Joseph Yuan-Chieh Lo

Radiology

Professor in Radiology

Joseph Yuan-Chieh Lo Profile Photo

Research Themes

Biomedical Imaging & Biophotonics

Education

Ph.D. Duke University, 1993

Trainings & Certifications

Research Associate, Radiology (1993 - 1995) Duke University

Positions

Professor in Radiology
Professor in the Department of Electrical and Computer Engineering
Professor of Biomedical Engineering
Member of the Duke Cancer Institute

Courses Taught

RROMP 301B: Radiology, Radiation Oncology & Medical Physics

Publications

Tushar FI, Vancoillie L, McCabe C, Kavuri A, Dahal L, Harrawood B, et al. Virtual Lung Screening Trial (VLST): An In Silico Replica of the National Lung Screening Trial for Lung Cancer Detection. ArXiv. 2024 Sep 24;
Ren Y, Liang Z, Ge J, Xu X, Go J, Nguyen DL, et al. Improving Computer-aided Detection for Digital Breast Tomosynthesis by Incorporating Temporal Change. Radiol Artif Intell. 2024 Sep;6(5):e230391.
Fortunato A, Mallo D, Cisneros L, King LM, Khan A, Curtis C, et al. Evolutionary Measures Show that Recurrence of DCIS is Distinct from Progression to Breast Cancer. 2024.
Nguyen DL, Ren Y, Jones TM, Thomas SM, Lo JY, Grimm LJ. Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies. Radiology. 2024 May;311(2):e232286.
Macdonald JA, Morgan KR, Konkel B, Abdullah K, Martin M, Ennis C, et al. A Method for Efficient De-identification of DICOM Metadata and Burned-in Pixel Text. J Imaging Inform Med. 2024 Apr 8;
Alaeikhanehshir S, Voets MM, van Duijnhoven FH, Lips EH, Groen EJ, van Oirsouw MCJ, et al. Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials. Cancer Imaging. 2024 Apr 5;24(1):48.
Donnelly J, Moffett L, Barnett AJ, Trivedi H, Schwartz F, Lo J, et al. AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction. Radiology. 2024 Mar;310(3):e232780.
Iyer M, Mallo D, Maley CC, Fortunato A, Cisneros L, King LM, et al. Abstract A038: Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data. In: Cancer Research. American Association for Cancer Research (AACR); 2024. p. A038–A038.
Hou R, Lo JY, Marks JR, Hwang ES, Grimm LJ. Classification performance bias between training and test sets in a limited mammography dataset. PLoS One. 2024;19(2):e0282402.
Kim D, Dahal L, Lo JY, Yeom YS, Kim CH, Segars WP. Random Walk Small Intestine Models for Virtual Patient Populations. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2024.
Yang J, Barnett AJ, Donnelly J, Kishore S, Fang J, Schwartz FR, et al. FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2024. p. 5003–9.
Liu X, Ren Y, Ryser M, Grimm LJ, Lo JY. A Residual-Attention Multimodal Fusion Network (ResAMF-Net) for Detection and Classification of Breast Cancer. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2024.
Tushar FI, Vancoillie L, McCabe C, Kavuri A, Dahal L, Harrawood B, et al. Virtual NLST: Towards Replicating National Lung Screening Trial. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2024.
Ren Y, Liu X, Ge J, Liang Z, Xu X, Grimm LJ, et al. Ipsilateral Lesion Detection Refinement for Tomosynthesis. IEEE Trans Med Imaging. 2023 Oct;42(10):3080–90.
Zhang D, Neely B, Lo JY, Patel BN, Hyslop T, Gupta RT. Utility of a Rule-Based Algorithm in the Assessment of Standardized Reporting in PI-RADS. Acad Radiol. 2023 Jun;30(6):1141–7.
Hou R, Lo JY, Marks JR, Hwang ES, Grimm LJ. Classification performance bias between training and test sets in a limited mammography dataset. medRxiv. 2023 Feb 23;
Ou YJ, Barnett AJ, Mitra A, Schwartz FR, Chen C, Grimm L, et al. A user interface to communicate interpretable AI decisions to radiologists. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2023.
Liu X, Ren Y, Liang Z, Grimm LJ, Ge J, Lo JY. Multi-view DBT Grid-Attention Detection Framework. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2023.
Dahal L, Wang Y, Tushar FI, Montero I, Lafata K, Abadi E, et al. Automatic quality control in computed tomography volumes segmentation using a small set of XCAT as reference images. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2023.
Hou R, Peng Y, Grimm LJ, Ren Y, Mazurowski MA, Marks JR, et al. Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases. IEEE Trans Biomed Eng. 2022 May;69(5):1639–50.
D’Anniballe VM, Tushar FI, Faryna K, Han S, Mazurowski MA, Rubin GD, et al. Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. BMC Med Inform Decis Mak. 2022 Apr 15;22(1):102.
Tong H, Pegues H, Samei E, Lo JY, Wiley BJ. Technical note: Controlling the attenuation of 3D-printed physical phantoms for computed tomography with a single material. Med Phys. 2022 Apr;49(4):2582–9.
Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features. Radiology. 2022 Apr;303(1):54–62.
Tushar FI, Abadi E, Sotoudeh-Paima S, Fricks RB, Mazurowski MA, Segars WP, et al. Virtual vs. reality: External validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Tushar FI, D’Anniballe VM, Hou R, Mazurowski MA, Fu W, Samei E, et al. Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning. Radiol Artif Intell. 2022 Jan;4(1):e210026.
Barnett AJ, Sharma V, Gajjar N, Fang J, Schwartz FR, Chen C, et al. Interpretable Deep Learning Models for Better Clinician-AI Communication in Clinical Mammography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Fu W, Sharma S, Abadi E, Iliopoulos A-S, Wang Q, Lo JY, et al. Corrections to "iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and its Application to CT Organ Dosimetry". IEEE J Biomed Health Inform. 2022 Jan;26(1):478.
Tushar FI, Danniballe VM, Rubin GD, Samei E, Lo JY. Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Tushar FI, Nujaim H, Fu W, Abadi E, Mazurowski MA, Segars WP, et al. Quality or quantity: toward a unified approach for multi-organ segmentation in body CT. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Dahal L, Tushar FI, Abadi E, Fricks RB, Mazurowski M, Segars WP, et al. Virtual versus reality: external validation of COVID-19 classifiers using XCAT phantoms for chest radiography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2022.
Barnett AJ, Schwartz FR, Tao C, Chen C, Ren Y, Lo JY, et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nature Machine Intelligence. 2021 Dec 1;3(12):1061–70.
Fortunato A, Mallo D, Rupp SM, King LM, Hardman T, Lo JY, et al. A new method to accurately identify single nucleotide variants using small FFPE breast samples. Brief Bioinform. 2021 Nov 5;22(6).
Buda M, Saha A, Walsh R, Ghate S, Li N, Swiecicki A, et al. A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. JAMA Netw Open. 2021 Aug 2;4(8):e2119100.
Fu W, Sharma S, Abadi E, Iliopoulos A-S, Wang Q, Lo JY, et al. iPhantom: A Framework for Automated Creation of Individualized Computational Phantoms and Its Application to CT Organ Dosimetry. IEEE J Biomed Health Inform. 2021 Aug;25(8):3061–72.
Xue C, Tang F-H, Lai CWK, Grimm LJ, Lo JY. Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change. Life (Basel). 2021 Jul 26;11(8).
Grimm LJ, Neely B, Hou R, Selvakumaran V, Baker JA, Yoon SC, et al. Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography. AJR Am J Roentgenol. 2021 Apr;216(4):903–11.
