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.

翻译：零样本自监督学习（ZS-SSL）已成为加速磁共振成像（MRI）重建中的一种有前景的范式，消除了对全采样外部数据集的依赖。然而，仅从单个欠采样扫描中学习存在监督稀缺和优化不稳定的问题，常导致过拟合或伪影。为应对这些挑战，我们提出了一种稳健的物理驱动ZS-SSL框架，将物理一致性与图像域非局部先验协同结合。我们的方法引入了三项核心创新：（1）线圈灵敏度图（CSM）引导的动态存储库，通过基于线圈灵敏度约束过滤物理不一致的伪影，稳定训练轨迹；（2）基于SPIRiT的正则化，通过学习到的相关核和随机掩蔽强制k空间自一致性；（3）非局部自相似性（NSS）像素库，利用前序模块建立的高保真参考，显式挖掘非局部解剖相似性，从而增强图像域的监督。在FastMRI数据集上的大量实验表明，我们的方法实现了最先进的性能，特别是在高加速因子下，有效弥合了零样本学习与监督方法之间的差距。代码可在https://github.com/Zolento/NS-SSL获取。