Joint Dynamic MRI Reconstruction and Aggregated Motion Estimation with Optical Flow Constraint

This paper proposes a novel framework to jointly reconstruct the dynamic magnetic resonance images (DMRI) and estimate the motion vectors from the under-sampled measurements. Due to the inherent motion effects in DMRI acquisition, reconstruction of DMRI using motion estimation/compensation has been studied under a compressed sensing (CS) scheme. In this paper, by embedding the intensity based optical flow (OF) constraint into the traditional CS scheme, we are able to couple the DMRI reconstruction with vector motion estimation. The resulting optimization problem is then solved by a primal-dual algorithm with linesearch due to its efficiency when dealing with non-differentiable problems. Moreover, the proposed framework is capable of handling a wide class of prior information (regularizations) for DMRI reconstruction, such as sparsity, low rank, total variation. In order to reduce the computational cost, the OF constraint is employed in a specific coarse scale. Experiments on various DMRI data, ranging from in vivo lung data to simulated phantom, validate the reconstruction quality improvement using the proposed scheme in comparison to several state-of-the-art algorithms.