Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts

TL;DR

This paper introduces a new method combining mesh morphing and PCA to analyze MRI data without averaging, preserving more information for better group comparisons.

Contribution

A novel technique integrating 3D mesh-morphing and PCA for analyzing diffusion tensor MRI data without averaging.

Findings

The new method clearly distinguished rugby players from controls, which traditional averaging could not achieve.

The approach preserves individual variability in diffusion metrics for more accurate group comparisons.

The method enables visualization of diffusion metric statistics across the whole white matter tract volume.

Abstract

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16–18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject’s native space. A template model of the right corticospinal tract was generated and morphed into each subject’s native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject’s variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.

Data reduction methods such as averaging are commonly used in high-dimensional MRI analysis, but this results in information loss. However, individual variability makes group-wise comparisons difficult without averaging. Tayebi et al. introduces a novel method that combines 3D mesh-morphing techniques with principal component analysis that allows MRI metric group comparisons without averaging.

Graphical Abstract

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