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Diffusion MRI is commonly performed using echo-planar imaging (EPI) due toits rapid acquisition time. However, the resolution of diffusion-weightedimages is often limited by magnetic field inhomogeneity-related artifacts andblurring induced by T2- and T2*-relaxation effects. To address theselimitations, multi-shot EPI (msEPI) combined with parallel imaging techniquesis frequently employed. Nevertheless, reconstructing msEPI can be challengingdue to phase variation between multiple shots. In this study, we introduce anovel msEPI reconstruction approach called zero-MIRID (zero-shotself-supervised learning of Multi-shot Image Reconstruction for ImprovedDiffusion MRI). This method jointly reconstructs msEPI data by incorporatingdeep learning-based image regularization techniques. The network incorporatesCNN denoisers in both k- and image-spaces, while leveraging virtual coils toenhance image reconstruction conditioning. By employing a self-supervisedlearning technique and dividing sampled data into three groups, the proposedapproach achieves superior results compared to the state-of-the-art parallelimaging method, as demonstrated in an in-vivo experiment.

Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction