Authors: William N. Duggar, Li Yuan
Affiliation: University of Mississippi Medical Center
Abstract Preview: Purpose:
Radiation Oncology departments typically utilize various systems from different vendors. Ensuring the integrity and correctness of data during transfers between these systems is essential ...
Authors: Si-Wa Chan, Yuan-Yu Lee, Zhi-Ying Li, Jia-Wei Liao, Hui-Yu Cathy Tsai
Affiliation: Department of Radiology, Taichung Veterans General Hospital, Institute of Nuclear Engineering and Science, National Tsing Hua University
Abstract Preview: Purpose: Dense breast tissue reduces the sensitivity of mammography, posing diagnostic challenges, especially for Asian women with high breast density (up to 50%). Current single-modality techniques o...
Authors: Xiaoyu Duan, Xiang Li, Wenbo Wan, Lei Zhang, Yiwen Zhang
Affiliation: Duke University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Breast screening has been proved to reduce breast cancer mortality by early detection and treatment for patients. Mammography is the most common and widely used technique for breast cancer sc...
Authors: Alexander Bookbinder, Matthew Tivnan, Xiangyi Wu, Wei Zhao
Affiliation: Stony Brook Medicine, Massachusetts General Hospital
Abstract Preview: Purpose: To investigate and benchmark a system-adaptive diffusion-based digital breast tomosynthesis (DBT) denoising model for a direct-indirect dual-layer flat panel detector (DI-DLFPD) with a k-edge...
Authors: Mengqi Shen, Meghna Trivedi, Tony J.C. Wang, Andy (Yuanguang) Xu, Yading Yuan
Affiliation: Columbia University Medical Center, Dept of Med Hematology & Oncology, Data Science Institute at Columbia University, Columbia University Irving Medical Center, Department of Radiation Oncology, Columbia University Irving Medical Center
Abstract Preview: Purpose: T2-weighted (T2w) images are critical for identifying pathological changes due to their superior contrast in differentiating tissue types. However, they often lack detailed anatomical resolut...
Authors: Sofia Beer, Menal Bhandari, Alec Block, Nader Darwish, Joseph Dingillo, Sebastien A. Gros, Hyejoo Kang, Andrew Keeler, Rajkumar Kettimuthu, Jason Patrick Luce, Ha Nguyen, John C. Roeske, George K. Thiruvathukal, Austin Yunker
Affiliation: Data Science and Learning Division, Argonne National Laboratory, Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, Stritch School of Medicine Loyola University Chicago, Cardinal Bernardin Cancer Center, Loyola University Chicago, Department of Computer Science, Loyola University of Chicago
Abstract Preview: Purpose: Artificial intelligence (AI) generated synthetic medical images are seeing increased use in radiology and radiation oncology. Physician observer studies are an ideal way to evaluate the usabi...
Authors: Marissa Brown, Geoffrey D. Clarke, Luke Norton
Affiliation: University of Texas Health Science Center at San Antonio
Abstract Preview: Purpose: To evaluate how different learning strategies affect convolutional neural network (CNN) estimates of the liver's intravoxel incoherent motion (IVIM) parameters.
Methods: A 3-stage U-Net wa...
Authors: Naruomi Akino, Kirsten Lee Boedeker, Ilmar Hein, Dylan Mather, Akira Nishikori, Daniel W Shin
Affiliation: Canon Medical Systems Corporation, Canon Medical Research USA
Abstract Preview: Purpose: To validate the performance a deep learning reconstruction (DLR) algorithm in an anatomical background compared to a uniform phantom background.
Methods: An analytic forward projection mod...
Authors: Hania A. Al-Hallaq, Chih-Wei Chang, Anees H. Dhabaan, Yuan Gao, Shaoyan Pan, Junbo Peng, Richard L.J. Qiu, Keyur Shah, Sibo Tian, Zhen Tian, Xiaofeng Yang, David Yu, Jun Zhou
Affiliation: Emory University, Whinship Cancer Institute, Emory University, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Traditional cone-beam computed tomography (CBCT) often requires multiple angular projections, increasing radiation exposure and extending scanning times, which may lead to heightened patient ...
Authors: Jiali Gong, Yi Guo, Chi Han, Wanli Huo, Hongdong Liu, Zhao Peng, Yaping Qi, Zhaojuan Zhang
Affiliation: Department of Radiotherapy, cancer center, The First Affiliated Hospital of Fujian Medical University, Department of Oncology, Xiangya Hospital, Central South University, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, China Jiliang University, Division of lonizing Radiation Metrology, National Institute of Metrology, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University
Abstract Preview: Purpose: To address overfitting from limited training data in multi-organ segmentation, an efficient transfer learning framework is proposed. It reduces reliance on training samples, enabling a single...
Authors: Petr Bruza, Yao Chen, David J. Gladstone, Lesley A Jarvis, Brian W Pogue, Kimberley S Samkoe, Yucheng Tang, Shiru Wang, Rongxiao Zhang
Affiliation: NVIDIA Corp, Dartmouth College, Thayer School of Engineering, Dartmouth College, Dartmouth Cancer Center, University of Missouri, University of Wisconsin - Madison
Abstract Preview: Purpose: Cherenkov imaging provides real-time visualization of megavoltage radiation beam delivery during radiotherapy. Patient-specific bio-morphological features, such as vasculature, captured in th...
Authors: Naruomi Akino, Kirsten Lee Boedeker, Ilmar Hein, Akira Nishikori, Daniel W Shin
Affiliation: Canon Medical Systems Corporation, Canon Medical Research USA
Abstract Preview: Purpose: To validate a simulation tool using physics-based image quality metrics in both phantom and patient data, and to assess the low contrast detectability (LCD) of Super Resolution-Deep Learning ...