Group sparse reconstruction using intensity-based clustering. http://dx.doi.org/10.1002/mrm.24333

Abstract

Compressed Sensing (CS) has been of great interest to speed up the acquisition of MR images. The k-t Group Sparse (k-t GS) method has recently been introduced for dynamic MR images to exploit not just the sparsity, as in CS, but also the spatial group structure in the sparse representation. k-t GS achieves higher acceleration factors compared to the conventional CS method. However, it assumes a spatial structure in the sparse representation and it requires a time consuming hard-thresholding reconstruction scheme. In this work, we propose to modify k-t GS by incorporating prior information about the sorted intensity of the signal in the sparse representation, for a more general and robust group assignment. This approach is referred to as Group Sparse reconstruction using Intensity based clustering (GSI). The feasibility of the proposed method is demonstrated for static 3D hyperpolarized lung images and applications with both dynamic and intensity changes, i.e. 2D cine and perfusion cardiac MRI. Acceleration factors of up to 8-fold are achieved outperforming the original compressed sensing and, when assumptions are not satisfied, the k-t GS method.