In this study, we propose a new spatio-temporal reconstruction scheme for the fast reconstruction of dynamic magnetic resonance imaging (dMRI) data from undersampled k-space measurements. To utilize both spatial and temporal redundancy in dMRI sequences, our method investigates the potential benefits of enforcing local spatial sparsity constraint on the difference to a reference image for each frame and additionally exploiting the low-rank property of global spatio-temporal signal via nuclear norm (NN) minimization. We present here an iterative algorithm that solves the convex optimization problem in an alternating fashion. The proposed method is tested on in-vivo 3D cardiac MRI and dynamic susceptibility contrast (DSC)-MRI brain perfusion datasets. In comparison to two state-of-the-art methods, numerical experiments demonstrate the superior performance of our method in terms of reconstruction accuracy.     «

In this study, we propose a new spatio-temporal reconstruction scheme for the fast reconstruction of dynamic magnetic resonance imaging (dMRI) data from undersampled k-space measurements. To utilize both spatial and temporal redundancy in dMRI sequences, our method investigates the potential benefits of enforcing local spatial sparsity constraint on the difference to a reference image for each frame and additionally exploiting the low-rank property of global spatio-temporal signal via nuclear no...     »