Accelerated 4d Mr Image Reconstruction Using Joint Higher Degree Total Variation And Local Low-Rank Constraints

Four-dimensional magnetic resonance imaging (4D-MRI) can provide 3D tissue properties and the temporal profiles at the same time. However, further applications of 4D-MRI is limited by the long acquisition time and motion artifacts. We introduce a regularized image reconstruction method to recover 4D MR images from their undersampled Fourier coefficients, named HDTV-LLR. We adopt the three-dimensional higher degree total variation and the local low-rank penalties to simultaneously exploit the spatial and temporal correlations of the dataset. In order to solve the resulting optimization problem efficiently, we propose a fast alternating minimization algorithm. The performance of the proposed method is demonstrated in the context of 4D cardiac MR images reconstruction with undersampling factors of 12 and 16. The proposed method is compared with iGRASP, and schemes using either low-rank or sparsity constraint alone. Numerical results show that the proposed method enables accelerated 4D-MRI with improved image quality and reduced artifacts.