White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging
<|reference_start|>White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging: 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.<|reference_end|>
@article{lo2024white,
    title={White Matter Geometry-Guided Score-Based Diffusion Model for Tissue
  Microstructure Imputation in Tractography Imaging},
    author={Yui Lo, Yuqian Chen, Fan Zhang, Dongnan Liu, Leo Zekelman, Suheyla
  Cetin-Karayumak, Yogesh Rathi, Weidong Cai, Lauren J. O'Donnell},
    journal={arXiv preprint arXiv:2407.19460},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19460},
    primaryClass={cs.CV}
}