Characterization and Correction of Time-Varying Eddy Currents for Diffusion MRI

TL;DR

This paper introduces TVEDDY, a novel retrospective algorithm that models time-varying eddy currents in diffusion MRI, significantly improving image quality especially for advanced gradient-intensive techniques like OGSE.

Contribution

The paper presents the first retrospective modeling of eddy current decay in diffusion MRI, enhancing correction accuracy for advanced diffusion encoding methods.

Findings

TVEDDY reduces blurring in OGSE images caused by eddy currents.

For PGSE, TVEDDY performs comparably to traditional methods.

Model-based reconstruction with field monitoring yields the lowest MSE.

Abstract

Purpose: Diffusion MRI (dMRI) suffers from eddy currents induced by strong diffusion gradients, which introduce artefacts that can impair subsequent diffusion metric analysis. Existing retrospective correction techniques that correct for diffusion gradient induced eddy currents do not account for eddy current decay, which is generally effective for traditional Pulsed Gradient Spin Echo (PGSE) diffusion encoding. However, these techniques do not necessarily apply to advanced forms of dMRI that require substantial gradient slewing, such as Oscillating Gradient Spin Echo (OGSE). Methods: An in-house algorithm (TVEDDY), that for the first time retrospectively models eddy current decay, was tested on PGSE and OGSE brain images acquired at 7T. Correction performance was compared to conventional correction methods by evaluating the mean-squared error (MSE) between diffusion-weighted images acquired with opposite polarity diffusion gradients. As a ground truth comparison, images were corrected using field dynamics up to third order in space measured using a field monitoring system. Results: Time-varying eddy currents were observed for OGSE, which introduced blurring that was not reduced using the traditional approach but was diminished considerably with TVEDDY and model-based reconstruction. No MSE difference was observed between the conventional approach and TVEDDY for PGSE, but for OGSE TVEDDY resulted in significantly lower MSE than the conventional approach. The field-monitoring-informed model-based reconstruction had the lowest MSE for both PGSE and OGSE. Conclusion: This work establishes that it is possible to estimate time-varying eddy currents from the diffusion data itself, which provides substantial image quality improvements for gradient-intensive dMRI acquisitions like OGSE.