Optimization and Validation of Accelerated Golden-angle Radial Sparse MRI Reconstruction with Self-Calibrating GRAPPA Operator Gridding

摘要

PurposeGolden-angle RAdial Sparse Parallel (GRASP) MRI reconstruction requires gridding and re-gridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging.