Imaging for Patient Safety and QA: Detection of Intracranial Hemorrhage Using C-Arm Cone-Beam CT Schafer, S., Wang, A., Otake, Y., Stayman, J., Zbijewski, W., Kleinszig, G., Xia, X., Gallia, G. L., Siewerdsen, J. H., Holmes, D. R., Yaniv, Z. R. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    View details for DOI 10.1117/12.2008043

    View details for Web of Science ID 000321905800068

  • Soft-Tissue Imaging in Low-Dose, C-Arm Conbe-Beam CT Using Statistical Image Reconstruction Wang, A. S., Schafer, S., Stadyman, J., Otake, Y., Sussman, M. S., Khanna, A., Gallia, G. L., Siewerdsen, J. H., Nishikawa, R. M., Whiting, B. R., Hoeschen, C. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    View details for DOI 10.1117/12.2008421

    View details for Web of Science ID 000322002700047

  • Efficacy of Fixed Filtration for Rapid kVp-Switching Dual Energy X-ray Systems: Experimental Verification Conference on Medical Imaging - Physics of Medical Imaging Yao, Y., Wang, A. S., Pelc, N. J. SPIE-INT SOC OPTICAL ENGINEERING. 2012

    View details for DOI 10.1117/12.913222

    View details for Web of Science ID 000304768000047

  • A comparison of dual kV energy integrating and energy discriminating photon counting detectors for dual energy x-ray imaging Conference on Medical Imaging - Physics of Medical Imaging Wang, A. S., Pelc, N. J. SPIE-INT SOC OPTICAL ENGINEERING. 2012

    View details for DOI 10.1117/12.912030

    View details for Web of Science ID 000304768000029

  • Image-based Synthetic CT: simulating arbitrary low dose single and dual energy protocols from dual energy images Conference on Medical Imaging - Physics of Medical Imaging Wang, A. S., Feng, C., Pelc, N. J. SPIE-INT SOC OPTICAL ENGINEERING. 2012

    View details for DOI 10.1117/12.912163

    View details for Web of Science ID 000304768000048

  • Synthetic CT: Simulating low dose single and dual energy protocols from a dual energy scan MEDICAL PHYSICS Wang, A. S., Pelc, N. J. 2011; 38 (10): 5551-5562


    The choice of CT protocol can greatly impact patient dose and image quality. Since acquiring multiple scans at different techniques on a given patient is undesirable, the ability to predict image quality changes starting from a high quality exam can be quite useful. While existing methods allow one to generate simulated images of lower exposure (mAs) from an acquired CT exam, the authors present and validate a new method called synthetic CT that can generate realistic images of a patient at arbitrary low dose protocols (kVp, mAs, and filtration) for both single and dual energy scans.The synthetic CT algorithm is derived by carefully ensuring that the expected signal and noise are accurate for the simulated protocol. The method relies on the observation that the material decomposition from a dual energy CT scan allows the transmission of an arbitrary spectrum to be predicted. It requires an initial dual energy scan of the patient to either synthesize raw projections of a single energy scan or synthesize the material decompositions of a dual energy scan. The initial dual energy scan contributes inherent noise to the synthesized projections that must be accounted for before adding more noise to simulate low dose protocols. Therefore, synthetic CT is subject to the constraint that the synthesized data have noise greater than the inherent noise. The authors experimentally validated the synthetic CT algorithm across a range of protocols using a dual energy scan of an acrylic phantom with solutions of different iodine concentrations. An initial 80/140 kVp dual energy scan of the phantom provided the material decomposition necessary to synthesize images at 100 kVp and at 120 kVp, across a range of mAs values. They compared these synthesized single energy scans of the phantom to actual scans at the same protocols. Furthermore, material decompositions of a 100/120 kVp dual energy scan are synthesized by adding correlated noise to the initial material decompositions. The aforementioned noise constraint also allows us to compute feasible mAs values that can be synthesized for each kVp.The single energy synthesized and actual reconstructed images exhibit identical signal and noise properties at 100 kVp and at 120 kVp, and across a range of mAs values. For example, the noise in both the synthesized and actual images at 100 kVp increases by 2 when the mAs is halved. The synthesized and actual material decompositions of a dual energy protocol show excellent agreement when the decomposition images are linearly weighted to form monoenergetic images at energies from 40 to 100 keV. For simulated single energy protocols with kVp between 80 and 140, the highest feasible mAs exceeds that of either initial scan.This work describes and validates the synthetic CT theory and algorithm by comparing its results to actual scans. Synthetic CT is a powerful new tool that allows users to realistically see how protocol selection affects CT images and enables radiologists to retrospectively identify the lowest dose protocol achievable that provides diagnostic quality images on real patients.

