Authors: Beth Bradshaw Ghavidel, Chih-Wei Chang, Yuan Gao, Priyanka Kapoor, Shaoyan Pan, Junbo Peng, Richard L.J. Qiu, Jill Remick, Justin R. Roper, Zhen Tian, Xiaofeng Yang
Affiliation: Whinship Cancer Institute, Emory University, Emory University, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Current cone-beam computed tomography (CBCT) typically requires no less than 200 degrees of angular projections, which prolongs scanning time and increases radiation exposure. To address thes...
Authors: Girish Bal, Jan Kralj, Ayan Mitra, PhD, Ling Shao, Matjaz Subic, Yevgen Voronenko
Affiliation: RefleXion Medical, Cosylab
Abstract Preview: Purpose: This work enhances the efficiency of radiation therapy treatment planning by optimizing the beamlet dose matrix and full patient dose computations using GPU acceleration. The Collapsed-Cone C...
Authors: Yu Gao, Lei Xing, Siqi Ye
Affiliation: Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose:
Limited-angle CBCT (LA-CBCT) scans are often the only option for non-coplanar radiation therapy to prevent potential mechanical collisions. However, the consecutive angular occlusion of pr...
Authors: Ziqi Gao, Lei Xing, Siqi Ye, S. Kevin Zhou
Affiliation: Department of Radiation Oncology, Stanford University, University of Science and Technology of China (USTC)
Abstract Preview: Purpose: To address the challenge of high memory usage in volumetric cone-beam CT (CBCT) imaging, we propose a method that combines joint reconstruction and super-resolution for sparsely sampled CBCT ...
Authors: Zahra Bagherpour, Manijeh Beigi, Pedram Fadavi, Faraz Kalantari, Moghadaseh Khaleghibizaki, Hengameh Nazari, Mojtaba Safari, Sepideh Soltani
Affiliation: Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Department of Radiation Oncology, School of Medicine, Emory University and Winship Cancer Institute, Department of Radiation Oncology, Iran University of Medical Sciences, University of Arkansas for medical sciences, Department of Radiation physics, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences
Abstract Preview: Purpose: This study aims to evaluate whether readily available mammographic and sonographic data, combined with machine learning (ML) models, can predict critical molecular factors (ER, PR, HER2) in b...
Authors: Arezoo Modiri, Robabeh Rahimi, Akira SaiToh, Amit Sawant
Affiliation: Maryland University Baltimore, University of Maryland, University of Maryland in Baltimore, Department of Computer and Information Sciences, Sojo University
Abstract Preview: Purpose: It has been a longstanding challenge to optimize the daily schedule of radiation treatment rooms toward minimum patient wait times, efficient use of clinical staff and reduced running cost of...
Authors: Hengjie Liu, Dan Ruan, Ke Sheng, DI Xu
Affiliation: Physics and Biology in Medicine, University of California, Los Angeles, Department of Radiation Oncology, University of California, San Francisco, Department of Radiation Oncology, University of California at San Francisco, Department of Radiation Oncology, University of California, Los Angeles
Abstract Preview: Purpose:
State-of-the-art deep learning-based deformable image registration often uses large, complex models directly adapted from computer vision tasks but achieves only comparable performance to ...
Authors: Duncan Henry Bohannon, Pretesh Patel, Sibo Tian, Yinan Wang, Xiaofeng Yang, Ahmal Jawad Zafar, Jun Zhou
Affiliation: Emory University, Department of Radiation Oncology and Winship Cancer Institute, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose:
4D robust optimization, incorporating additional images (e.g., maximum inhale/exhale phases), is commonly used to account for target motion in proton treatment planning. However, the incre...