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Title:

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging.

Document type:
Journal Article
Author(s):
Hammernik, Kerstin; Küstner, Thomas; Yaman, Burhaneddin; Huang, Zhengnan; Rueckert, Daniel; Knoll, Florian; Akçakaya, Mehmet
Abstract:
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these. We then focus on physics-dri...     »
Journal title abbreviation:
IEEE Signal Process Mag
Year:
2023
Journal volume:
40
Journal issue:
1
Pages contribution:
98-114
Fulltext / DOI:
doi:10.1109/msp.2022.3215288
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37304755
Print-ISSN:
1053-5888
TUM Institution:
Institut für KI und Informatik in der Medizin
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