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Themenabend: AI in Medicine

Austausch über die eigene Disziplin hinaus

Die engverwobenen Bereiche Künstliche Intelligenz und Medizin leben vom Austausch über die eigene Disziplin hinaus, vom Diskurs und der Zusammenarbeit der unterschiedlichen Fachbereiche. Diesen Austausch wollen wir mit dieser Veranstaltung anregen und anstoßen. Annäherungen an Fragestellungen, aktuelle Entwicklungen und Herausforderungen dieses interdisziplinären Themenbereichs sollen im Zentrum stehen und für Gesprächsstoff sorgen.

Wir starten den Abend mit Florian Knoll (Friedrich-Alexander Universität Erlangen-Nürnberg) und seiner Keynote „KI in der MR-Bilderfassung: Von ersten Ergebnissen zu aktuellen Herausforderungen“. Im Anschluss laden wir zum gemütlichen Austausch und freuen uns mit Ihnen ins Gespräch zu kommen.

Themenabend: AI in Medicine

WANN

Mittwoch, 28.09.2022
17:30 - 19:30 Uhr
Im Anschluss gemütlicher Austausch bei Getränk & Fingerfood.

WAS

Keynote, Austausch, Netzwerken

Florian Knoll

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.

Florian Knoll ©FAU/GeorgPöhlein

AI in MR Image Acquisition: From Initial Findings to Ongoing Challenges

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).

References
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).