Self-Navigated, Retrospective, Data-Consistent Motion Correction for MPnRAGE

TL;DR

This paper introduces an automated motion correction method for 3D radial T1-weighted imaging that improves image quality and test-retest reliability in the presence of motion.

Contribution

A novel data-consistent, self-navigated motion correction method with error-based weighting and improved timing resolution for 3D radial T1-weighted imaging.

Findings

The method improved image quality across various motion types, including cases previously unusable.

Test-retest reliability of cortical thickness measures improved significantly in pediatric subjects.

Faster temporal correction rates further enhanced reliability metrics.

Abstract

To extend and automate a data-consistent, self-navigated motion-correction method for 3D radial T1-weighted imaging.

This method incorporated rigid-body motion effects into the forward model, solving for parameters that maximize consistency with the data. The method was tested on five datasets with a range of motion types and severities. A separate collection of datasets was used to study the effect that the method has on the test-retest reliability of cortical thickness estimates.

Image quality was improved across a wide range of distinct motion types, including some cases that would have been unusable if left uncorrected. The error-based weighting scheme and the increased timing resolution afforded by the proposed method were especially useful in cases of extreme and rapid motions. Moreover, the method improved test-retest reliability of cortical thickness measures in pediatric subjects, decreasing the average coefficient of variation from 2.73% ± 1.75% in uncorrected images (with freesurfer failing on one subject) down to 0.88% ± 0.21% for images corrected at ~ 2 s timing resolution and 0.79% ± 0.16% when corrected at faster temporal rates.

This method was found to be effective when used on T1-weighted radial data, both qualitatively and quantitatively. The fine-scale timing resolution and error-based weighting afforded by this technique will likely provide only a small benefit, unless one is investigating motion-prone populations or is searching for a very small effect size.

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