Motivation: Estimating high-quality parameter maps from highly undersampled measurements presents one of the major challenges in MR fingerprinting (MRF). Goal(s): To propose a novel MRF reconstruction framework based on manifold structured data priors for high-quality parameter maps estimation. Approach: We propose a novel MRF reconstruction framework leveraging manifold structured data priors to improve the accuracy of the reconstructed parameter maps. Additionally, we integrate a locally low-rank prior into the reconstruction framework to exploit local correlations within each patch and further enhance reconstruction performance. Results: Experimental results demonstrate that our method can achieve significantly improved reconstruction performance with reduced computational time over the state-of-the-art methods. Impact: By exploiting the manifold structure prior of MRF data, our method can better reconstruct detailed textures and provide accurate brain tissue characterization, thereby improving the diagnostic accuracy in clinical applications.
Peng et al. (Tue,) studied this question.