Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly learned from data. These data-driven priors typically remain unchanged for future data in the testing phase once they are learned during training. In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach. While the proposed approach can compromise the extremely fast reconstruction time of deep learning MRI methods, our results on knee MRI indicate that such adaptation can substantially reduce the remaining artifacts in reconstructed images. In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data.
|Original language||English (US)|
|Title of host publication||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society|
|Subtitle of host publication||Enabling Innovative Technologies for Global Healthcare, EMBC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Jul 2020|
|Event||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada|
Duration: Jul 20 2020 → Jul 24 2020
|Name||Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS|
|Conference||42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020|
|Period||7/20/20 → 7/24/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- Magnetic Resonance Imaging
- Neural Networks, Computer
- Radionuclide Imaging
PubMed: MeSH publication types
- Research Support, U.S. Gov't, Non-P.H.S.
- Journal Article
- Research Support, N.I.H., Extramural