Tolga ukur1, Juan Santos1, John Pauly1, Dwight Nishimura1
1Department of Electrical Engineering,
PILS is a very fast reconstruction method for both Cartesian and non-Cartesian sampling; however, it can suffer from residual aliasing artifacts when coupled with variable-density acquisitions. In this work, we propose an improved variable-FOV method that suppresses the aliasing artifacts, while optimally utilizing the densely sampled low-spatial-frequency data. Individual coil images are then linearly combined using data-driven sensitivity estimates. In vivo comparisons with PILS and SENSE are provided.