Accelerating Iterative SENSE-based Algorithms for Cartesian Trajectories using the Point Spread Function and Coil Compression

For multi-shot diffusion-weighted imaging, iterative SENSE-based algorithms like POCSMUSE are boosting SNR allowing for higher image resolution. These advantages are achieved at the cost of higher computational load, thereby narrowing the clinical use case. The Cartesian implementation of such SENSE algorithms iteratively involves time-consuming 1D-Fast Fourier Transforms. In this abstract, the well-known point spread function for regular Cartesian undersampling is exploited to accelerate gradient- and projection-based SENSE updates. Accelerations of approximately 45 coil compression is evaluated for these algorithms.