A Novel Dual-Stream Framework for dMRI Tractography Streamline Classification with Joint dMRI and fMRI Data
This work addresses a bottleneck in streamline classification for neuroimaging researchers by integrating functional data to improve tract parcellation, though it is incremental as it builds on existing methods with a novel hybrid approach.
The authors tackled the problem of distinguishing functionally distinct fiber tracts with similar pathways in dMRI tractography by introducing a dual-stream framework that jointly analyzes dMRI and fMRI data, resulting in superior performance in parcellating the corticospinal tract into its four somatotopic subdivisions.
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.