Zero-Shot Self-Supervised Distortion-Free Diffusion MRI Reconstruction

Motivation: Multi-shot Echo Planar Imaging (msEPI) for diffusion MRI captures detailed anatomy but suffers from phase inconsistencies and susceptibility-induced distortions. Goal(s): To develop a zero-shot self-supervised reconstruction method that eliminates msEPI distortions using undersampled k-space data from single subject, without external datasets. Approach: We present ZS-PRIME, the first zero-shot, high-fidelity framework for distortion-free msEPI reconstruction. ZS-PRIME leverages PRIME, a distortion-free multi-echo acquisition where the second echo, at lower resolution and acceleration, provides high-fidelity field maps. CNN-based k-space and image-space regularization ensure phase consistency and anatomical accuracy. Results: ZS-PRIME outperforms existing methods, including PRIME with LORAKS regularization, delivering high-quality, distortion-free reconstructions, enhancing diffusion MRI fidelity. Impact: ZS-PRIME combines advanced field map estimation (PRIME) with zero-shot self-supervised training, achieving distortion-free, high-resolution multi-shot diffusion MRI from undersampled data. This obviates the dependency on external training datasets, setting a new benchmark for efficient, high-fidelity diffusion MRI.