Prof. Charles Bouman at the Wilson Lecture Series

Plug-and-Play: A Framework for Integrating Physics and Machine Learning in CT Imaging

This talk presents emerging methods for the integration of physics-based and machine learning (ML) models with novel acquisition methods to push CT technology well beyond traditional limits. For example, while ML methods such as deep neural networks offer unprecedented ability to model complex behavior, they typically lack the flexibility and accuracy of traditional physics-based methods for modeling imaging sensors. In order to address this dilemma, we present plug-and-play methods as a general framework for getting the ``best of both worlds’’ by integrating traditional physics-based models based on probability distributions with action-based ML models. Throughout the talk, we present state-of-the-art examples using imaging modalities including computed tomography (CT), transmission electron microscopy (STEM), synchrotron beam imaging, optical sensing, scanning electron microscopy (SEM), and ultrasound imaging.

About the speaker 

Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University. His research is in the area of Computational Imaging, with applications in medical, scientific, and commercial imaging. He received his B.S.E.E. degree from the University of Pennsylvania, M.S. degree from the University of California at Berkeley, and Ph.D. from Princeton University in 1989. He is a member of the National Academy of Inventors, a Fellow of the IEEE, AIMBE, IS&T, and SPIE. He is the recipient of the 2021 IEEE Signal Processing Society, Claude Shannon-Harry Nyquist Technical Achievement Award, the 2014 Electronic Imaging Scientist of the Year award, and the IS&T’s Raymond C. Bowman Award; and in 2020, his paper on Plug-and-Play Priors won the SIAM Imaging Science Best Paper Prize.