Sparsifying-transform-learning-based magnetic resonance fingerprinting reconstruction method

For highly sparse down-sampling in the k-space, magnetic resonance fingerprinting (MRF) technology faces the challenge to reconstruct clean fingerprints due to aliasing noise. Based on the compressed sensing (CS) theory, modelbased reconstruction methods reconstruct the fingerprints by solving an optimization problem. However, most stationary information of fingerprints can be reconstructed easily but the temporal varied features which decides the accuracy of dictionary matching. Sparsifying transform learning adaptively learns the transform domain based on image patches, leading to improved sparse representation and consequently enhancing reconstruction efficiency and quality. Therefore, a sparsifying transform learning based magnetic resonance fingerprinting (STLMRF) method is proposed, which integrates MRF reconstruction model with sparsifying transform learning model for the first time. Firstly, aliasing noises are suppressed well for the adaptive sparsity levels got by sparsifying transform learning. Additionally, the discriminative temporal features of the fingerprint are retrieved through dictionary matching constraint. Secondly, to accelerate the reconstruction process, singular value decomposition (SVD) is applied to map dictionary and k-space data into a lower-dimensional subspace. Finally, numerical experiments are conducted to validate the performance of the proposed STLMRF method. Compared with the state-of-the-art reconstruction methods, STLMRF is better in terms of accuracy and robustness. It means that STLMRF holds significant promise for improving the quality and reliability of MRF-based parameter estimation.