Deep Structure-Preserved Graph Embedding for Improved MRF Reconstruction

We introduce a novel deep-learning framework based on the structure-preserved graph embedding for improved MRF reconstruction. Our work first models the acquired high-dimensional MRF data and the corresponding parameter maps as graph data nodes. Then, we propose an MRF reconstruction framework based on the graph embedding method, retaining intrinsic graph structures between parameter maps and MRF data. To improve the accuracy of the estimated graph structure and the computational efficiency of the proposed framework, we unroll the iterative optimization process into a deep neural network and introduce a learned graph embedding module to adaptively learn the graph structure. By redefining the MRF reconstruction problem as a structure-preserved graph embedding problem, the proposed method can effectively reduce the computational complexity of MRF reconstruction compared to data-priors-driven methods. Numerical experiments demonstrate that our approach can reconstruct high-quality MRF data and multiple parameter maps within a significantly reduced computational cost.