Deformation Corrected Blind Compressed Sensing (DC-BCS): A Novel Framework for Dynamic MRI reconstruction

Dynamic magnetic resonance imaging (MRI) is becoming vital important in modern clinical applications, and compared to other imaging methods such as B-ultrasound and CT, it has unique advantages. In this paper, we extend a deformation corrected blind compressed sensing (DC-BCS) method to reconstruct dynamic magnetic resonance data from under-sampled measurements. We introduce blind compressed sensing on the deformation corrected dynamic signals which avoids the need to know the sparsity basis in both the sampling and the recovery process. Then we will register the recovered images to the deformation corrected images and update them all in the each recovery iteration. The registration can be regard as a constraint to get a sparser representation and BCS techniques have been demonstrated to provide much better image reconstruction quality compared to compressed sensing methods that utilize a fixed sparsifying transform or dictionary. Combining them, we can achieve good performance. We jointly exploit the spatial and temporal sparsity actually and use variable splitting and alternative optimization to decouple the proposed complicated optimization problem to five easier subproblems. In order to avoid the risk of local convergence, we utilize effective continuation strategy. The results of experiment on the in-vivo dynamic myocardial perfusion MRI dataset show the proposed method achieves superior reconstruction quality, compared to the most state-of-the-art reconstruction methods.