White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging

TL;DR

This paper introduces WMG-Diff, a novel deep learning diffusion model guided by white matter geometry, designed to impute tissue microstructure in tractography imaging, improving accuracy over existing methods.

Contribution

The work presents a new geometry-guided diffusion model for tissue microstructure imputation, incorporating white matter atlas information for enhanced downstream analysis.

Findings

Outperforms state-of-the-art methods in microstructure imputation.

Achieves higher accuracy in downstream phenotype prediction.

Validated on a large dataset of 9,342 subjects.

Abstract

Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype classifications. However, parcellation does not always reach 100\% accuracy due to various factors, including inter-individual anatomical variability and the quality of neuroimaging scan data. The failure to identify parcels causes a problem of missing microstructure data values, which is especially challenging for downstream tasks that analyze large brain datasets. In this work, we propose a novel deep-learning model to impute tissue microstructure: the White Matter Geometry-guided Diffusion (WMG-Diff) model. Specifically, we first propose a deep score-based guided diffusion model to impute tissue microstructure for diffusion magnetic resonance imaging (dMRI) tractography fiber clusters. Second, we propose a white matter atlas geometric relationship-guided denoising function to guide the reverse denoising process at the subject-specific level. Third, we train and evaluate our model on a large dataset with 9342 subjects. Comprehensive experiments for tissue microstructure imputation and a downstream non-imaging phenotype prediction task demonstrate that our proposed WMG-Diff outperforms the compared state-of-the-art methods in both error and accuracy metrics. Our code will be available at: https://github.com/SlicerDMRI/WMG-Diff.