mlsanityData to 7 Oct 2026

FOD-Diff: 3D Multi-Channel Patch Diffusion Model for Fiber Orientation Distribution

arXiv 2512.16075 ↗First posted 18 Dec 2025cs.CV cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Diffusion MRI (dMRI) is a critical non-invasive technique to estimate fiber orientation distribution (FOD) for characterizing white matter integrity. Estimating FOD from single-shell low angular resolution dMRI (LAR-FOD) is limited by accuracy, whereas estimating FOD from multi-shell high angular resolution dMRI (HAR-FOD) requires a long scanning time, which limits its applicability. Diffusion models have shown promise in estimating HAR-FOD based on LAR-FOD. However, using diffusion models to efficiently generate HAR-FOD is challenging due to the large number of spherical harmonic (SH) coefficients in FOD. Here, we propose a 3D multi-channel patch diffusion model to predict HAR-FOD from LAR-FOD. We design the FOD-patch adapter by introducing the prior brain anatomy for more efficient patch-based learning. Furthermore, we introduce a voxel-level conditional coordinating module to enhance the global understanding of the model. We design the SH attention module to effectively learn the complex correlations of the SH coefficients. Our experimental results show that our method achieves the best performance in HAR-FOD prediction and outperforms other state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 12 of its 26 references that are in the map. First posted on arXiv 18 Dec 2025.

Builds on

12 of 26 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis5 Jun
  2. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis29 May
  3. 2023CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis24 Mar
  4. 2023DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution24 Mar

Other references · 8

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  3. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  4. 2023Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Modelscited by 131
  5. 2019COCO-GAN: Generation by Parts via Conditional Coordinatingcited by 67
  6. 2023Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Gliomacited by 12
  7. 2024FOD-Swin-Net: angular super resolution of fiber orientation distribution using a transformer-based deep modelcited by 4
  8. 2023Recovering high-quality FODs from a reduced number of diffusion-weighted images using a model-driven deep learning architecturecited by 3

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  1. 2024Diffusion Model-based FOD Restoration from High Distortion in dMRI
  2. 2020Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
  3. 2024Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
  4. 2024QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
  5. 2024X-Diffusion: Generating Detailed 3D MRI Volumes From a Single Image Using Cross-Sectional Diffusion Models
  6. 20253D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
  7. 2025Pattern-Aware Diffusion Synthesis of fMRI/dMRI with Tissue and Microstructural Refinement
  8. 2025Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction