Speaker
Description
Xiaofeng Yang, PhD, is Professor, Vice Chair, and Chief of Medical Physics in the Department of Radiation and Cellular Oncology at the University of Chicago. He is a medical physicist with extensive experience in clinical practice, research, education, and mentorship. Dr. Yang’s research focuses on artificial intelligence (AI)-enabled quantitative imaging, including CBCT/CT, MRI, PET/CT, and PET/MRI, as well as image-guided radiation therapy. His specific interests include adaptive radiotherapy, image segmentation and synthesis, deformable image registration, digital twins, proton therapy, and FLASH radiotherapy. His laboratory develops advanced AI and imaging technologies to improve treatment planning, image guidance, motion management, and personalized radiation therapy. Dr. Yang has led and contributed to multiple NIH- and NSF-funded research projects and has extensive experience mentoring graduate students, postdoctoral fellows, medical physics residents, physician residents, and junior investigators. He is also actively engaged in the medical physics and medical imaging community through professional service, peer review, and editorial activities.
Summary
Artificial intelligence (AI) is changing medical imaging and radiation therapy, moving from individual image-processing tasks toward more integrated and patient-specific models. This talk will highlight the progression from AI-based image synthesis to digital twins for precision radiation therapy. Image synthesis can generate or improve medical images across different imaging modalities and can also reconstruct high-quality images from limited or undersampled data. These methods can improve image quality, reduce imaging dose and acquisition time, and provide useful quantitative information. When combined with image segmentation, deformable registration, and longitudinal imaging, they can also help track anatomical and functional changes over time. Digital twins build on these technologies by integrating imaging, treatment information, and other patient-specific data into a dynamic model of an individual patient. In radiation therapy, digital twins may help predict anatomical changes, treatment response, toxicity, and delivered dose, supporting adaptive and personalized treatment. This talk will present our work in AI-enabled imaging, image synthesis, deformable registration, and adaptive radiation therapy, and discuss how these tools may contribute to the development of digital twins for future clinical care.