Barnett AJ, Schwartz FR, Tao C, Chen C, Ren Y, Lo JY, et al. IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. 2021 Mar 23;
Ikejimba LC, Salad J, Graff CG, Goodsitt M, Chan H-P, Huang H, et al. Assessment of task-based performance from five clinical DBT systems using an anthropomorphic breast phantom. Med Phys. 2021 Mar;48(3):1026–38.
D’Anniballe VM, Tushar FI, Faryna K, Han S, Mazurowski MA, Rubin GD, et al. Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning. 2021 Feb 4;
Draelos RL, Dov D, Mazurowski MA, Lo JY, Henao R, Rubin GD, et al. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Med Image Anal. 2021 Jan;67:101857.
Fu W, Segars PW, Sharma S, Lo JY, Samei E. IPhantom: An automated framework in generating personalized computational phantoms for organ-based radiation dosimetry. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2021.
Draelos RL, Dov D, Mazurowski MA, Lo JY, Henao R, Rubin GD, et al. Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes. Medical Image Anal. 2021;67:101857–101857.
Ren Y, Lu J, Liang Z, Grimm LJ, Kim C, Taylor-Cho M, et al. Retina-Match: Ipsilateral Mammography Lesion Matching in a Single Shot Detection Pipeline. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2021. p. 345–54.
Buda M, Saha A, Walsh R, Ghate S, Li N, Święcicki A, et al. Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. JAMA Netw Open 2021;4(8):e2119100. 2020 Nov 13;
Selvakumaran V, Hou R, Baker JA, Yoon SC, Ghate SV, Walsh R, et al. Predicting Upstaging of DCIS to Invasive Disease: Radiologists's Predictive Performance. Acad Radiol. 2020 Nov;27(11):1580–5.
MacDonald LR, Lo JY, Sturgeon GM, Zeng C, Harrison RL, Kinahan PE, et al. Impact of Using Uniform Attenuation Coefficients for Heterogeneously Dense Breasts in a Dedicated Breast PET/X-ray Scanner. IEEE Trans Radiat Plasma Med Sci. 2020 Sep;4(5):585–93.
Fu W, Sharma S, Abadi E, Iliopoulos A-S, Wang Q, Lo JY, et al. iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry. 2020 Aug 19;
Abadi E, Segars WP, Tsui BMW, Kinahan PE, Bottenus N, Frangi AF, et al. Virtual clinical trials in medical imaging: a review. J Med Imaging (Bellingham). 2020 Jul;7(4):042805.
Hou R, Mazurowski MA, Grimm LJ, Marks JR, King LM, Maley CC, et al. Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation. IEEE Trans Biomed Eng. 2020 Jun;67(6):1565–72.
Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open. 2020 Mar 2;3(3):e200265.
Vancoillie L, Cockmartin L, Marshall NW, Lo JY, Bosmans H. Evaluation of possible phantoms for assessment of image quality in synthetic mammograms. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Faryna K, Tushar FI, D’Anniballe VM, Hou R, Rubin GD, Lo JY. Attention-guided classification of abnormalities in semi-structured computed tomography reports. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Saha A, Tushar FI, Faryna K, D’Anniballe VM, Hou R, Mazurowski MA, et al. Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Ikejimba LC, Salad J, Graff CG, Goodsitt M, Chan HP, Zhao W, et al. Assessment of task-based performance from five clinical DBT systems using an anthropomorphic breast phantom. In: Proceedings of SPIE - The International Society for Optical Engineering. 2020.
Samei E, Abadi E, Kapadia A, Lo J, Mazurowski M, Segars P. Virtual imaging trials: An emerging experimental paradigm in imaging research and practice. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Peng Y, Hou R, Ren Y, Grimm LJ, Marks JR, Hwang ES, et al. Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Hou R, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Pegues H, Tong H, Wiley BJ, Samei E, Lo JY. CT phantom with 3D anthropomorphic, contrast-enhanced texture. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2020.
Ikejimba LC, Salad J, Graff CG, Ghammraoui B, Cheng W-C, Lo JY, et al. A four-alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet-printed anthropomorphic phantom. Med Phys. 2019 Sep;46(9):3883–92.
Ren Y, Zhu Z, Li Y, Lo J. Mask Embedding in conditional GAN for Guided Synthesis of High Resolution Images. 2019 Jul 2;
Grimm LJ, Miller MM, Thomas SM, Liu Y, Lo JY, Hwang ES, et al. Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease. Radiology. 2019 Jul;292(1):77–83.
Georgian-Smith D, Obuchowski NA, Lo JY, Brem RF, Baker JA, Fisher PR, et al. Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis. AJR Am J Roentgenol. 2019 Jun;212(6):1393–9.
Samei E, Lo J. Special Section Guest Editorial: Special Section on 3D Printing in Medical Imaging. Journal of Medical Imaging. 2019 Apr 1;6(2).
Rossman AH, Catenacci M, Zhao C, Sikaria D, Knudsen JE, Dawes D, et al. Three-dimensionally-printed anthropomorphic physical phantom for mammography and digital breast tomosynthesis with custom materials, lesions, and uniform quality control region. J Med Imaging (Bellingham). 2019 Apr;6(2):021604.
Pegues H, Knudsen J, Tong H, Gehm ME, Wiley BJ, Samei E, et al. Using inkjet 3D printing to create contrast-enhanced textured physical phantoms for CT. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Ren Y, Zhu Z, Li Y, Kong D, Hou R, Grimm LJ, et al. Mask Embedding for Realistic High-Resolution Medical Image Synthesis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2019. p. 422–30.
Benitez M, Tian J, Kelly M, Selvakumaran V, Phelan M, Mazurowski M, et al. Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Tang R, Tushar FI, Han S, Hou R, Rubin GD, Lo JY. Classification of chest CT using case-level weak supervision. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Han S, Tian J, Kelly M, Selvakumaran V, Henao R, Rubin GD, et al. Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Fu W, Sharma S, Smith T, Hou R, Abadi E, Selvakumaran V, et al. Multi-organ segmentation in clinical-computed tomography for patient-specific image quality and dose metrology. In: Medical Imaging 2019: Physics of Medical Imaging. 2019. p. 1094829–1094829.
Tong H, Pegues H, Yang F, Samei E, Lo JY, Wiley BJ. Controlling the position-dependent contrast of 3D printed physical phantoms with a single material. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Liu Y, Fu W, Selvakumaran V, Phelan M, Segars WP, Samei E, et al. Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Geng Y, Ren Y, Hou R, Han S, Rubin GD, Lo JY. 2.5D CNN model for detecting lung disease using weak supervision. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Kong D, Ren Y, Hou R, Grimm LJ, Marks JR, Lo JY. Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Ren Y, Hou R, Kong D, Geng Y, Grimm LJ, Marks JR, et al. Multiview mammographic mass detection based on a single shot detection system. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019.
Hou R, Ren Y, Grimm LJ, Mazurowski MA, Marks JR, King L, et al. Malignant microcalcification clusters detection using unsupervised deep autoencoders. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2019 Jan 1;10950.
Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, et al. Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. J Am Coll Radiol. 2018 Mar;15(3 Pt B):527–34.
Lo JY, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, et al. Prediction of occult invasive disease in ductal carcinoma in situ using deep learning features. In: CANCER RESEARCH. AMER ASSOC CANCER RESEARCH; 2018.
Ikejimba LC, Salad J, Kemp K, Graff CG, Ghammraoui B, Lo JY, et al. Method for task-based evaluation of clinical FFDM and DBT systems using an anthropomorphic breast phantom. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Rossman A, Catenacci M, Li AM, Sauer TJ, Solomon J, Gehm ME, et al. 3D printed anthropomorphic physical phantom for mammography and DBT with high contrast custom materials, lesions, and uniform chest wall region. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Wei Q, Ren Y, Hou R, Shi B, Lo JY, Carin L. Anomaly detection for medical images based on a one-class classification. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Hou R, Shi B, Grimm LJ, Mazurowski MA, Marks JR, King LM, et al. Improving classification with forced labeling of other related classes: Application to prediction of upstaged ductal carcinoma in situ using mammographic features. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Wen G, Chang H-C, Reinhold J, Lo JY, Markey MK. Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images. J Med Imaging (Bellingham). 2018 Jan;5(1):015501.
Shi B, Hou R, Mazurowski MA, Grimm LJ, Ren Y, Marks JR, et al. Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Ikejimba LC, Yan T, Kemp K, Salad J, Graff CG, Ghammraoui B, et al. Methodology for the objective assessment of lesion detection performance with breast tomosynthesis and digital mammography using a physical anthropomorphic phantom. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Rajagopal J, Sturgeon GM, Chen XC, Sauer TJ, Ren Y, Segars WP, et al. Evaluation of statistical breast phantoms with higher resolution. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2018.
Sturgeon GM, Park S, Segars WP, Lo JY. Synthetic breast phantoms from patient based eigenbreasts. Med Phys. 2017 Dec;44(12):6270–9.
Shi B, Grimm LJ, Mazurowski MA, Baker JA, Marks JR, King LM, et al. Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features? Acad Radiol. 2017 Sep;24(9):1139–47.
Ikejimba LC, Graff CG, Rosenthal S, Badal A, Ghammraoui B, Lo JY, et al. A novel physical anthropomorphic breast phantom for 2D and 3D x-ray imaging. Med Phys. 2017 Feb;44(2):407–16.
Ikejimba LC, Graff CG, Rosenthal S, Badal A, Ghammraoui B, Lo JY, et al. A physical breast phantom for 2D and 3D x-ray imaging made through inkjet printing. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Shi B, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Sauer TJ, Graff CG, Zeng R, Santana M, Sturgeon GM, Bosmans H, et al. Detectability of artificial lesions in anthropomorphic virtual breast phantoms of variable glandular fraction. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Zhao C, Solomon J, Sturgeon GM, Gehm ME, Catenacci M, Wiley BJ, et al. Third generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and uniform chest wall QC region. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Reinhold J, Wen G, Lo JY, Markey MK. Lesion detectability in stereoscopically viewed digital breast tomosynthesis projection images: A model observer study with anthropomorphic computational breast phantoms. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Zhao A, Santana M, Samei E, Lo J. Comparison of effects of dose on image quality in digital breast tomosynthesis across multiple vendors. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Chen X, Gong X, Graff CG, Santana M, Sturgeon GM, Sauer TJ, et al. High-resolution, anthropomorphic, computational breast phantom: Fusion of rule-based structures with patient-based anatomy. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Robinson MD, Chiu SJ, Toth CA, Izatt JA, Lo JY, Farsiu S. New applications of super-resolution in medical imaging. In: Super-Resolution Imaging. 2017. p. 383–412.
Shi B, Grimm LJ, Mazurowski MA, Marks JR, King LM, Maley CC, et al. Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features? In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2017.
Ikejimba LC, Glick SJ, Choudhury KR, Samei E, Lo JY. Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms. Med Phys. 2016 Oct;43(10):5593.
Wang M, Zhang J, Grimm LJ, Ghate SV, Walsh R, Johnson KS, et al. Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach. Expert Systems with Applications. 2016 Sep 1;56:1–8.
Kiarashi N, Nolte LW, Lo JY, Segars WP, Ghate SV, Solomon JB, et al. Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study. J Med Imaging (Bellingham). 2016 Jul;3(3):035504.
Sturgeon GM, Kiarashi N, Lo JY, Samei E, Segars WP. Finite-element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation. Med Phys. 2016 May;43(5):2207.
Ikejimba L, Lo JY, Chen Y, Oberhofer N, Kiarashi N, Samei E. A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom. Med Phys. 2016 Apr;43(4):1627.
Grimm LJ, Zhang J, Lo JY, Johnson KS, Ghate SV, Walsh R, et al. Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns. J Am Coll Radiol. 2016 Feb;13(2):198–202.
Sturgeon GM, Tward DJ, Ketcha M, Ratnanather JT, Miller MI, Park S, et al. Eigenbreasts for statistical breast phantoms. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Sikaria D, Musinsky S, Sturgeon GM, Solomon J, Diao A, Gehm ME, et al. Second generation anthropomorphic physical phantom for mammography and DBT: Incorporating voxelized 3D printing and inkjet printing of iodinated lesion inserts. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Makeev A, Ikejimba L, Lo JY, Glick SJ. Investigation of optimal parameters for penalized maximum-likelihood reconstruction applied to iodinated contrast-enhanced breast CT. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Wang M, Zhang J, Grimm LJ, Ghate SV, Walsh R, Johnson KS, et al. Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Erickson DW, Wells JR, Sturgeon GM, Samei E, Dobbins JT, Segars WP, et al. Population of 224 realistic human subject-based computational breast phantoms. Med Phys. 2016 Jan;43(1):23.
Ikejimba L, Glick SJ, Samei E, Lo JY. Comparison of model and human observer performance in FFDM, DBT, and synthetic mammography. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Solomon J, Ba A, Diao A, Lo J, Bier E, Bochud F, et al. Design, fabrication, and implementation of voxel-based 3D printed textured phantoms for task-based image quality assessment in CT. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2016.
Schmidt M, Lo JY, Grzetic S, Lutzky C, Brizel DM, Das SK. Semiautomated head-and-neck IMRT planning using dose warping and scaling to robustly adapt plans in a knowledge database containing potentially suboptimal plans. Med Phys. 2015 Aug;42(8):4428–34.
Kiarashi N, Nolte AC, Sturgeon GM, Segars WP, Ghate SV, Nolte LW, et al. Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data. Med Phys. 2015 Jul;42(7):4116–26.
