基于非局部约束球面反卷积模型的纤维追踪算法

基于扩散磁共振成像的纤维追踪技术为非侵入性观测脑白质结构提供了有力的手段，约束球面反卷积作为一种多纤维追踪模型，能够对体素内纤维的方向信息进行建模，进而实现脑纤维的重构.针对约束球面反卷积模型的不适定性以及细节信息丢失问题，本文在约束球面反卷积的基础上，结合邻域信息和分数阶正则化，提出了一种基于非局部约束球面反卷积模型的确定型纤维追踪算法，分数阶的非局部特性使得纤维方向分布模型估计的误差更小，而邻域信息的引入保证了空间一致性，可以减少噪声的影响.分别利用模拟数据、人脑实际数据对本文算法及基于约束球面反卷积的确定型纤维追踪算法作对比实验，结果表明，利用本文算法追踪的纤维不仅整体视觉效果上较整洁，而且对交叉纤维的重建结果更完整准确.

A Fiber Tracking Algorithm Based on Non-Local Constrained Spherical Deconvolution

Fiber tracking with diffusion magnetic resonance imaging provides a powerful tool for non-invasive observation of white matter in the brain. Constrained spherical deconvolution (CSD) is a multi-fiber tracking model, which can model the orientation of fibers in the voxel and achieve brain fiber reconstruction. This paper proposes a deterministic fiber tracking algorithm based on a non-local CSD model that combines neighborhood information and fractional regularization. The algorithm aimed to solve the ill-posed problem and loss detailed information in the conventional CSD model. The nonlocality of fractional order reduced the errors of fiber orientation distribution estimation, and the neighborhood information was used to ensure spatial consistency, reducing the effects of random noise. Simulation data and experimental human brain data were used to compare the performance of the proposed algorithm and the conventional CSD deterministic tracking algorithm. The results demonstrated that the proposed algorithm produced not only better overall visual effect, but also more complete and accurate reconstruction of the crossing fibers.