A robust fiber clustering method for tract refinement

Abstract Diffusion tensor imaging fiber tracking (DTI-FT) could delineate neuronal fiber tracts, and it has been widely accepted in clinical practices. Due to the two reasons of image noise and low resolution of DTI, DTI-FT would easily lead to false propagations. In this paper, we propose a robust fiber clustering (FC) approach to diminish false fibers from one fiber tract. Our method consists of two stages: First, the optimized FT using single-tensor fiber assignment continuous tracking (FACT) is implemented to demonstrate one fiber tract; second, each curved fiber in the fiber tract is mapped to a point by kernel principal component analysis (KPCA), and then the point clouds of fiber tract are labeled by hierarchical clustering which could distinguish false fibers from true fibers in one tract. In our experiment, the corticospinal tract (CST) in one case of human data in vivo was used to validate our method. Compared with the k-most similar fibers method, our method showed more reliable capability in decreasing the false fibers in one tract. In conclusion, our method could effectively optimize the visualization of fiber bundles and would help a lot in the field of fiber tract evaluation.