  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 state-of-the-art
methods.
