用低分辨率扫描数据快速生成高精度脑纤维方向图
FOD-Diff: 3D Multi-Channel Patch Diffusion Model for Fiber Orientation Distribution
- 分块扩散模型结合解剖先验，提升学习效率
- 在多项指标上超越现有方法，最高提升12.3%
- 适合需要快速脑连接成像的研究者使用
扩散MRI（dMRI）是无创表征白质完整性的重要技术，用于估计纤维方向分布（FOD）。从单层低角分辨率dMRI（LAR-FOD）估计FOD精度有限，而多层高角分辨率dMRI（HAR-FOD）虽更准确，但扫描时间长，应用受限。扩散模型可基于LAR-FOD预测HAR-FOD，但因FOD包含大量球谐系数（SH），建模难度大。本文提出3D多通道分块扩散模型，通过引入解剖先验的FOD分块适配器实现高效分块学习，并设计体素级条件协调模块增强全局理解，同时引入SH注意力机制捕捉系数间复杂关联。实验表明，该方法在HAR-FOD预测中性能最优，显著优于当前主流方法，在平均相关系数（MCC）等指标上最高提升12.3%。
原文摘要 · Abstract (English)
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.
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