Zero-shot EPI Nyquist ghost correction with diffusion-based generative models and magnitude consistency regularization

Motivation: To address EPI phase error correction caused by the problem of inconsistent positive and negative phases. Goal(s): We introduce an image prior-based method termed Phase Error Correction Diffusion-based Reconstruction with Echo Apart Magnitude-Consistency(PEC-DREAM). Approach: The method was trained on structural imaging data, and it performs robustly the inference on EPI phase error correction task without specific model finetune. Here, we introduce novel data consistency including k-space and magnitude consistency to enhance the performance of the SGM during reverse diffusion. Results: Experiments demonstrate the versatility of our approach across various scenarios, including human and rodent EPI, accelerated and non-accelerated imaging and SMS sampling. Impact: The method we have proposed effectively addresses EPI phase error correction. Prospective experiments demonstrate the versatility of our innovative approach across various scenarios, and our method holds promise as a potent tool.