Weak supervision in multi-coil accelerated MR image reconstruction

Motivation: In training-data-limited settings, weak supervision –cooperatively utilizing under-sampled and fully-sampled datasets– can be advantageous. Goal(s): To compare weakly-supervised multi-coil Magnetic Resonance (MR) image reconstruction against reconstruction using only under-sampled or fully-sampled datasets in high- and low-data regimes. Approach: Pretrain a Variational Network (VarNet) in a self-supervised manner by minimizing L1 loss in k-space using a 4x under-sampled dataset. Transfer the pre-trained weights to another VarNet and fine-tune it using a smaller, fully sampled dataset by optimizing MS-SSIM loss in image space. Results: We demonstrate improvements in reconstruction quality in the high-data regime as well as enhanced robustness of reconstruction in the low-data regime. Impact: Multi-coil MR image reconstruction exploiting both under-sampled and fully-sampled datasets is achievable with transfer learning and fine-tuning. Our proposed methodology can provide improved reconstruction quality and robustness.