FOD-Diff: 3D Multi-Channel Patch Diffusion Model for Fiber Orientation Distribution
This addresses the challenge of efficiently generating accurate FODs for white matter characterization in diffusion MRI, which is incremental as it builds on existing diffusion models with novel adaptations.
The paper tackles the problem of estimating high angular resolution fiber orientation distribution (HAR-FOD) from low angular resolution dMRI (LAR-FOD) to improve accuracy without long scanning times, and the result is a method that achieves the best performance in HAR-FOD prediction, outperforming other state-of-the-art methods.
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.