Zhang J, Grimm LJ, Lo JY, Johnson KS, Ghate SV, Walsh R, et al. Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis? J Am Coll Radiol. 2015 Jul;12(7):728–32.
Grimm LJ, Zhang J, Johnson KS, Lo JY, Mazurowski MA. Incorporating breast tomosynthesis into radiology residency: Does trainee experience in breast imaging translate into improved performance with this new modality? In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2015.
Ikejimba L, Chen Y, Oberhofer N, Kiarashi N, Lo JY, Samei E. A quantitative metrology for performance characterization of breast tomosynthesis systems based on an anthropomorphic phantom. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2015.
Kiarashi N, Nolte LW, Lo JY, Segars WP, Ghate SV, Samei E. The impact of breast structure on lesion detection in breast tomosynthesis. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2015.
Sechopoulos I, Sabol JM, Berglund J, Bolch WE, Brateman L, Christodoulou E, et al. Radiation dosimetry in digital breast tomosynthesis: report of AAPM Tomosynthesis Subcommittee Task Group 223. Med Phys. 2014 Sep;41(9):091501.
Zhang J, Lo JY, Kuzmiak CM, Ghate SV, Yoon SC, Mazurowski MA. Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents. Med Phys. 2014 Sep;41(9):091907.
Kiarashi N, Lo JY, Lin Y, Ikejimba LC, Ghate SV, Nolte LW, et al. Development and application of a suite of 4-D virtual breast phantoms for optimization and evaluation of breast imaging systems. IEEE Trans Med Imaging. 2014 Jul;33(7):1401–9.
Ikejimba LC, Kiarashi N, Ghate SV, Samei E, Lo JY. Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis. In: Med Phys. 2014. p. 061908.
Segars WP, Veress AI, Wells JR, Sturgeon GM, Kiarashi N, Lo JY, et al. Population of 100 realistic, patient-based computerized breast phantoms for multi-modality imaging research. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2014.
Mahadevan R, Ikejimba LC, Lin Y, Samei E, Lo JY. A task-based comparison of two reconstruction algorithms for digital breast tomosynthesis. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2014.
Nolte A, Kiarashi N, Samei E, Segars WP, Lo JY. A second generation of physical anthropomorphic 3D breast phantoms based on human subject data. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2014.
Mazurowski MA, Zhang J, Lo JY, Kuzmiak CM, Ghate SV, Yoon S. Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2014.
Good D, Lo J, Lee WR, Wu QJ, Yin F-F, Das SK. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. Int J Radiat Oncol Biol Phys. 2013 Sep 1;87(1):176–81.
Huo Z, Summers RM, Paquerault S, Lo J, Hoffmeister J, Armato SG, et al. Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use. Med Phys. 2013 Jul;40(7):077001.
Chung H, Ikejimba L, Kiarashi N, Samei E, Hoernig M, Lo JY. Estimating breast density with dual energy mammography: A simple model based on calibration phantoms. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2013 Jun 3;8668.
Kiarashi N, Sturgeon GM, Nolte LW, Lo JY, III JTD, Segars WP, et al. Development of matched virtual and physical breast phantoms based on patient data. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2013.
Kiarashi N, Ghate SV, Lo JY, Nolte LW, Samei E. Application of a dynamic 4D anthropomorphic breast phantom in contrast-based imaging system optimization: Dual-energy or temporal subtraction? Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2012 Aug 15;7361 LNCS:658–65.
Chanyavanich V, Lo J, Das S. SU-E-T-572: A Plan Quality Metric for Evaluating Knowledge-Based Treatment Plans. In: Med Phys. 2012. p. 3837.
Dick D, Das S, Lo J. WE-G-BRCD-06: Knowledge-Based Intensity Modulated Radiotherapy (IMRT) Treatment Planning for Prostate Cancer. In: Med Phys. 2012. p. 3965–6.
Lin Y, Ghate S, Lo J, Samei E. 3D biopsy for tomosynthesis: Simulation of prior information based reconstruction for dose and artifact reduction. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2012.
Kiarashi N, Lin Y, Segars WP, Ghate SV, Ikejimba L, Chen B, et al. Development of a dynamic 4D anthropomorphic breast phantom for contrast-based breast imaging. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2012.
Ikejimba L, Kiarashi N, Lin Y, Chen B, Ghate SV, Zerhouni M, et al. Task-based strategy for optimized contrast enhanced breast imaging: analysis of six imaging techniques for mammography and tomosynthesis. In: Pelc NJ, Nishikawa RM, Whiting BR, editors. SPIE Proceedings. SPIE; 2012. p. 831309–831309.
Mazurowski MA, Lo JY, Harrawood BP, Tourassi GD. Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis. J Biomed Inform. 2011 Oct;44(5):815–23.
Baker JA, Lo JY. Breast tomosynthesis: state-of-the-art and review of the literature. Acad Radiol. 2011 Oct;18(10):1298–310.
Shafer CM, Seewaldt VL, Lo JY. Validation of a 3D hidden-Markov model for breast tissue segmentation and density estimation from MR and tomosynthesis images. Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011. 2011 Jul 7;
Shafer CM, Seewaldt VL, Lo JY. Segmentation of adipose and glandular tissue for breast tomosynthesis imaging using a 3D hidden-Markov model trained on breast MRIs. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2011.
Chanyavanich V, Das SK, Lee WR, Lo JY. Knowledge-based IMRT treatment planning for prostate cancer. Med Phys. 2011 May;38(5):2515–22.
Webb LJ, Samei E, Lo JY, Baker JA, Ghate SV, Kim C, et al. Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection. Med Phys. 2011 Apr;38(4):1972–80.
Singh S, Maxwell J, Baker JA, Nicholas JL, Lo JY. Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. Radiology. 2011 Jan;258(1):73–80.
Chanyavanich V, Das S, lo J. SU‐E‐T‐851: An Inter‐Institutional Comparison of Knowledge‐Based IMRT Treatment Planning for Prostate Cancer. In: Medical Physics. 2011. p. 3687.
Mazurowski MA, Lo JY, Tourassi GD. User modeling for improved computer-aided training in radiology: Initial experience. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2010.
Robinson MD, Toth CA, Lo JY, Farsiu S. Efﬁcient fourier-wavelet super-resolution. IEEE Trans Image Process. 2010 Oct;19(10):2669–81.
Ranger NT, Lo JY, Samei E. A technique optimization protocol and the potential for dose reduction in digital mammography. Med Phys. 2010 Mar;37(3):962–9.
Shafer CM, Samei E, Lo JY. The quantitative potential for breast tomosynthesis imaging. Med Phys. 2010 Mar;37(3):1004–16.
lo JY, Baydush AH, Karim E, Mendonca SJ. WE‐A‐201B‐04: Reducing Dose in Breast Tomosynthesis Using Bayesian Image Estimation. In: Medical Physics. 2010. p. 3413.
Mehtaji D, Shafer C, Chen B, lo J. SU‐GG‐I‐154: Evaluation of Quantitative Potential of Breast Tomosynthesis Using a Voxelized Anthropomorphic Breast Phantom. In: Medical Physics. 2010. p. 3137.
Freeman MS, Chanyavanich V, Das SK, lo JY. SU‐GG‐T‐131: A Linear Metric of Knowledge‐Based IMRT Treatment Plan Quality for the Prostate. In: Medical Physics. 2010. p. 3214.
Chanyavanich V, Freeman M, Das S, lo J. SU‐GG‐T‐134: Knowledge‐Based IMRT Treatment Planning for Prostate Cancer. In: Medical Physics. 2010. p. 3215.
