Highly Accelerated Dynamic Parallel MRI Exploiting Constrained State-Space Model with Low Rank and Sparsity

Fast magnetic resonance imaging (MRI) techniques [1-4], which lead to signal recovery from incomplete data, have been introduced in dynamic imaging to improve spatiotemporal resolution without apparent loss of image quality. In this respect, we propose a novel, highly accelerated dynamic parallel MRI reconstruction method exploiting a constrained state space model with low rank and sparsity while jointly estimating spatiotemporal kernels and missing signals in k-t space in an iterative fashion. Spatiotemporal kernels stacked across multiple time frames are estimated using the low rank constraint due to the nature of smoothly varying spatiotemporal correlation in k-t space during calibration, while the solution is projected onto the reconstructed k-t space with the sparsity constraint imposed on the estimated dynamic images in x-f space.

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