磁共振指纹（MRF）成像技术在疾病组织磁敏感性定量分析方面展现出巨大的应用前景．然而，如何从高欠采样数据中重建出高质量的时域图像，进而实现高精度定量成像，依然是MRF技术发展面临的关键挑战之一．本文提出了一种基于流形结构正则化的MRF重建方法．该方法将指纹信号与组织定量参数视为流形上的数据点，并揭示了指纹流形与参数流形之间存在内在的拓扑结构一致性．基于此重要发现，构建了MRF成像的流形结构正则化约束，通过在重建过程中保持指纹流形与参数流形的结构一致性，有效提升了重建质量．此外，为了充分挖掘数据内部的潜在关联，还在重建框架中融合了局部低秩先验，进一步增强了重建性能．实验结果表明，与现有先进方法相比，本文所提出的方法在重建质量上取得了显著提升，同时大幅降低了计算耗时，充分展现了其在高精度定量成像中的应用潜力．

Magnetic resonance fingerprinting (MRF) has shown great potential for the quantitative assessment of tissue susceptibility across diverse diseases. However, reconstructing high-quality temporal images from highly undersampled data and thus achieving high-precision quantitative imaging remains a primary challenge in MRF. In this paper, we propose a novel MRF reconstruction framework leveraging manifold structured data priors. This approach models fingerprint signals and tissue quantitative parameters as data points residing on manifolds, and reveals the intrinsic topological consistency between the fingerprint manifold and the parameter manifold. Based on this key observation, we introduce a manifold structured data regularization constraint for MRF reconstruction. By enforcing structural consistency between the fingerprint manifold and the parameter manifold during reconstruction, the proposed constraint effectively improves reconstruction quality. Furthermore, to fully exploit the inherent data correlations, we integrate a locally low-rank prior into our reconstruction framework, which further enhances reconstruction performance. Experimental results demonstrate that the proposed method achieves notable enhancement in reconstruction quality while significantly reducing computational time compared to existing approaches, highlighting its potential for clinical translation in high-quality MRF imaging.