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Updated: Jun 22, 2025
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Diffusion Model-based FOD Restoration from High Distortion in dMRI
Shuo Huang1,2, Lujia Zhong1,3, Yonggang Shi1,2,3
1Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA.
Abstract:
Fiber orientation distributions (FODs) is a popular model to represent the diffusion MRI (dMRI) data. However, imaging artifacts such as susceptibility-induced distortion in dMRI can cause signal loss and lead to the corrupted reconstruction of FODs, which prohibits successful fiber tracking and connectivity analysis in affected brain regions such as the brain stem. Generative models, such as the diffusion models, have been successfully applied in various image restoration tasks. However, their application on FOD images poses unique challenges since FODs are 4-dimensional data represented by spherical harmonics (SPHARM) with the 4-th dimension exhibiting order-related dependency. In this paper, we propose a novel diffusion model for FOD restoration that can recover the signal loss caused by distortion artifacts. We use volume-order encoding to enhance the ability of the diffusion model to generate individual FOD volumes at all SPHARM orders. Moreover, we add cross-attention features extracted across all SPHARM orders in generating every individual FOD volume to capture the order-related dependency across FOD volumes. We also condition the diffusion model with low-distortion FODs surrounding high-distortion areas to maintain the geometric coherence of the generated FODs. We trained and tested our model using data from the UK Biobank (n = 1315). On a test set with ground truth (n = 43), we demonstrate the high accuracy of the generated FODs in terms of root mean square errors of FOD volumes and angular errors of FOD peaks. We also apply our method to a test set with large distortion in the brain stem area (n = 1172) and demonstrate the efficacy of our method in restoring the FOD integrity and, hence, greatly improving tractography performance in affected brain regions.
Insights
This study introduces a novel diffusion model to restore corrupted fiber orientation distributions (FODs) in diffusion MRI (dMRI) data, improving brain connectivity analysis in distorted regions like the brain stem.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Computational Neuroscience
Background:
- Diffusion MRI (dMRI) data is commonly modeled using Fiber Orientation Distributions (FODs).
- Susceptibility-induced distortions in dMRI corrupt FOD reconstruction, hindering tractography and connectivity analysis, particularly in brain regions like the brain stem.
- Existing generative models, like diffusion models, show promise for image restoration but face challenges with the 4D nature and order-dependency of FODs.
Purpose of the Study:
- To develop a novel diffusion model for restoring Fiber Orientation Distributions (FODs) affected by imaging artifacts.
- To address the unique challenges of applying diffusion models to 4D FOD data, including spherical harmonics (SPHARM) order dependency.
- To improve the accuracy of FOD reconstruction and enhance tractography performance in distorted brain regions.
Main Methods:
- Proposed a novel diffusion model incorporating volume-order encoding to generate FOD volumes across all SPHARM orders.
- Integrated cross-attention features across SPHARM orders to capture inter-order dependencies during FOD generation.
- Conditioned the model on surrounding low-distortion FODs to preserve geometric coherence in high-distortion areas.
- Trained and validated the model using UK Biobank data (n=1315), with ground truth testing on n=43.
Main Results:
- Demonstrated high accuracy in generated FOD volumes and FOD peaks using root mean square errors and angular errors on a ground truth test set.
- Successfully applied the method to a large dataset (n=1172) with significant brain stem distortion, restoring FOD integrity.
- Showcased substantial improvements in tractography performance in brain regions affected by distortion artifacts.
Conclusions:
- The proposed diffusion model effectively restores signal loss in FODs caused by distortion artifacts.
- The model's architecture, including volume-order encoding and cross-attention, successfully handles the complexity of 4D FOD data.
- This restoration technique significantly enhances the reliability of fiber tracking and connectivity analysis in challenging neuroimaging datasets.
