Self-Supervised Deep Learning Reconstruction for Highly Accelerated Diffusion Imaging

We propose a zero-shot self-supervised learning (ZS-SSL) approach for accelerated diffusion MRI reconstruction. Our method builds on the approach in 3 for subject-specific MRI reconstruction. We perform reconstruction across all diffusion directions with a single model, rather than different models for each direction, reducing computation time. We partition the directions as training and validation directions. We train the model on training directions, while keeping track of validation loss. We test our model on entire directions, evaluating the reconstruction quality of a single network across all directions. Jointly trained ZS-SSL provides better reconstructions than standard parallel imaging, while remaining computationally efficient.