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.