Physics-Driven Zero-Shot MRI Reconstruction with Non-local Image Priors

2026-06-13

AI Summary

This work addresses the challenges of optimization instability and artifact generation in zero-shot self-supervised methods for single-coil undersampled MRI reconstruction, which often arise due to limited supervisory signals. To enhance both stability and reconstruction accuracy, the authors propose a physics-driven zero-shot framework that synergistically integrates three mechanisms: coil sensitivity–guided dynamic image priors, k-space self-consistency regularization based on SPIRiT kernel modeling, and non-local self-similarity exploitation. Notably, the method requires no additional training data and achieves state-of-the-art performance on the FastMRI dataset, substantially narrowing the performance gap between zero-shot and supervised learning approaches—particularly under high acceleration factors.

Abstract

Zero-Shot Self-Supervised Learning (ZS-SSL) has emerged as a promising paradigm for accelerated Magnetic Resonance Imaging (MRI) reconstruction, eliminating the reliance on fully-sampled external datasets. However, learning solely from a single under-sampled scan suffers from supervision scarcity and optimization instability, often leading to overfitting or artifacts. To address these challenges, we propose a robust physics-driven ZS-SSL framework that synergizes physical consistency with image-domain non-local priors. Our method introduces three core innovations: (1) a Coil Sensitivity Map (CSM)-Guided Dynamic Repository, which stabilizes the training trajectory by filtering physically inconsistent artifacts based on coil sensitivity constraints; (2) a SPIRiT-based regularization, which enforces k-space self-consistency via a learned correlation kernel and stochastic masking; (3) a Non-Local Self-Similarity (NSS) Pixel Bank, which leverages the high-fidelity reference established by the former modules to explicitly mine non-local anatomical similarities, thereby augmenting supervision in the image domain. Extensive experiments on the FastMRI dataset demonstrate that our approach achieves state-of-the-art performance, particularly under high acceleration factors, effectively bridging the gap between zero-shot learning and supervised methods. The code is available at https://github.com/Zolento/NS-SSL.

Problem

• Zero-Shot MRI Reconstruction
• Self-Supervised Learning
• Acceleration Artifacts
• Supervision Scarcity
• Optimization Instability

Innovation

• Zero-Shot Self-Supervised Learning
• Physics-Driven Reconstruction
• Non-Local Self-Similarity
• Coil Sensitivity Map
• SPIRiT Regularization