Purpose: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets. Methods: Self-supervised learning via data undersampling (SSDU) for physics-guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground-truth data, as well as conventional compressed-sensing and parallel imaging methods using the publicly available fastMRI knee database. The same physics-guided neural network is used for both proposed SSDU and supervised training. The SSDU training is also applied to prospectively two-fold accelerated high-resolution brain data sets at different acceleration rates, and compared with parallel imaging. Results: Results on five different knee sequences at an acceleration rate of 4 shows that the proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed-sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study. The results on prospectively subsampled brain data sets, in which supervised learning cannot be used due to lack of ground-truth reference, show that the proposed self-supervised approach successfully performs reconstruction at high acceleration rates (4, 6, and 8). Image readings indicate improved visual reconstruction quality with the proposed approach compared with parallel imaging at acquisition acceleration. Conclusion: The proposed SSDU approach allows training of physics-guided deep learning MRI reconstruction without fully sampled data, while achieving comparable results with supervised deep learning MRI trained on fully sampled data.
Bibliographical noteFunding Information:
National Institutes of Health (U01EB025144 and P41EB027061) and the National Science Foundation (CAREER CCF-1651825) Knee MRI data were obtained from the New York University (NYU) fastMRI initiative database.58 The NYU fastMRI database was acquired with the relevant institutional review board approvals as detailed in the original paper.58 The NYU fastMRI investigators provided data but did not participate in the analysis or writing of this report. A listing of NYU fastMRI investigators, subject to updates, can be found at fastmri.med.nyu.edu.
- accelerated imaging
- convolutional neural networks
- deep learning
- image reconstruction
- parallel imaging
- self-supervised learning