It would have been great to welcome Emil to the Bergkirchweih this year. Unfortunately, the festival was cancelled. Yet, we still have the pleasure to have Emil virtually here in Erlangen!
Abstract: This talk examines the claim made in the literature that ill-posed inverse problems associated with image reconstruction in computed tomography (CT) can be solved with a convolutional neural network (CNN). To lay the groundwork, a brief overview of inverse problems will be given including a discussion on what makes an inverse problem ill-posed and what constitutes its solution. Examples of how inverse problem investigations play a role in CT image reconstruction will be presented in order to appreciate the value of the generalizable knowledge gained in such studies. Having set the stage, the talk will the discuss the evidence that deep-learning with convolutional neural networks solve the CT inverse problem. Finally, I will cover our own investigation into the use of CNNs to solve the sparse-view CT inverse problem in the context of a breast CT simulation.
Short Bio: Dr. Sidky is Research Professor in the Department of Radiology at The University of Chicago. He received his B.S. degree (1988) in Physics, Astronomy-Physics, and Mathematics from the University of Wisconsin-Madison. He went on to obtain his M.S (1991) and Ph.D (1993) in Physics from The University of Chicago. Dr. Sidky worked as a post-doctoral research assistant in Atomic Physics at the University of Copenhagen (1993-1996), University of Bielefeld (1996), and Kansas State University (1996-2001). In 2001, Dr. Sidky switched to medical imaging and joined the lab of Dr. Xiaochuan Pan; most recently, he was promoted to Research Professor in 2018. Dr. Sidky has published approximately 100 papers, and about 70 of them are in medical imaging. His theoretical work has mainly focused on X-ray tomography with sparse or limited-angular range sampling. He has also applied advanced techniques for non-smooth or non-convex large-scale optimization applied to imaging. His application work has centered on tomographic breast imaging, CT and tomosynthesis, and developing image reconstruction algorithms and calibration techniques for spectral CT scanners based on photon-counting detectors.
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