mlsanityData to 13 Oct 2026

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

arXiv 2407.19460 ↗First posted 28 Jul 2024cs.CVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 23 of its 41 references that are in the map. First posted on arXiv 28 Jul 2024.

Builds on

23 of 41 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models6 Feb

Other references · 22

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,223
  3. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,112
  4. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 671
  5. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 579
  6. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 350
  7. 2021Ensemble deep learning: A reviewcited by 284
  8. 2022Diffusion models for missing value imputation in tabular datacited by 77
  9. 2023AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Modelscited by 46
  10. 2022Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitionscited by 19
  11. 2022Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approachcited by 17
  12. 2021Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellationcited by 16
10 more
  1. 2022SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep Learningcited by 13
  2. 2023TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performancecited by 11
  3. 2023TractGraphCNN: anatomically informed graph CNN for classification using diffusion MRI tractographycited by 10
  4. 2022TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractographycited by 9
  5. 2023Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methodscited by 7
  6. 2024TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractographycited by 7
  7. 2021PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traitscited by 6
  8. 2023Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypescited by 5
  9. 2024Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Predictioncited by 5
  10. 2023Imputing Brain Measurements Across Data Sets via Graph Neural Networkscited by 1

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning studycited by 4
  2. 2025A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractographycited by 0

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Close in wording, no citation either way

  1. 20253D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
  2. 2025DDTracking: A Deep Generative Framework for Diffusion MRI Tractography with Streamline Local-Global Spatiotemporal Modeling
  3. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
  4. 2025Exploring Robustness of Cortical Morphometry in the presence of white matter lesions, using Diffusion Models for Lesion Filling
  5. 2024Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
  6. 2024Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
  7. 2023DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
  8. 2026Multiscale Structure-Guided Latent Diffusion for Multimodal MRI Translation