Sulcal Pattern Matching with the Wasserstein Distance

TL;DR

This paper introduces a novel computational framework using Wasserstein distance for nonlinear alignment and comparison of human brain sulcal patterns from MRI data.

Contribution

It develops a mathematical and algorithmic approach for sulcal pattern matching, addressing topological differences across subjects.

Findings

Successfully aligned sulcal patterns across subjects.

Quantified registration performance effectively.

Identified gender differences in sulcal patterns.

Abstract

We present the unified computational framework for modeling the sulcal patterns of human brain obtained from the magnetic resonance images. The Wasserstein distance is used to align the sulcal patterns nonlinearly. These patterns are topologically different across subjects making the pattern matching a challenge. We work out the mathematical details and develop the gradient descent algorithms for estimating the deformation field. We further quantify the image registration performance. This method is applied in identifying the differences between male and female sulcal patterns.