Chawla AS, Lo JY, Baker JA, Samei E. Optimized image acquisition for breast tomosynthesis in projection and reconstruction space. Med Phys. 2009 Nov;36(11):4859–69.
Chawla AS, Samei E, Lo JY. Optimized lesion detection in breast tomosynthesis. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2009.
Li CM, Segars WP, Lo JY, Veress AI, Boone JM, Dobbins JT. Computerized 3D breast phantom with enhanced High-Resolution detail. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2009.
Saunders RS, Samei E, Lo JY, Baker JA. Can compression be reduced for breast tomosynthesis? Monte carlo study on mass and microcalcification conspicuity in tomosynthesis. Radiology. 2009 Jun;251(3):673–82.
Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, et al. Do serum biomarkers really measure breast cancer? BMC Cancer. 2009 May 28;9:164.
Chawla AS, Saunders RS, Singh S, Lo JY, Samei E. Towards optimized acquisition scheme for multiprojection correlation imaging of breast cancer. Acad Radiol. 2009 Apr;16(4):456–63.
Shafer C, lo J, Samei E. MO‐FF‐A4‐01: Evaluation of Background Trend Correction Technique in Breast Tomosynthesis Quantitation. In: Medical Physics. 2009. p. 2713.
Robinson MD, Farsiu S, Lo JY, Toth CA. Efficient restoration and enhancement of super-resolved X-ray images. In 2008. p. 629–32.
Xia JQ, Lo JY. Mass detectability in dedicated breast CT: A simulation study with the application of volume noise removal. In: 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008. 2008.
Chawla AS, Samei E, Lo JY, Mertelmeier T. Multi-projection correlation imaging as a new diagnostic tool for improved breast cancer detection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 635–42.
Tourassi GD, Sharma AC, Singh S, Saunders RS, Lo JY, Samei E, et al. Knowledge transfer across breast cancer screening modalities: A pilot study using an information-theoretic CADe system for mass detection. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 292–8.
Shafer CM, Samei E, Mertelmeier T, Saunders RS, Zerhouni M, Lo JY. Assessment of low energies and slice depth in the quantification of breast tomosynthesis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 530–6.
Singh S, Tourassi GD, Lo JY. Effect of similarity metrics and ROI sizes in featureless computer aided detection of breast masses in tomosynthesis. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2008. p. 286–91.
Singh S, Tourassi GD, Baker JA, Samei E, Lo JY. Automated breast mass detection in 3D reconstructed tomosynthesis volumes: a featureless approach. Med Phys. 2008 Aug;35(8):3626–36.
Singh S, Tourassi GD, Chawla AS, Saunders RS, Samei E, Lo JY. Computer aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2008.
Chawla AS, Samei E, Saunders RS, Lo JY, Singh S. Optimized acquisition scheme for multi-projection correlation imaging of breast cancer. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2008.
Williams MB, Raghunathan P, More MJ, Seibert JA, Kwan A, Lo JY, et al. Optimization of exposure parameters in full field digital mammography. Med Phys. 2008 Jun;35(6):2414–23.
Shafer CM, Samei E, Saunders RS, Zerhouni M, Lo JY. Toward quantification of breast tomosynthesis imaging. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2008.
Li CM, Segars WP, Lo JY, Veress AI, Boone JM, Dobbins JT. Three-dimensional computer generated breast phantom based on empirical data. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2008.
Floyd CE, Kapadia AJ, Bender JE, Sharma AC, Xia JQ, Harrawood BP, et al. Neutron-stimulated emission computed tomography of a multi-element phantom. Phys Med Biol. 2008 May 7;53(9):2313–26.
Xia JQ, Lo JY, Yang K, Floyd CE, Boone JM. Dedicated breast computed tomography: volume image denoising via a partial-diffusion equation based technique. Med Phys. 2008 May;35(5):1950–8.
Chawla AS, Samei E, Saunders RS, Lo JY, Baker JA. A mathematical model platform for optimizing a multiprojection breast imaging system. Med Phys. 2008 Apr;35(4):1337–45.
Karellas A, Lo JY, Orton CG. Point/Counterpoint. Cone beam x-ray CT will be superior to digital x-ray tomosynthesis in imaging the breast and delineating cancer. Med Phys. 2008 Feb;35(2):409–11.
Chen Y, Lo JY, Dobbins JT. Impulse response and Modulation Transfer Function analysis for Shift-And-Add and Back Projection image reconstruction algorithms in Digital Breast Tomosynthesis (DBT). Int J Funct Inform Personal Med. 2008;1(2):189–204.
Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 2008;21(2–3):427–36.
Robinson MD, Farsiu S, Lo JY, Toth CA. Efficient Restoration and Enhancement of Super-resolved X-ray Images. In: 2008 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, PROCEEDINGS. IEEE; 2008. p. 633-+.
lo JY. TU‐D‐342‐02: What Every Medical Physicist Should Know About Breast Tomosynthesis. In: Medical Physics. 2008. p. 2908.
Robinson MD, Farsiu S, Lo JY, Milanfar P, Toth CA. Efficient registration of aliased x-ray images. Conference Record - Asilomar Conference on Signals, Systems and Computers. 2007. p. 215–9.
Chen Y, Lo JY, Dobbins JT. A comparison between traditional shift-and-add (SAA) and point-by-point back projection (BP) - Relevance to morphology of microcalcifications for isocentric motion in Digital Breast tomosynthesis (DBT). Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007 Dec 1;563–9.
Jesneck JL, Mukherjee S, Nolte LW, Lokshin AE, Marks JR, Lo J. Decision fusion of circulating markers for breast cancer detection in premenopausal women. In 2007. p. 1434–8.
Singh S, Tourassi GD, Lo JY. Breast mass detection in tomosynthesis projection images using information-theoretic similarity measures. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2007 Oct 18;6514(PART 1).
Saunders RS, Samei E, Majdi-Nasab N, Lo JY. Initial human subject results for breast Bi-plane correlation imaging technique. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2007.
Jerebko A, Quan Y, Merlet N, Ratner E, Singh S, Lo JY, et al. Feasibility study of breast tomosynthesis CAD system. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2007.
Xia JQ, Tourassi GD, Lo JY, Floyd CE. On the development of a Gaussian noise model for scatter compensation. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2007.
Johnson JP, Lo J, Mertelmeier T, Nafziger JS, Timberg P, Samei E. Visual image quality metrics for optimization of breast tomosynthesis acquisition technique. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2007.
Chen Y, Lo JY, Ranger NT, Samei E, Dobbins JT. Methodology of NEQ (f) analysis for optimization and comparison of digital breast tomosynthesis acquisition techniques and reconstruction algorithms. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2007.
Chen Y, Lo JY, Dobbins JT. Importance of point-by-point back projection correction for isocentric motion in digital breast tomosynthesis: relevance to morphology of structures such as microcalcifications. Med Phys. 2007 Oct;34(10):3885–92.
Tourassi GD, Harrawood B, Singh S, Lo JY. Information-theoretic CAD system in mammography: entropy-based indexing for computational efficiency and robust performance. Med Phys. 2007 Aug;34(8):3193–204.
Jesneck JL, Lo JY, Baker JA. Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors. Radiology. 2007 Aug;244(2):390–8.
Baydush AH, Catarious DM, Lo JY, Floyd CE. Incorporation of a Laguerre-Gauss channelized Hotelling observer for false-positive reduction in a mammographic mass CAD system. J Digit Imaging. 2007 Jun;20(2):196–202.
Samei E, Stebbins SA, Dobbins JT, McAdams HP, Lo JY. Multiprojection correlation imaging for improved detection of pulmonary nodules. AJR Am J Roentgenol. 2007 May;188(5):1239–45.
Patterson K, Vasek J, Yuan CM, Bailey GE, Kusnadi I, Do T, et al. Circuit-based SEM contour OPC model calibration. Wong AKK, Singh VK, editors. SPIE Proceedings. 2007 Mar 16;6521:65211N-65211N.
Williams M, Raghunathan P, Seibert JA, Kwan A, lo J, Samei E, et al. TU‐B‐M100J‐01: Optimizing Mammography Image Quality and Dose: X‐Ray Spectrum and Exposure Parameter Selection. In: Medical Physics. 2007. p. 2540–1.
lo JY, Singh S, Dobbins JT, Samei E. MO‐D‐L100F‐03: New Developments in Digital Breast Tomosynthesis. In: Medical Physics. 2007. p. 2518.
Tourassi GD, Harrawood B, Singh S, Lo JY, Floyd CE. Evaluation of information-theoretic similarity measures for content-based retrieval and detection of masses in mammograms. Med Phys. 2007 Jan;34(1):140–50.
Floyd CE, Sharma AC, Bender JE, Kapadia AJ, Xia JQ, Harrawood BP, et al. Neutron stimulated emission computed tomography: Background corrections. Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms. 2007 Jan 1;254(2):329–36.
Jesneck JL, Nolte LW, Baker JA, Floyd CE, Lo JY. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis. Med Phys. 2006 Aug;33(8):2945–54.
Floyd CE, Bender JE, Sharma AC, Kapadia A, Xia J, Harrawood B, et al. Introduction to neutron stimulated emission computed tomography. Phys Med Biol. 2006 Jul 21;51(14):3375–90.
Sharma A, Floyd C, Harrawood B, Tourassi G, Kapadia A, Bender J, et al. Rotating slat collimator design for high-energy near-field imaging. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2006 Jul 3;6142 I.
Chen Y, Lo JY, Baker JA, Dobbins JT. Gaussian frequency blending algorithm with Matrix Inversion Tomosynthesis (MITS) and Filtered Back Projection (FBP) for better digital breast tomosynthesis reconstruction. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2006 Jun 30;6142 I.
Chen Y, Lo JY, Dobbins JT. Noise power spectrum analysis for several digital breast tomosynthesis reconstruction algorithms. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2006 Jun 30;6142 III.
Floyd CE, Bender JE, Harrawood B, Sharma AC, Kapadia A, Tourassi GD, et al. Breast cancer diagnosis using neutron stimulated emission computed tomography: Dose and count requirements. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2006 Jun 30;6142 II.
Jesneck JL, Nolte LW, Baker JA, Lo JY. The effect of data set size on computer-aided diagnosis of breast cancer: Comparing decision fusion to a linear discriminant. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2006 Jun 23;6146.
Singh S, Baydush A, Harrawood B, Lo J. Mass detection in mammographic ROIs using Watson filters. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2006 Jun 22;6146.
Land WH, McKee DW, Anderson FR, Masters T, Lo JY, Embrechts M, et al. Using computational intelligence for computer-aided diagnosis of screen-film mammograms. In: Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer. 2006. p. 315–75.
Lo JY, Bilska-Wolak AO, Baker JA, Tourassi GD, Floyd CE, Markey MK. Computer-aided diagnosis in breast imaging: Where do we go after detection? In: Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of Breast Cancer. 2006. p. 871–900.
Williams MB, Raghunathan P, Seibert A, Kwan A, Lo J, Samei E, et al. Beam optimization for digital mammography - II. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006 Jan 1;4046 LNCS:273–80.
Chen Y, Lo J, Baker J, Dobbins J. SU‐FF‐I‐21: Two‐Dimensional Shift‐And‐Add (SAA) Algorithm for Digital Breast Tomosynthesis Reconstruction. In: Medical Physics. 2006. p. 2001.
Xia JQ, Lo JY, Floyd CE. Characterization of scatter radiation of a breast phantom on siemens prototype FFDM with and without an anti-scatter grid. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2005 Aug 25;5745(II):1096–102.
Markey MK, Lo JY. Issues in assessing multi-institutional performance of BI-RADS-based CAD systems. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2005 Aug 25;5747(II):858–65.
Chen Y, Lo JY, Dobbins JT. Impulse response analysis for several digital tomosynthesis mammography reconstruction algorithms. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2005 Aug 25;5745(I):541–9.
Jesneck JL, Saunders RS, Samei E, Xia JQ, Lo JY. Detector evaluation of a prototype amorphous selenium-based full field digital mammography system. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2005 Aug 25;5745(I):478–85.
Samei E, Lo JY, Yoshizumi TT, Jesneck JL, Dobbins JT, Floyd CE, et al. Comparative scatter and dose performance of slot-scan and full-field digital chest radiography systems. Radiology. 2005 Jun;235(3):940–9.
Bilska-Wolak AO, Floyd CE, Lo JY, Baker JA. Computer aid for decision to biopsy breast masses on mammography: validation on new cases. Acad Radiol. 2005 Jun;12(6):671–80.
Baker JA, Rosen EL, Crockett MM, Lo JY. Accuracy of segmentation of a commercial computer-aided detection system for mammography. Radiology. 2005 May;235(2):385–90.
Saunders RS, Samei E, Jesneck JL, Lo JY. Physical characterization of a prototype selenium-based full field digital mammography detector. Med Phys. 2005 Feb;32(2):588–99.
Bissonnette M, Hansroul M, Masson E, Savard S, Cadieux S, Warmoes P, et al. Digital breast tomosynthesis using an amorphous selenium flat panel detector. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2005;5745(I):529–40.
Samei E, Dobbins JT, Lo JY, Tornai MP. A framework for optimising the radiographic technique in digital X-ray imaging. Radiat Prot Dosimetry. 2005;114(1–3):220–9.
Fischer EA, Lo JY, Markey MK. Bayesian networks of BI-RADS™ descriptors for breast lesion Classification. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 2004 Dec 1;26 IV:3031–4.
Baker JA, Lo JY, Delong DM, Floyd CE. Computer-aided detection in screening mammography: variability in cues. Radiology. 2004 Nov;233(2):411–7.
Land WH, Wong L, McKee D, Masters T, Anderson F, Raturi A, et al. New results in computer aided diagnosis (CAD) of breast cancer using a recently developed SVM/GRNN oracle hybrid. Proceedings of SPIE - The International Society for Optical Engineering. 2004 Oct 27;5370 II:777–84.
Land WH, McKee DW, Anderson FR, Lo JY. Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines. Proceedings of SPIE - The International Society for Optical Engineering. 2004 Oct 27;5370 II:880–7.
