Hybrid CS-DMRI: Periodic Time-Variant Subsampling and Omnidirectional Total Variation Based Reconstruction

Compressive sensing (CS) has been used to accelerate dynamic magnetic resonance imaging (DMRI). Currently, the online CS-DMRI is faster, whereas the offline CS-DMRI provides higher accuracy for image reconstruction. To achieve good image reconstruction performance in terms of both speed and accuracy, we propose a hybrid CS-DMRI method using periodic time-variant subsampling for different frames. In each period, there is one reference frame that is sampled at a higher subsampling ratio. The two nearby reference frames with good reconstruction quality can be used to provide rough predictions of the other frames between them. To finely recover the current frame, one structural regularization in the optimization model for reconstruction is a 2-D omnidirectional total variation (OTV) for exploiting the sparsity of the difference between the predicted and estimated frames, and the other is a 3-D OTV as a regularization term for exploiting the bilateral spatio-temporal coherence between the forward reference frame, current frame, and backward reference frame. Compared with classical total variation, the proposed OTV fully utilizes the correlations of all the possible directions of the data. The formulated optimization model can be solved using iterative reweighted least squares with the pre-conditioned conjugate gradient method. Numerical experiments demonstrate that the proposed method has better reconstruction accuracy than all the existing methods and low computational complexity that is comparable to the existing online methods.