Cycle-Consistent Self-Supervised Learning for Improved Highly-Accelerated MRI Reconstruction

Physics-driven deep learning (PD-DL) has become a powerful tool for accelerated MRI. Recent developments have also developed unsupervised learning for PD-DL, including self-supervised learning. However, at very high acceleration rates, such approaches show performance deterioration. In this study, we propose to use cyclic-consistency (CC) to improve self-supervised learning for highly accelerated MRI. In our proposed CC, simulated measurements are obtained by undersampling the network output using patterns drawn from the same distribution as the true one. The reconstructions of these simulated measurements are obtained using the same network, which are then compared to the acquired data at the true sampling locations. This CC approach is used in conjunction with a masking-based self-supervised loss. Results show that the proposed method can substantially reduce aliasing artifacts at high acceleration rates, including rate 6 and 8 fastMRI knee imaging and 20-fold HCP-style fMRI.