Bi-smooth constraints for accelerated dynamic MRI with low-rank plus sparse tensor decomposition

Abstract

The long data acquisition required for dynamic magnetic resonance imaging (DMRI) has limited its development. This paper proposes a low-rank plus sparse tensor decomposition model with bi-smooth constraints to reconstruct DMR images from under-sampled k-t space data. The DMR images can be naturally decomposed into a low-rank and sparse tensor to exploit the spatiotemporal correlation to improve the reconstruction quality. Different degrees of smoothness in the foreground and background parts of the DMR images are analyzed and utilized to further exploit the smoothness with a divide-and-conquer strategy. By imposing the bi-smooth constraints on these two parts, more edges and details of DMR images can be preserved. The experimental results show that the proposed method can achieve better reconstruction performance than the existing state-of-the-art methods.