    View details for DOI 10.1118/1.3633895

    View details for Web of Science ID 000295617400030

    View details for PubMedID 21992373

  • Pulse pileup statistics for energy discriminating photon counting x-ray detectors MEDICAL PHYSICS Wang, A. S., Harrison, D., Lobastov, V., Tkaczyk, J. E. 2011; 38 (7): 4265-4275


    Purpose: Energy discriminating photon counting x-ray detectors can be subject to a wide range of flux rates if applied in clinical settings. Even when the incident rate is a small fraction of the detector's maximum periodic rate No, pulse pileup leads to count rate losses and spectral distortion. Although the deterministic effects can be corrected, the detrimental effect of pileup on image noise is not well understood and may limit the performance of photon counting systems. Therefore, the authors devise a method to determine the detector count statistics and imaging performance.The detector count statistics are derived analytically for an idealized pileup model with delta pulses of a nonparalyzable detector. These statistics are then used to compute the performance (e.g., contrast-to-noise ratio) for both single material and material decomposition contrast detection tasks via the Cramdr-Rao lower bound (CRLB) as a function of the detector input count rate. With more realistic unipolar and bipolar pulse pileup models of a nonparalyzable detector, the imaging task performance is determined by Monte Carlo simulations and also approximated by a multinomial method based solely on the mean detected output spectrum. Photon counting performance at different count rates is compared with ideal energy integration, which is unaffected by count rate.The authors found that an ideal photon counting detector with perfect energy resolution outperforms energy integration for our contrast detection tasks, but when the input count rate exceeds 20% N0, many of these benefits disappear. The benefit with iodine contrast falls rapidly with increased count rate while water contrast is not as sensitive to count rates. The performance with a delta pulse model is overoptimistic when compared to the more realistic bipolar pulse model. The multinomial approximation predicts imaging performance very close to the prediction from Monte Carlo simulations. The monoenergetic image with maximum contrast-to-noise ratio from dual energy imaging with ideal photon counting is only slightly better than with dual kVp energy integration, and with a bipolar pulse model, energy integration outperforms photon counting for this particular metric because of the count rate losses. However, the material resolving capability of photon counting can be superior to energy integration with dual kVp even in the presence of pileup because of the energy information available to photon counting.A computationally efficient multinomial approximation of the count statistics that is based on the mean output spectrum can accurately predict imaging performance. This enables photon counting system designers to directly relate the effect of pileup to its impact on imaging statistics and how to best take advantage of the benefits of energy discriminating photon counting detectors, such as material separation with spectral imaging.

    View details for DOI 10.1118/1.3592932

    View details for Web of Science ID 000292521100043

    View details for PubMedID 21859028

  • Sufficient Statistics as a Generalization of Binning in Spectral X-ray Imaging IEEE TRANSACTIONS ON MEDICAL IMAGING Wang, A. S., Pelc, N. J. 2011; 30 (1): 84-93


    It is well known that the energy dependence of X-ray attenuation can be used to characterize materials. Yet, even with energy discriminating photon counting X-ray detectors, it is still unclear how to best form energy dependent measurements for spectral imaging. Common ideas include binning photon counts based on their energies and detectors with both photon counting and energy integrating electronics. These approaches can be generalized to energy weighted measurements, which we prove can form a sufficient statistic for spectral X-ray imaging if the weights used, which we term μ-weights, are basis attenuation functions that can also be used for material decomposition. To study the performance of these different methods, we evaluate the Cramér-Rao lower bound (CRLB) of material estimates in the presence of quantum noise. We found that the choice of binning and weighting schemes can greatly affect the performance of material decomposition. Even with optimized thresholds, binning condenses information but incurs penalties to decomposition precision and is not robust to changes in the source spectrum or object size, although this can be mitigated by adding more bins or removing photons of certain energies from the spectrum. On the other hand, because μ-weighted measurements form a sufficient statistic for spectral imaging, the CRLB of the material decomposition estimates is identical to the quantum noise limited performance of a system with complete energy information of all photons. Finally, we show that μ-weights lead to increased conspicuity over other methods in a simulated calcium contrast experiment.

