September 19 Sep 19
12:00 PM - 01:00 PM
Tuesday Tue


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Stanford University School of Medicine

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Medical Physics Seminar - Lei Ren

AI-Assisted Image Guidance and Clinical Decision Making in Radiation Therapy

12:00pm – 1:00pm Seminar & Discussion

Zoom Webinar

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Lei Ren, Ph.D., Professor and Associate Chief of Physics Research in the Radiation Oncology department at the University of Maryland & adjunct Professor at Duke University

Dr. Ren is a Professor and Associate Chief of Physics Research in the Radiation Oncology department at the University of Maryland and an adjunct Professor at Duke University. Dr. Ren’s research focus is image-guided radiation therapy (IGRT), including imaging dose reduction, image reconstruction, synthesis, enhancement, registration, 4D imaging, and development and applications of AI for outcome prediction and clinical decision making in radiation therapy. His research has been published in numerous peer-reviewed journal papers, including featured articles, and has been presented in over 40 invited talks. Dr. Ren has served as the PI for multiple NIH R01 grants and Industry grants and has won several awards from AAPM, ASTRO, and ISMRM. Dr. Ren frequently provides scientific reviews for peer-reviewed journals, conferences, book proposals, and NIH study sections, and has served in editorial roles for different journals, including the Deputy Editor of Medical Physics journal. He is actively involved in committee services at AAPM and ASTRO, including serving as the Vice-Chair of the AAPM Annual Meeting Committee. Dr. Ren was elected as a Fellow of AAPM in 2020. Dr. Ren has mentored over 30 postdoc and graduate students and has received the Mentorship Award from the Duke Medical Physics program.

AI-Assisted Image Guidance and Clinical Decision Making in Radiation Therapy

Artificial intelligence (AI), especially deep learning, has progressed significantly in the past few years and has shown to be both transformative and disruptive in many fields, including radiation therapy. This talk will introduce our research in two main areas of developing AI in radiation therapy: (1). AI for image-guided radiation therapy (IGRT). Imaging techniques play a vital role in IGRT since they determine the precision of radiation delivery, which directly impacts tumor control and normal tissue toxicities. Maximizing the benefits of imaging techniques requires optimizing their imaging efficiency, dose, and quality, which is often challenging given the competing nature of these aspects. The recent rise of AI, especially deep learning, opens up a new horizon for addressing some of the long-standing problems from a completely different perspective. This talk will cover our recent research in developing AI for image reconstruction and processing to improve the quality and efficiency of different imaging modalities, including conventional modalities such as CBCT and new rising modalities such as protoacoustic imaging and prompt gamma imaging. (2). AI for clinical decision making. AI can become a powerful tool to assist clinical decision making by providing additional information to the clinicians such as outcome prediction or modeling the clinical decisions directly. This talk will introduce our recent developments in harnessing the power of deep learning for modeling the physician decision process in SRS treatments. AI modeling physician decisions can greatly improve the efficiency and quality of clinical practice and address healthcare disparities in low-resource areas with limited access to clinical care.