Authors: Jie Deng, Yunxiang Li, Xiao Liang, Weiguo Lu, Jiacheng Xie, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, University of Texas Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Recently, foundational models trained on large datasets have shown remarkable performance across various tasks. Developing a foundational model for medical image modality translation in head-...
Authors: Benito De Celis Alonso, Braian Adair Maldonado Luna, Gerardo Uriel Perez Rojas, René Eduardo Rodríguez-Pérez, Kamal Singhrao
Affiliation: Department of Radiation Oncology, Brigham and Women's Hospital, Dana Farber Cancer Institute, Harvard Medical School, Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla
Abstract Preview: Purpose: Artificial Intelligence (AI)-generated synthetic CT (sCT) images can be used to provide electron densities for dose calculation for online adaptive MRI-guided stereotactic body radiotherapy (...
Authors: Rani Anne', Wenchao Cao, Yingxuan Chen, Wookjin Choi, Firas Mourtada, Yevgeniy Vinogradskiy
Affiliation: Thomas Jefferson University
Abstract Preview: Purpose: In-room mobile cone-beam CT (CBCT) is emerging to enhance high-dose-rate (HDR) brachytherapy workflow using on-demand imaging. However, metal artifacts from X-ray markers inside gynecological...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Cynthia Fu-Yu Chuang, Yu Gao, Dimitre Hristov, Lianli Liu, Lawrie Skinner, Lei Xing
Affiliation: Department of Radiation Oncology, Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose:
MR-guided radiation therapy has introduced a significant leap in cancer treatment by allowing adaptive treatment. The low-field MR-guided system predominantly uses the TrueFISP sequence, w...
Authors: Yunxiang Li, Xinlong Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Acquiring high-resolution (HR) proton density (PD) images is time-consuming, while lower-resolution (LR) PD scans are faster but can lack sufficient details. We propose CycleHR, a T2-contrast...
Authors: Jie Hu, Zhengdong Jiang, Nan Li, Tie Lv, Yuqing Xia, Shouping Xu, Gaolong Zhang, Wei Zhao, Changyou Zhong
Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Radiotherapy Department of Meizhou People’s Hospital (Huangtang Hospital), UT Health San Antonio, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology
Abstract Preview: Purpose: Patients usually undergo cone-beam computed tomography (CBCT) scans which are used for patient set-up before radiotherapy. However, the low image quality of CBCT hinders its use in adaptive r...
Authors: Derek Tang, Susu Yan
Affiliation: Massachusetts General Hospital
Abstract Preview: Purpose: To evaluate the performance of a multi-task automated-segmentation and synthetic CT generation model (sCT) and investigate its application in an adaptive proton therapy workflow.
Methods: ...
Authors: Manju Liu, Weiwei Sang, Yanyan Shi, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Lihua Zhang, Rihui Zhang
Affiliation: Jiahui International Hospital, Jiahui International Hospital, Radiation Oncology, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: This study aims to transform cone-beam computed tomography (CBCT) images acquired from deep inspiration breath-hold (DIBH) breast cancer patients into high-fidelity synthetic CT (sCT) images....
Authors: So Hyun Ahn, Chris Beltran, Byongsu Choi, Jeong Heon Kim, Jin Sung Kim, Bo Lu, Justin Chunjoo Park, Bongyong Song, Jun Tan
Affiliation: Mayo Clinic, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Ewha Medical Research Institute, Ewha Womans University College of Medicine, UC San Diego, Yonsei University College of Medicine
Abstract Preview: Purpose:
Cone-beam computed tomography (CBCT) is widely used in IGRT for patient positioning but suffers from low resolution and poor soft tissue contrast. Synthetic CT (sCT) generated from CBCT ad...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Serdar Charyyev, Xianjin Dai, PhD, Yu Gao, Thomas R. Niedermayr, Lei Xing
Affiliation: Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose: Real-time transrectal ultrasound imaging is the gold standard for needle placement and treatment planning of real-time based-ultrasound-based high dose-rate (HDR) prostate brachytherapy. Cumu...
Authors: Mahya Ahmadzadeh, Nagarajan Kandasamy, Keyur Shah, Gregory C. Sharp, Santhosh Vadivel, John MacLaren Walsh
Affiliation: Electrical and Computer Engineering Department, Massachusetts General Hospital, Emory University, Drexel University
Abstract Preview: Purpose: In image-guided radiotherapy (IGRT), cone beam CTs (CBCTs) suffer from distortions that degrade registration with planning CTs. While CycleGANs can generate synthetic CTs (sCTs) from CBCTs, e...
Authors: Akihiro Haga, Ren Iwasaki, Kenya Kusunose, Makoto Miyake, Kenji Moriuchi, Yasuharu Takeda, Hidekazu Tanaka, Hirotsugu Yamada
Affiliation: Department of Cardiovascular Medicine, Nephrology, and Neurology Graduate School of Medicine, University of the Ryukyus, Graduate School of Biomedical Sciences, Tokushima University, Tokushima university, Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Department of Cardiology, Tenri Hospital, Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Division of Heart Failure, Department of Heart Failure and Transplant, National Cerebral and Cardiovascular Center
Abstract Preview: Purpose: Device dependency is a significant challenge in medical AI, potentially limiting generalization performance. This study aimed to develop a robust deep learning model for predicting left ventr...
Authors: Jie Hu, Nan Li, Chuanbin Xie, Shouping Xu, Xinlei Xu, Gaolong Zhang, Zhilei Zhang
Affiliation: School of Physics, Beihang University, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, Peopleʼs Republic of China, Department of Radiation Oncology, School of Physics, Beihang University, Beijing, 102206, Peopleʼs Republic of China
Abstract Preview: Purpose: This study aims to synthesize CT images for MRI-only radiation therapy using a deep learning approach that integrates information from the T1- and T2-weighted MRI sequence.
Methods: 97 hea...