  Diffusion MRI is commonly performed using echo-planar imaging (EPI) due to
its rapid acquisition time. However, the resolution of diffusion-weighted
images is often limited by magnetic field inhomogeneity-related artifacts and
blurring induced by T2- and T2*-relaxation effects. To address these
limitations, multi-shot EPI (msEPI) combined with parallel imaging techniques
is frequently employed. Nevertheless, reconstructing msEPI can be challenging
due to phase variation between multiple shots. In this study, we introduce a
novel msEPI reconstruction approach called zero-MIRID (zero-shot
self-supervised learning of Multi-shot Image Reconstruction for Improved
Diffusion MRI). This method jointly reconstructs msEPI data by incorporating
deep learning-based image regularization techniques. The network incorporates
CNN denoisers in both k- and image-spaces, while leveraging virtual coils to
enhance image reconstruction conditioning. By employing a self-supervised
learning technique and dividing sampled data into three groups, the proposed
approach achieves superior results compared to the state-of-the-art parallel
imaging method, as demonstrated in an in-vivo experiment.
