Fast Reconstruction of Motion‐Corrupted Data With Mobile‐GRAPPA: Motion and δB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

Purpose To develop an accurate and computationally efficient motion‐corrected MRI reconstruction framework that incorporates hundreds to thousands of motion and estimates from high‐temporal‐resolution tracking. Methods We propose Mobile‐GRAPPA, a k‐space preprocessing approach that uses MLP‐parameterized local GRAPPA operators to jointly correct trajectory perturbations, coil reweighting, and ‐induced phase changes before standard downstream reconstruction. Reconstruction accuracy, noise propagation, spatial resolution, and runtime were evaluated using 3D MPRAGE, multi‐echo 3D GRE, and 3D EPTI. Results Experiments with discrete motion states demonstrated that Mobile‐GRAPPA followed by standard SENSE achieved image quality comparable to Aligned‐SENSE. In 3D GRE with 1620 tracked states and 3D EPTI with 544 tracked states, Mobile‐GRAPPA incorporated all state estimates with minimal motion‐correction overhead. Total reconstruction times were approximately 15 s for GRE and 20 min for EPTI, whereas full‐state Aligned‐SENSE was computationally prohibitive (approximately 10 h for GRE and multiple days for EPTI). Pseudo‐replica and PSF analyses showed limited additional noise amplification and negligible spatial‐resolution loss. Conclusion Mobile‐GRAPPA enables dense motion and information to be incorporated with minimal motion‐correction overhead while preserving standard SENSE, subspace, and other downstream reconstruction pipelines.