Fast Reconstruction of Motion-Corrupted Data with Mobile-GRAPPA: Motion and dB0 Inhomogeneity Correction Leveraging Efficient GRAPPA

TL;DR

Mobile-GRAPPA is a fast, efficient method for correcting motion and dB0 inhomogeneity in MRI data, enabling high-quality reconstructions without extensive computational costs.

Contribution

It introduces a lightweight, kernel-based approach using a neural network to incorporate high-temporal-resolution motion and dB0 information into SENSE reconstruction.

Findings

Enabled accurate reconstruction of highly motion-corrupted data

Achieved significant reduction in reconstruction time compared to full Aligned-SENSE

Demonstrated effectiveness on large, real-world MRI datasets

Abstract

Advanced motion navigations now enable rapid tracking of subject motion and dB0-induced phase, but accurately incorporating this high-temporal-resolution information into SENSE (Aligned-SENSE) is often computationally prohibitive. We propose "Mobile-GRAPPA", a k-space "cleaning" approach that uses local GRAPPA operators to remove motion and dB0 related corruption so that the resulting data can be reconstructed with standard SENSE. We efficiently train a family of k-space-position-specific Mobile-GRAPPA kernels via a lightweight multilayer perceptron (MLP) and apply them across k-space to generate clean data. In experiments on highly motion-corrupted 1-mm whole-brain GRE (Tacq = 10 min; 1,620 motion/dB0 trackings) and EPTI (Tacq = 2 min; 544 trackings), Mobile-GRAPPA enabled accurate reconstruction with negligible time penalty, whereas full Aligned-SENSE was impractical (reconstruction times > 10 h for GRE and > 10 days for EPTI). These results show that Mobile-GRAPPA incorporates detailed motion and dB0 tracking into SENSE with minimal computational overhead, enabling fast, high-quality reconstructions of challenging data.