Samei E, Saunders RS, Lo JY, Dobbins JT, Jesneck JL, Floyd CE, et al. Fundamental imaging characteristics of a slot-scan digital chest radiographic system. Med Phys. 2004 Sep;31(9):2687–98.
Hong AS, Baker JA, Lo JY, Nicholas JL, Soo MS. Computer-aided classification of breast masses using mammogram, ultrasound, and clinical inputs. In: AMERICAN JOURNAL OF ROENTGENOLOGY. AMER ROENTGEN RAY SOC; 2004. p. 33–33.
Baker JA, Rosen EL, Lo JY, Gimenez EI, Walsh R, Soo MS. Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. AJR Am J Roentgenol. 2003 Oct;181(4):1083–8.
Bilska-Wolak AO, Floyd CE, Lo JY. Prediction of breast biopsy outcome using a likelihood ratio classifier and biopsy cases from two medical centers. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 III:1386–91.
Lo JY, Gavrielides M, Markey MK, Jesneck JL. Computer-aided classification of breast microcalcification clusters: Merging of features from image processing and radiologists. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 II:882–9.
Land WH, McKee DW, Lo JY, Anderson FR. Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 II:898–907.
Land WH, McKee D, Velazquez R, Wong L, Lo JY, Anderson F. Application of support vector machines to breast cancer screening using mammogram and clinical history data. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 I:546–56.
Tourassi GD, Lo JY, Markey MK. Validation of a constraint satisfaction neural network for breast cancer diagnosis: New results from 1,030 cases. Proceedings of SPIE - The International Society for Optical Engineering. 2003 Sep 15;5032 I:207–14.
Bilska-Wolak AO, Floyd CE, Nolte LW, Lo JY. Application of likelihood ratio to classification of mammographic masses; performance comparison to case-based reasoning. Med Phys. 2003 May;30(5):949–58.
Markey MK, Lo JY, Tourassi GD, Floyd CE. Self-organizing map for cluster analysis of a breast cancer database. Artif Intell Med. 2003 Feb;27(2):113–27.
Land WH, McKee DW, Lo JY, Anderson F. Improving mammogram screening using a bank of support vector machines (SVMs). Intelligent Engineering Systems Through Artificial Neural Networks. 2002 Dec 1;12:779–84.
Land WH, Lo JY, Velázquez R. Using evolutionary programming to configure support vector machines for the diagnosis of breast cancer. Intelligent Engineering Systems Through Artificial Neural Networks. 2002 Dec 1;12:249–54.
Markey MK, Lo JY, Floyd CE. Differences between computer-aided diagnosis of breast masses and that of calcifications. Radiology. 2002 May;223(2):489–93.
Keogan MT, Lo JY, Freed KS, Raptopoulos V, Blake S, Kamel IR, et al. Outcome analysis of patients with acute pancreatitis by using an artificial neural network. Acad Radiol. 2002 Apr;9(4):410–9.
Gavrielides MA, Lo JY, Floyd CE. Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms. Med Phys. 2002 Apr;29(4):475–83.
Markey MK, Lo JY, Vargas-Voracek R, Tourassi GD, Floyd CE. Perceptron error surface analysis: a case study in breast cancer diagnosis. Comput Biol Med. 2002 Mar;32(2):99–109.
Lo JY, Markey MK, Baker JA, Floyd CE. Cross-institutional evaluation of BI-RADS predictive model for mammographic diagnosis of breast cancer. AJR Am J Roentgenol. 2002 Feb;178(2):457–63.
Land WH, Bryden M, Lo JY, McKee DW, Anderson FR. Performance tradeoff between evolutionary computation (EC)/adaptive boosting (AB) hybrid and support vector machine breast cancer classification paradigms. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002. 2002 Jan 1;1:187–92.
Markey MK, Lo JY, Tourassi GD, Floyd CE. Cluster analysis of BI-RADS™ descriptions of biopsy-proven breast lesions. Proceedings of SPIE - The International Society for Optical Engineering. 2002 Jan 1;4684 I:363–70.
Land WH, Akanda A, Lo JY, Anderson F, Bryden M. Application of support vector machines to breast cancer screening using mammogram and history data. Proceedings of SPIE - The International Society for Optical Engineering. 2002 Jan 1;4684 I:636–42.
Baydush AH, Catarious DM, Lo JY, Abbey CK, Floyd CE. Computerized classification of suspicious regions in chest radiographs using subregion Hotelling observers. Med Phys. 2001 Dec;28(12):2403–9.
Tourassi GD, Markey MK, Lo JY, Floyd CE. A neural network approach to breast cancer diagnosis as a constraint satisfaction problem. Med Phys. 2001 May;28(5):804–11.
Land J, Masters T, Lo JY, McKee DW. Application of evolutionary computation and neural network hybrids for breast cancer classification using mammogram and history data. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. 2001 Jan 1;2:1147–54.
Land WH, Masters T, Lo JY, McKee DW, Anderson FR. New results in breast cancer classification obtained from an evolutionary computation/adaptive boosting hybrid using mammogram and history data. In: SMCia 2001 - Proceedings of the 2001 IEEE Mountain Workshop on Soft Computing in Industrial Applications. 2001. p. 47–52.
Baydush AH, Catarious DM, Lo JY, Abbey CK, Floyd CE. Computer-aided detection of lung nodules in chest radiographs using sub-region Hotelling observers. In: RADIOLOGY. 2001. p. 548–548.
Land J, Masters T, Lo JY, McKee DW. Application of adaptive boosting to EP-derived multi-layer feedforward neural networks (MLFN) to improve benign/malignant breast cancer classification. Proceedings of SPIE - The International Society for Optical Engineering. 2001 Jan 1;4322(3):1717–24.
Land W, Masters T, Lo J. Application of a new Evolutionary Programming/Adaptive Boosting hybrid to breast cancer diagnosis. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. 2000 Dec 3;2:1436–42.
Floyd CE, Lo JY, Tourassi GD. Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. AJR Am J Roentgenol. 2000 Nov;175(5):1347–52.
Tourassi GD, Floyd, Jr. CE, Lo JY. <title>Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation</title>. Hanson KM, editor. SPIE Proceedings. 2000 Jun 6;3979:46–54.
Jr WHL, Masters T, Lo JY. Application of a GRNN ORACLE to the intelligent combination of several breast cancer benign/malignant predictive paradigms. Proceedings of SPIE - The International Society for Optical Engineering. 2000;3979:I/-.
Land WH, Jr MT, Lo JY. Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms. Proc SPIE - Int Soc Opt Eng (USA). 2000;3979:77–85.
Lo JY, Land WH, Morrison CT. Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of SPIE - The International Society for Optical Engineering. 2000 Jan 1;3979.
Lo JY, Land WH, Morrison CT. Evolutionary programming technique for reducing complexity of artificial neural networks for breast cancer diagnosis. Proceedings of SPIE - The International Society for Optical Engineering. 2000;3979:I/-.
Tourassi GD, Floyd CE, Lo JY. Use of a constraint satisfaction neural network for breast cancer diagnosis and dynamic scenarios simulation. Proceedings of SPIE - The International Society for Optical Engineering. 2000 Jan 1;3979.
Gavrielides MA, Lo JY, Vargas-Voracek R, Floyd CE. Segmentation of suspicious clustered microcalcifications in mammograms. Med Phys. 2000 Jan;27(1):13–22.
