A Mutual Exchange Extending Beyond Individual Disciplines
The densely intertwined fields of artificial intelligence and medicine benefit from discourse and collaboration, especially beyond the confines of their respective academic disciplines. This event is aimed at stimulating and initiating exactly this type of beneficial exchange. The event will focus on approaches to the issues, current developments, and various challenges facing this interdisciplinary field, thereby providing a platform for discussion.
We will start the evening off by welcoming Florian Knoll (Friedrich-Alexander University Erlangen-Nuremberg). His keynote speech is titled "AI in MR Imaging: From First Results to Current Challenges". We then invite you to join us afterwards at a small reception for a pleasant exchange of ideas. We look forward to seeing you there!
Event: AI in Medicine
Wednesday, September 28, 2022
5:30 - 7:30 PM
A reception with refreshments will follow.
WHAT TO EXPECT
Keynote address, discussions, networking
Professor, Department of Artificial Intelligence in Biomedical Engineering (AIBE)
Friedrich-Alexander Universität Erlangen-Nürnberg
Florian Knoll earned his PhD in electrical engineering in 2011 at the Graz University of Technology. He was Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine between 2015 and 2021. He has been a Professor and head of the Computational Imaging Lab at the Department of Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander University Erlangen Nuremberg since 2021. He holds an R01, R21 and a P41 TR&D project award from NIH. His research interests include iterative MR image reconstruction, parallel MR imaging, Compressed Sensing and Machine Learning.
Machine learning techniques were first introduced six years ago to solve the inverse problem of MR image generation from accelerated acquisitions (1,2,3). Since then, the field has grown significantly, leading to a wide range of machine learning methods developments that can be applied to a wide range of imaging applications that have already been rolled out as clinical products by major manufacturers.
My presentation begins by first focusing on the background of an artificial intelligence process to generate MR images stemming from acquired measurement data. In particular, I will discuss a range of approaches that map iterative algorithms onto neural networks. I will discuss advantages and ongoing challenges, covering the design of neural network architecture and the training procedure, error metrics, computation time, generalizability and validation of the results. I will also include a discussion of the lessons learned from the recent fastMRI image reconstruction challenges organized jointly with Facebook AI research (4,5).
1. Learning a variational model for compressed sensing MRI reconstruction. Hammernik, et al. Proc. ISMRM p33 (2016).
2. Accelerating magnetic resonance imaging via deep learning. Wang et al. IEEE ISBI 514-517 (2016).
3. Hammernik et al. Learning a Variational Network for Reconstruction of Accelerated MRI Data. MRM, 79:3055-3071 (2018).
4. Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Knoll et al. MRM 84 (6), 3054-3070 (2020).
5. Results of the 2020 fastmri challenge for machine learning MR image reconstruction. Muckley et al. IEEE TMI 40 (9), 2306-2317(2021).