基于混张量模型的纤维跟踪算法研究及初步应用

Study and preliminary application of the fiber tracking algorithm based on mixed-tensor model

目的研究与探讨一种新的解决纤维交叉问题的算法。方法在目前最典型的连续示踪纤维分配(FACT)算法的基础上引入双张量模型进行改进,并将改进后的算法应用到健康志愿者和中枢神经系统疾病的患者中。结果与传统的FACT相比,改进算法能够跟踪到更合理的纤维。结论改进的算法能够解决纤维交叉问题,具有潜在的临床应用价值。

Objective To study and explore a new algorithm to deal with the cross-fiber problem. Methods Based on typical FACT (Fiber Assignment with a Continuous Tracking) algorithm, we involved two-tensor model to it and developed an improved algorithm. And then we applied it to normal subjects and patients with central nervous system problems. Results Comparing with typical FACT algorithm, our newly algorithm obtained much more appropriate fibers. Conclusion The improved algorithm could deal with cross-fiber problems, which indicated that it had potential value in clinics.