Parametrization of white matter manifold-like structures using principal surfaces

TL;DR

This paper introduces a geometrically motivated algorithm to parameterize white matter tracts like the corpus callosum using principal surfaces, enabling visualization and analysis of diffusion tensor imaging data.

Contribution

The authors develop a novel surface-based method to construct and flatten principal surfaces of white matter tracts from DTI data, applicable to various diffusion metrics and tract types.

Findings

Algorithm demonstrates fast convergence and robustness in simulations.

Applied to a large MS patient dataset with 466 scans, showing practical utility.

Provides detailed surface maps of FA values for white matter analysis.

Abstract

In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. We approach the problem by finding a geometrically motivated surface-based representation of the corpus callosum and visualize the fractional anisotropy (FA) values projected onto the surface; the method applies to any other diffusion summary as well as to other white matter tracts. We provide an algorithm that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm was applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.