A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data

TL;DR

This paper presents a dual-stream framework combining dMRI and fMRI data to improve white matter tract classification, addressing limitations of geometric-only methods and enhancing functional specificity.

Contribution

Introduces a novel dual-stream neural network that jointly analyzes dMRI and fMRI data for more accurate streamline classification and tract parcellation.

Findings

Outperforms existing methods in tract classification accuracy.

Effectively parcellates corticospinal tract into somatotopic subdivisions.

Demonstrates the benefit of integrating functional data with structural tractography.

Abstract

Streamline classification is essential to identify anatomically meaningful white matter tracts from diffusion MRI (dMRI) tractography. However, current streamline classification methods rely primarily on the geometric features of the streamline trajectory, failing to distinguish between functionally distinct fiber tracts with similar pathways. To address this, we introduce a novel dual-stream streamline classification framework that jointly analyzes dMRI and functional MRI (fMRI) data to enhance the functional coherence of tract parcellation. We design a novel network that performs streamline classification using a pretrained backbone model for full streamline trajectories, while augmenting with an auxiliary network that processes fMRI signals from fiber endpoint regions. We demonstrate our method by parcellating the corticospinal tract (CST) into its four somatotopic subdivisions. Experimental results from ablation studies and comparisons with state-of-the-art methods demonstrate our approach's superior performance.