Overview
Deshan Yang is the professor of Medical Physics in the Department of Radiation Oncology, Duke University. He received his bachelor's degree in electronics engineering from Tsinghua University in 1992, a master’s degree in computer science from the Illinois Institute of Technologies in 2002, and his master’s and Ph.D. degrees in Biomedical Engineering from the University of Wisconsin-Madison in 2005. He spent two years as a postdoctoral researcher before joining Washington University in St. Louis as a faculty member. He worked as an instructor to a professor at Washington University in St. Louis between 2006 and 2021 before joining Duke University in 2021. His main research areas are medical image processing and analysis for radiation oncology applications, adaptive radiotherapy, cardiac radiosurgery, health information technologies for radiation oncology and medical physics.
Current Appointments & Affiliations
Professor of Radiation Oncology
·
2023 - Present
Radiation Oncology,
Clinical Science Departments
Member of the Duke Cancer Institute
·
2022 - Present
Duke Cancer Institute,
Institutes and Centers
Recent Publications
Fast motion-compensated reconstruction for 4D-CBCT using deep learning-based groupwise registration.
Journal Article Biomed Phys Eng Express · December 23, 2024 Objective. Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment tim ... Full text Link to item CiteA comprehensive lung CT landmark pair dataset for evaluating deformable image registration algorithms.
Journal Article Med Phys · May 2024 PURPOSE: Deformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high-qual ... Full text Open Access Link to item CiteTechnical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.
Journal Article J Appl Clin Med Phys · March 2024 BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT). PURPOSE: This study tested an unsupervised generative adversarial network to mitigate ICD artifacts i ... Full text Link to item CiteRecent Grants
Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results
ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2022 - 2026Novel imaging and treatment technologies for image-guided noninvasive stereotactic cardiac radiosurgery
ResearchPrincipal Investigator · Awarded by Washington University in St. Louis · 2021 - 2024An interactive deep-learning method to semi-automatically segment abdominal organs to support stereotactic MR guided online adaptive radiotherapy (SMART) for abdominal cancers
ResearchPrincipal Investigator · Awarded by National Institutes of Health · 2019 - 2022View All Grants
Education, Training & Certifications
University of Wisconsin, Madison ·
2006
Ph.D.
University of Wisconsin, Madison ·
2005
M.S.