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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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DTSTART:20220101T000000
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DTSTART;VALUE=DATE:20230918
DTEND;VALUE=DATE:20230919
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SUMMARY:From basic concepts to clinical translation of deep learning in MRI reconstruction
DESCRIPTION:In 2016\, deep learning techniques have been introduced to solve the inverse problem of MR image reconstruction from undersampled data from accelerated acquisitions (1\,2\,3). Since then\, the field has grown substantially\, and a wide range of machine learning methods have been developed and applied to a wide range of imaging applications. In this talk\, I will give a short overview of the background of a deep learning reconstruction that is based on iterative reconstruction methods used in compressed sensing. I will discuss advantages as well as ongoing challenges that need to be met when translating these approaches into daily clinical practice (4). This will include a discussion of the lessons learnt from the recent fastMRI image reconstruction challenges (5\,6). \nReferences:\n1. Learning a variational model for compressed sensing MRI reconstruction. Hammernik\, et al. Proc. ISMRM p33 (2016).\n2. Accelerating magnetic resonance imaging via deep learning. Wang et al. IEEE ISBI 514-517 (2016).\n3. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Hammernik et al. MRM\, 79:3055-3071 (2018).\n4. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Johnson et al.\, Radiology 307:e220425 (2023).\n5. 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).\n6. Results of the 2020 fastmri challenge for machine learning MR image reconstruction. Muckley et al. IEEE TMI 40 (9)\, 2306-2317(2021). \nBiography of the speaker: \nFlorian Knoll received his PhD in electrical engineering in 2011 from Graz University of Technology. From 2015 to 2021\, he was Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine. Since 2021\, he is Professor and head of the Computational Imaging Lab at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander University Erlangen Nuremberg. In currently holds two grants from the German research fund (DFG) as well as an R01 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. \nAll are welcome.
URL:https://ece.hku.hk/events/from-basic-concepts-to-clinical-translation-of-deep-learning-in-mri-reconstruction/
CATEGORIES:Seminar
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