Land WH, Masters T, Morrison CT, Lo JY. Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms. Intelligent Engineering Systems Through Artificial Neural Networks. 1999 Dec 1;9:803–8.
Munley MT, Lo JY, Sibley GS, Bentel GC, Anscher MS, Marks LB. A neural network to predict symptomatic lung injury. Phys Med Biol. 1999 Sep;44(9):2241–9.
Lo JY, Baker JA, Kornguth PJ, Floyd CE. Effect of patient history data on the prediction of breast cancer from mammographic findings with artificial neural networks. Acad Radiol. 1999 Jan;6(1):10–5.
Lo JY, Floyd CE. Application of artificial neural networks for diagnosis of breast cancer. Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999. 1999 Jan 1;3:1755–9.
Floyd CE, Lo JY, Baker JA. Prediction of breast biopsy outcomes from mammographic findings. Doi K, MacMahon H, Giger ML, Hoffmann KR, editors. COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING. 1999 Jan 1;1182:193–200.
Floyd CE, Lo JY, Tourassi GD. Case-based reasoning as a computer aid to diagnosis. Proceedings of SPIE - The International Society for Optical Engineering. 1999 Jan 1;3661(I):486–9.
Lo JY, Land WH, Morrison CT. Application of evolutionary programming and probabilistic neural networks to breast cancer diagnosis. Proceedings of the International Joint Conference on Neural Networks. 1999 Jan 1;5:3712–6.
Tourassi GD, Floyd CE, Lo JY. Constraint Satisfaction Neural Network for medical diagnosis. Proceedings of the International Joint Conference on Neural Networks. 1999 Jan 1;5:3632–5.
Lo JY, Floyd CE. Computer-aided diagnosis of breast cancer. Doi K, MacMahon H, Giger ML, Hoffmann KR, editors. COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING. 1999 Jan 1;1182:221–5.
Freed KS, Lo JY, Baker JA, Floyd CE, Low VH, Seabourn JT, et al. Predictive model for the diagnosis of intraabdominal abscess. Acad Radiol. 1998 Jul;5(7):473–9.
Sakamoto H, Lo JYD, Nishida T. QoS middleware for Internet multimedia streaming. NEC Tech J (Japan). 1998;51(8):35–40.
Lo JY, Floyd CE. Self-organizing maps for analyzing mammographic findings. IEEE International Conference on Neural Networks - Conference Proceedings. 1997 Dec 1;4:2472–4.
Lo JY, Baker JA, Kornguth PJ, Iglehart JD, Floyd CE. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features. Radiology. 1997 Apr;203(1):159–63.
Lo JY, Kim J, Baker JA, Floyd CE. Computer-aided diagnosis of mammography using an artificial neural network: Predicting the invasiveness of breast cancers from image features. Proceedings of SPIE - The International Society for Optical Engineering. 1996 Dec 1;2710:725–32.
Floyd CE, Patz EF, Lo JY, Vittitoe NF, Stambaugh LE. Diffuse nodular lung disease on chest radiographs: a pilot study of characterization by fractal dimension. AJR Am J Roentgenol. 1996 Nov;167(5):1185–7.
Jr FCE, Lo JY, Tourassi GD, Baker JA, Vitittoe NF, Vargas-Vorack R. Computer aided diagnosis in thoracic and mammographic radiology. Med Imaging Technol (Japan). 1996;14(6):629–34.
Baker JA, Kornguth PJ, Lo JY, Floyd CE. Artificial neural network: improving the quality of breast biopsy recommendations. Radiology. 1996 Jan;198(1):131–5.
Mun SK, Freedman MT, Wu YC, Lo BS, Floyd CE, Lo JY, et al. Academic consortium for the evaluation of computer-aided diagnosis (CADx) in mammography. Proceedings of SPIE - The International Society for Optical Engineering. 1995 Dec 1;2431:442–6.
Lo JY, Baker JA, Kornguth PJ, Floyd CE. Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. Acad Radiol. 1995 Oct;2(10):841–50.
Baker JA, Kornguth PJ, Lo JY, Williford ME, Floyd CE. Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology. 1995 Sep;196(3):817–22.
Lo JY, Grisson AT, Floyd CE, Kornguth PJ. Computer-aided diagnosis of mammograms using an artificial neural network: Merging of standardized input features from the ACR lexicon. Proceedings of SPIE - The International Society for Optical Engineering. 1995 May 12;2434:571–8.
LO JY, BAYDUSH AH, BAKER JA, KORNGUTH PJ, FLOYD CE. COMPUTER-AIDED DIAGNOSIS OF BREAST MASS MALIGNANCY WITH AUTOMATED FEATURE-EXTRACTION AND ARTIFICIAL NEURAL NETWORKS. In: RADIOLOGY. 1995. p. 425–425.
Floyd CE, Lo JY, Yun AJ, Sullivan DC, Kornguth PJ. Prediction of breast cancer malignancy using an artificial neural network. Cancer. 1994 Dec 1;74(11):2944–8.
Floyd CE, Baydush AH, Lo JY, Bowsher JE, Ravin CE. Bayesian restoration of chest radiographs. Scatter compensation with improved signal-to-noise ratio. Invest Radiol. 1994 Oct;29(10):904–10.
Lo JY, Baydush AH, Floyd CE. Spatially varying scatter compensation for chest radiographs using a hybrid Madaline artificial neural network. Proceedings of SPIE - The International Society for Optical Engineering. 1994 May 11;2167:601–11.
Lo JY, Floyd CE, Baker JA, Ravin CE. Scatter compensation in digital chest radiography using the posterior beam stop technique. Med Phys. 1994 Mar;21(3):435–43.
FLOYD CE, YUN AJ, LO JY, TOURASSI G, SULLIVAN DC, KORNGUTH PJ. PREDICTION OF BREAST-CANCER MALIGNANCY FOR DIFFICULT CASES USING AN ARTIFICIAL NEURAL-NETWORK. In: WORLD CONGRESS ON NEURAL NETWORKS-SAN DIEGO - 1994 INTERNATIONAL NEURAL NETWORK SOCIETY ANNUAL MEETING, VOL 1. LAWRENCE ERLBAUM ASSOC PUBL; 1994. p. A127–32.
Baker JA, Floyd CE, Lo JY, Ravin CE. Observer evaluation of scatter subtraction for digital portable chest radiographs. Invest Radiol. 1993 Aug;28(8):667–70.
Jordan LK, Floyd CE, Lo JY, Ravin CE. Measurement of scatter fractions in erect posteroanterior and lateral chest radiography. Radiology. 1993 Jul;188(1):215–8.
Floyd CE, Baydush AH, Lo JY, Bowsher JE, Ravin CE. Scatter compensation for digital chest radiography using maximum likelihood expectation maximization. Invest Radiol. 1993 May;28(5):427–33.
Lo JY, Floyd CE, Baker JA, Ravin CE. An artificial neural network for estimating scatter exposures in portable chest radiography. Med Phys. 1993;20(4):965–73.
LO JY, FLOYD CE, BOWSHER JE, RAVIN CE. SPATIALLY VARYING SCATTER ESTIMATION IN PORTABLE CHEST RADIOGRAPHY WITH AN ARTIFICIAL NEURAL NETWORK. RADIOLOGY. 1992 Nov 1;185:300–300.
BAYDUSH AH, FLOYD CE, LO JY, BOWSHER JE, RAVIN CE. SCATTER REDUCTION IN PORTABLE DIGITAL CHEST RADIOGRAPHY WITH BAYESIAN IMAGE ESTIMATION. RADIOLOGY. 1992 Nov 1;185:305–305.
Floyd CE, Baker JA, Lo JY, Ravin CE. Measurement of scatter fractions in clinical bedside radiography. Radiology. 1992 Jun;183(3):857–61.
Floyd CE, Baker JA, Lo JY, Ravin CE. Posterior beam-stop method for scatter fraction measurement in digital radiography. Invest Radiol. 1992 Feb;27(2):119–23.
Floyd CE, Lo JY, Chotas HG, Ravin CE. Quantitative scatter measurement in digital radiography using a photostimulable phosphor imaging system. Med Phys. 1991;18(3):408–13.
Chotas HG, Floyd CE, Dobbins JT, Lo JY, Ravin CE. Scatter fractions in AMBER imaging. Radiology. 1990 Dec;177(3):879–80.
Floyd CE, Bowsher JE, Munley MT, Tourassi GD, Garg S, Baydush AH, et al. Artificial neural networks for SPECT image reconstruction with optimized weighted backprojection (Accepted). In: Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference. IEEE; p. 2184–8.

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