Event
BIOE Seminar Series: Lei Ren (University of Maryland School of Medicine, Radiation Oncology)
Friday, September 3, 2021
9:00 a.m.-10:00 a.m.
Virtual
Gregg Duncan
gaduncan@umd.edu
*Note: meeting login information for virtual seminars will be distributed via email a few days in advance of the event. If you do not receive seminar emails and wish to join the list, email BIOE Public Relations Specialist Emily Rosenthal (erosent1@umd.edu).
Artificial Intelligence (AI) for Image-guided radiation therapy (IGRT)
Abstract
Imaging techniques play a vital role in image-guided radiation therapy (IGRT) since they determine the precision of the radiation delivery, which directly correlates to tumor control and normal tissue toxicities. Maximizing the benefits of imaging techniques requires optimizing their imaging efficiency, dose, and precision, which is often challenging given the competing nature of these aspects. Recent advances in artificial intelligence (AI) open up new opportunities for addressing some of these long-standing problems from a completely different perspective. Dr. Ren’s group has focused on developing fast low-dose high precision x-ray based imaging techniques for both inter- and intra-fraction verification in radiation therapy. In this talk, he will introduce their recent developments in harnessing the power of AI for different applications in IGRT, including image reconstruction, enhancement, synthesis, deformable image registration, and 4D imaging. He will also discuss the potential limitations and future directions of AI in radiation therapy.
About the Speaker, Lei Ren, Ph.D.
Dr. Ren is a clinical medical physicist certified by ABR and a leading scientist in medical imaging and radiation therapy. His seminal research focuses on the image-guided radiation therapy (IGRT) and development and application of AI in radiation therapy.
Dr. Ren conducted some of the first studies on developing digital tomosynthesis (DTS) for fast, low-dose target localization in radiation therapy, which led to the development of the limited-angle intrafraction verification (LIVE) system for intrafraction verification in radiotherapy. His group also developed novel cone-beam CT (CBCT) scatter reduction and correction methods, DTS/CBCT/MRI image reconstruction methods using prior information and motion modeling, and 4D-CBCT sorting and reconstruction algorithms.
In recent years, Dr. Ren's group has focused on developing and implementing AI for radiation therapy applications. His research areas include developing novel AI, especially deep learning, techniques for deformable image registration, image synthesis, image reconstruction, image augmentation, 4D imaging, radiomics, clinical decision making, and digital phantom generation. They have been developing techniques to use deep learning and biomechanical modeling to generate on-board hybrid virtual-MRI/CBCT images to substantially improve the soft tissue contrast in CBCT for target localization in liver radiotherapy. In addition, his group is actively developing AI technologies for synthesizing highly realistic eXtended Modular ANthropomorphic (XMAN) phantoms for motion management and virtual clinical trials in radiation therapy.
The overall goal of Dr. Ren's research is to develop novel imaging and therapy technologies to improve the precision and outcome of radiation therapy treatments with high efficiency and minimal imaging dose.
