Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications. These methods unroll an iterative optimization algorithm into a series of regularizer and data consistency units. The unrolled networks are typically trained end-to-end using a supervised approach. Current supervised PG-DL approaches use all of the available sub-sampled measurements in their data consistency units. Thus, the network learns to fit the rest of the measurements. In this study, we propose to improve the performance and robustness of supervised training by utilizing randomness by retrospectively selecting only a subset of all the available measurements for data consistency units. The process is repeated multiple times using different random masks during training for further enhancement. Results on knee MRI show that the proposed multi-mask supervised PG-DL enhances reconstruction performance compared to conventional supervised PG-DL approaches.
|Original language||English (US)|
|Number of pages||5|
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada|
Duration: Jun 6 2021 → Jun 11 2021
Bibliographical noteFunding Information:
This work was partially supported by NIH R01HL153146, P41EB027061, U01EB025144; NSF CAREER CCF-1651825. Knee MRI data were obtained from the NYU fastMRI initiative database . NYU fastMRI database was acquired with the relevant institutional review board approvals as detailed in . NYU fastMRI investigators provided data but did not participate in analysis or writing of this report. A listing of NYU fastMRI investigators, subject to updates, can be found at fastmri.med.nyu.edu. There is no conflict of interest for the authors.
© 2021 IEEE
- Accelerated imaging
- Algorithm unrolling
- Data augmentation
- Magnetic resonance imaging
- Physics-guided deep learning
- Supervised learning