关键词: 纤维追踪, 最大期望聚类, 移动最小二乘法, 流线型纤维追踪算法

Abstract:

The fiber tracking algorithm can track the brain fibers through the fiber orientation distribution function. Considering that the dispersion of water molecules is mutual and the fiber orientation distribution function reconstructed from scanned data may have errors, this paper proposes a fiber tracking algorithm optimized by direction correction based on the traditional streamline tracking algorithm combined with the maximum cosine similarity. Meanwhile, considering the existence of anisotropically dispersed and isotropically dispersed water molecules in the human brain, and that the latter accounts for a larger proportion, the maximum expectation algorithm is used to cluster the seed points with the same properties to reduce the tracking of isotropically dispersed voxel points. Finally, the experiments were conducted using simulated and real data respectively, and the results show that the proposed algorithm takes less time for tracking, the average fiber length is longer compared to the traditional streamline tracking streamline tracking (STT) algorithm, the number of incorrectly tracked clusters is significantly less and the ratio of correctly tracked bundles significantly higher than that of the traditional fiber tracking algorithm. Additionally, it demonstrates a higher overlap rate and a lower overestimation rate in the tracking of most specific fiber bundles, better reflecting the structural distribution of fibers in practical scenarios.

Key words: fiber tracking, maximum expectation clustering, moving least squares, streamline tracking algorithm