    View details for DOI 10.1109/TMI.2010.2061862

    View details for Web of Science ID 000285844900008

    View details for PubMedID 20682470

  • Contrast-to-Noise of a Non-Ideal, Multi-bin, Photon Counting X-ray Detector Tkaczyk, J., Lobastov, V., Harrison, D. D., Wang, A. S., Pelc, N. J., Samei, E., Nishikawa, R. M. SPIE-INT SOC OPTICAL ENGINEERING. 2011

    View details for DOI 10.1117/12.878290

    View details for Web of Science ID 000294178500122

  • Synthetic CT: simulating arbitrary low dose single and dual energy protocols Conference on Medical Imaging 2011 - Physics of Medical Imaging Wang, A. S., Pelc, N. J. SPIE-INT SOC OPTICAL ENGINEERING. 2011

    View details for DOI 10.1117/12.878771

    View details for Web of Science ID 000294178500058

  • Impact of Photon Counting Detector Spectral Response on Dual Energy Techniques Conference on Medical Imaging - Physics of Medical Imaging Wang, A. S., Pelc, N. J. SPIE-INT SOC OPTICAL ENGINEERING. 2010

    View details for DOI 10.1117/12.840052

    View details for Web of Science ID 000285047200122

  • Understanding and controlling the effect of lossy raw data compression on CT images MEDICAL PHYSICS Wang, A. S., Pelc, N. J. 2009; 36 (8): 3643-3653


    The requirements for raw data transmission through a CT scanner slip ring, through the computation system, and for storage of raw CT data can be quite challenging as scanners continue to increase in speed and to collect more data per rotation. Although lossy compression greatly mitigates this problem, users must be cautious about how errors introduced manifest themselves in the reconstructed images. This paper describes two simple yet effective methods for controlling the effect of errors in raw data compression and describe the impact of each stage on the image errors. A CT system simulator (CATSIM, GE Global Research Center, Niskayuna, NY) was used to generate raw CT datasets that simulate different regions of human anatomy. The raw data are digitized by a 20-bit ADC and companded by a log compander. Lossy compression is performed by quantization and is followed by JPEG-LS (lossless), which takes advantage of the correlations between neighboring measurements in the sinogram. Error feedback, a previously proposed method that controls the spatial distribution of reconstructed image errors, and projection filtering, a newly proposed method that takes advantage of the filtered backprojection reconstruction process, are applied independently (and combined) to study their intended impact on the control and behavior of the additional noise due to the compression methods used. The log compander and the projection filtering method considerably reduce image error levels, while error feedback pushes image errors toward the periphery of the field of view. The results for the images are a compression ratio (CR) of 3 that keeps peak compression errors under 1 HU and a CR of 9 that increases image noise by only 1 HU in common CT applications. Lossy compression can substantially reduce raw CT data size at low computational cost. The proposed methods have the flexibility to operate at a wide range of compression ratios and produce predictable, object-independent, and often imperceptible image artifacts.

    View details for DOI 10.1118/1.3158738

    View details for Web of Science ID 000268440600029

    View details for PubMedID 19746798

  • Lossy raw data compression in computed tomography with noise shaping to control image effects Medical Imaging 2008 Conference Xie, Y., Wang, A. S., Pelc, N. J. SPIE-INT SOC OPTICAL ENGINEERING. 2008

    View details for DOI 10.1117/12.769954

    View details for Web of Science ID 000256660300105

  • Effect of the frequency content and spatial location of raw data errors on CT images MEDICAL IMAGING 2008: PHYSICS OF MEDICAL IMAGING, PTS 1-3 Wang, A. S., Xie, Y., Pelc, N. J. 2008; 6913

    View details for DOI 10.1117/12.770552

    View details for Web of Science ID 000256660300107

  • Detection of flicker caused by high-frequency interharmonics Kim, T., Wang, A., Powers, E. J., Grady, W., Arapostathis, A., IEEE IEEE. 2007: 336-+