Compressive sensing recovery of dynamic MRI via nonlocal low-rank regularization

Compressive sensing (CS) based dynamic MRI techniques have been proposed to improve the imaging speed and spatiotemporal resolution. However, existing CS recovery methods haven´t exploited the rich redundancy among the spatial and temporal dimensions. In this paper, we address the CS recovery of dynamic MRI from partially sampled k-t space using the nonlocal low-rank regularization (NLR). To exploit the nonlocal redundancy in the spatial-temporal dimension, the dynamic MRI sequence is divided into overlapping 3D patches along both the spatial and temporal directions. We exploit the fact that the matrix that consists of a sufficient number of similar patches is low-rank. To effectively approximate the low-rank matrix, the non-convex surrogate function logdet (·) is used instead of the convex nuclear norm. Experimental results show that our proposed method can outperform existing state-of-the-art dynamic MRI reconstruction methods.