Mapping Tractography Across Subjects

TL;DR

This paper introduces a novel graph-based method to directly map corresponding streamlines between tractographies across subjects without relying on traditional image registration, aiming to improve white matter analysis.

Contribution

It proposes a new approach using graph matching and combinatorial optimization to find direct streamline correspondences in tractography data.

Findings

Preliminary results show promising comparison with standard registration methods.

The method effectively identifies streamline correspondences in corticospinal tract segmentation.

Simulated annealing used for optimization demonstrates potential in tractography mapping.

Abstract

Diffusion magnetic resonance imaging (dMRI) and tractography provide means to study the anatomical structures within the white matter of the brain. When studying tractography data across subjects, it is usually necessary to align, i.e. to register, tractographies together. This registration step is most often performed by applying the transformation resulting from the registration of other volumetric images (T1, FA). In contrast with registration methods that "transform" tractographies, in this work, we try to find which streamline in one tractography correspond to which streamline in the other tractography, without any transformation. In other words, we try to find a "mapping" between the tractographies. We propose a graph-based solution for the tractography mapping problem and we explain similarities and differences with the related well-known graph matching problem. Specifically, we define a loss function based on the pairwise streamline distance and reformulate the mapping problem as combinatorial optimization of that loss function. We show preliminary promising results where we compare the proposed method, implemented with simulated annealing, against a standard registration techniques in a task of segmentation of the corticospinal tract.