Compressed sensing for longitudinal MRI: An adaptive-weighted approach

In this paper, we propose an algorithm for acceleration of longitudinal MRI. The mutual similarity of the follow-up scans in longitudinal studies is exploited in an adaptive approach, that adjusts the sampling and reconstruction strategies to the actual similarity between the scans. The baseline MR scan is utilized both in the sampling stage, via adaptive sampling, and in the reconstruction stage, with weighted Compressed Sensing (CS). In adaptive sampling, {\bf\emph k}-space sampling locations are optimized such that the acquired data is focused on the change between the follow-up MRI and the former one. Weighted CS uses the locations of the nonzero coefficients in the sparse domains as a prior in the recovery process. Experiments demonstrate that our longitudinal adaptive CS MRI (LACS-MRI) scheme provides reconstruction quality which outperforms traditional CS MRI for rapid MRI. Examples are shown on patients with brain tumors and demonstrate improved spatial resolution and accelerated acquisition for 2D and 3D brain imaging.