Deep Structure-Preserved Graph Embedding for Improved MRF Reconstruction

Motivation: Improve MRF Reconstruction. Goal(s): Introduce a novel deep-learning framework based on the structure-preserved graph embedding for improved MRF reconstruction. Approach: We propose a reconstruction framework based on graph embedding, modeling the high-dimensional MRF data and the parameter maps as graph data nodes. To improve the accuracy of the estimated graph structure and the computational efficiency of the proposed framework, we unroll the iterative steps into a deep neural network and introduce a learned graph embedding module to adaptively learn the graph structure. Results: Numerical experiments demonstrate that our approach can reconstruct high-quality parameter maps within reduced computational cost. Impact: 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.