Abstract
Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compared to 2D imaging. However, image acceleration is required due to long scan times and contrast washout. Physics-guided deep learning (PG-DL) approaches have recently emerged as an improved accelerated MRI strategy. Training of PG-DL methods is typically performed in a supervised manner requiring fully-sampled data, which is challenging in 3D LGE CMR. Recently, a self-supervised learning approach was proposed to enable training PG-DL techniques without fully-sampled data. In this work, we extend this self-supervised learning approach to 3D imaging, while tackling challenges related to small training database sizes of 3D volumes. Results and a reader study on prospectively accelerated 3D LGE show that the proposed approach at 6-fold acceleration outperforms the clinically utilized compressed sensing approach at 3-fold acceleration.
Original language | English (US) |
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Title of host publication | 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021 |
Publisher | IEEE Computer Society |
Pages | 100-104 |
Number of pages | 5 |
ISBN (Electronic) | 9781665412469 |
DOIs | |
State | Published - Apr 13 2021 |
Event | 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France Duration: Apr 13 2021 → Apr 16 2021 |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2021-April |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Conference
Conference | 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 |
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Country/Territory | France |
City | Nice |
Period | 4/13/21 → 4/16/21 |
Bibliographical note
Funding Information:This work was partially supported by NIH R01HL153146, P41EB027061, U01EB025144; NSF CAREER CCF-1651825. There is no conflict of interest for the authors.
Publisher Copyright:
© 2021 IEEE.
Keywords
- Accelerated imaging
- Cardiac MRI
- Late gadolinium enhancement
- Parallel imaging
- Physics-guided deep learning
- Self-supervised learning