Authors: Prasanna Alluri, Mona Arbab, Xingzhe Li, Chang-Shiun Lin, Mu-han Lin, David D.M. Parsons, Asal Rahimi, Justin D. Visak, Narine Wandrey
Affiliation: UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
Abstract Preview: Purpose: Intelligence-Optimization-Engine (IOE) v1.0 relied heavily on planner expertise and patient-specific IMRT beam arrangements, requiring frequent revisions. While VMAT workflows offered potenti...
Authors: Yankui Chang, Shijun Li, Xi Pei, Ripeng Wang, Xuanhe Wang, X. George Xu, Qing Zhang, Jingfang Zhao
Affiliation: University of Science and Technology of China, Shanghai proton and heavy ion center, School of Nuclear Science and Technology, University of Science and Technology of China, Anhui Wisdom Technology Co., Ltd.
Abstract Preview: Purpose:
This paper describes disruptive methods using both GPU-based MC simulation and deep-learning (DL)-based MC denoising algorithms, as well as clinical tests involving more than 560 patient p...
Authors: Thomas I. Banks, Tsuicheng D. Chiu, Viktor M. Iakovenko, Christopher Kabat, Chang-Shiun Lin, Mu-Han Lin, Arnold Pompos
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
Abstract Preview: Purpose: The superior soft-tissue contrast provided by MR imaging offers favorable conditions for the effective application of stereotactic body radiation therapy (SBRT). Accurate small field dosimetr...
Authors: Stephen R. Bowen, Shijun Chen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Qianqian Tong, Jiajie Wang, Shouyi Wang, Faisal Yaseen
Affiliation: The University of Texas at Austin, Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington
Abstract Preview: Purpose: Tumor subregion clustering and prediction of region-specific response can augment assessments and adaptive treatment decisions. A modeling framework was constructed to predict chemoradiation ...