Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
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.
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