Invited Talk: Kerstin Hammernik – Physics-based learning for MRI reconstruction – Recent advances in static and dynamic imaging, 9th of June, 12PM CET – LIVE ONLY, Register now!
It’s a great pleasure to welcome Kerstin Hammernik – one of the creators of variational networks – to our lab! Don’t miss this talk! It won’t be recorded
Title: Physics-based learning for MRI reconstruction – Recent advances in static and dynamic imaging
Date: 9th of June, 12PM CET
Registration Link: https://fau.zoom.us/webinar/register/WN_X3awEy9YQcOn5qYE5l75jg
Abstract: During the past years, deep learning has evolved tremendously in the research field of MR image reconstruction. In this talk, I will guide you through these developments, ranging from learning advanced image regularization to learning physics-based unrolled optimization, and I will discuss the challenges and caveats of deep learning for MR image reconstruction. I will cover examples ranging from 2D musculoskeletal imaging to higher-dimensional cardiac imaging that will show the vast potential for the future of fast MR image acquisition and reconstruction.
Bio: Kerstin Hammernik is a postdoctoral researcher in the group of Prof. Daniel Rueckert at the Lab for Artificial Intelligence in Healthcare and Medicine, Technical University of Munich, Germany, and at the Department of Computing, Imperial College London, United Kingdom. In 2019, she received her Ph.D. degree in Computer Science from Graz University of Technology, Austria. Her Ph.D. thesis “Variational Networks for Medical Image Reconstruction” was supervised by Prof. Thomas Pock, Institute of Computer Graphics and Vision, Graz University of Technology. During her Ph.D., she spent four months as a research intern at the Center for Advanced Imaging Innovation and Research, New York University School of Medicine, USA. Her research interests are inverse problems and machine learning in medical imaging, with a special focus on fast MRI acquisition and reconstruction for cardiac and musculoskeletal applications.