Fast Reconstruction of Motion-Corrupted Data With Mobile-GRAPPA: Motion and δ B 0 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 δ B 0 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 δ B 0 -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 δ B 0 information to be incorporated with minimal motion-correction overhead while preserving standard SENSE, subspace, and other downstream reconstruction pipelines.