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The clinical utility of myocardial perfusion MR imaging (MPI) is often restricted by the inability of current acquisition schemes to simultaneously achieve high spatio-temporal resolution, good volume coverage, and high signal to noise ratio. Moreover, many subjects often find it difficult to hold their breath for sufficiently long durations making it difficult to obtain reliable MPI data. Accelerated acquisition of free breathing MPI data can overcome some of these challenges. Recently, an algorithm termed as k - t SLR has been proposed to accelerate dynamic MRI by exploiting sparsity and low rank properties of dynamic MRI data. The main focus of this paper is to further improve k - t SLR and demonstrate its utility in considerably accelerating free breathing MPI. We extend its previous implementation to account for multi-coil radial MPI acquisitions. We perform k - t sampling experiments to compare different radial trajectories and determine the best sampling pattern. We also introduce a novel augmented Lagrangian framework to considerably improve the algorithm's convergence rate. The proposed algorithm is validated using free breathing rest and stress radial perfusion data sets from two normal subjects and one patient with ischemia. k - t SLR was observed to provide faithful reconstructions at high acceleration levels with minimal artifacts compared to existing MPI acceleration schemes such as spatio-temporal constrained reconstruction and k - t SPARSE/SENSE.

The clinical utility of myocardial perfusion MR imaging (MPI) is often restricted by the inability of current acquisition schemes to simultaneously achieve high spatio-temporal resolution, good volume coverage, and high signal to noise ratio. Moreover, many subjects often find it difficult to hold their breath for sufficiently long durations making it difficult to obtain reliable MPI data. Accelerated acquisition of free breathing MPI data can overcome some of these challenges. Recently, an algorithm termed as k − t SLR has been proposed to accelerate dynamic MRI by exploiting sparsity and low rank properties of dynamic MRI data. The main focus of this paper is to further improve k − t SLR and demonstrate its utility in considerably accelerating free breathing MPI. We extend its previous implementation to account for multi-coil radial MPI acquisitions. We perform k − t sampling experiments to compare different radial trajectories and determine the best sampling pattern. We also introduce a novel augmented Lagrangian framework to considerably improve the algorithm’s convergence rate. The proposed algorithm is validated using free breathing rest and stress radial perfusion data sets from two normal subjects and one patient with ischemia. k − t SLR was observed to provide faithful reconstructions at high acceleration levels with minimal artifacts compared to existing MPI acceleration schemes such as spatio-temporal constrained reconstruction and k − t SPARSE/SENSE. [ABSTRACT FROM AUTHOR]