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Study and preliminary application of an improved fiber tracking algorithm in central nervous system

机译:改进的纤维跟踪算法在中枢神经系统中的研究与初步应用

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As a noninvasive fiber visualization imaging technique, DTI has been developed rapidly in recent years. It has been considered to have high potential value in the diagnosis and treatment of the nervous system diseases. And in the applications of DTI, the fiber tracking algorithm is critical to the visualization results. So far, researchers have proposed many 3D visualization methods. Among them, Fiber Assignment by Continuous Tracking (FACT) algorithm is the most commonly used one, especially in white matter fiber tracking. But it is powerless to deal with the fiber crossing problem. In this paper, based on FACT algorithm under traditional single-tensor model, two-tensor model is enrolled to try to solve this kind of problem. We proposed a better fiber tracking way by the combination of these two models. At last, we applied it to some nervous system diseases.
机译:作为一种非侵入性的纤维可视化成像技术,DTI近年来得到了迅速的发展。人们认为它在神经系统疾病的诊断和治疗中具有很高的潜在价值。在DTI的应用中,光纤跟踪算法对于可视化结果至关重要。到目前为止,研究人员已经提出了许多3D可视化方法。其中,连续跟踪光纤分配(FACT)算法是最常用的算法,尤其是在白质光纤跟踪中。但是处理光纤交叉问题是无能为力的。本文基于传统的单张量模型下的FACT算法,引入两张量模型来尝试解决此类问题。通过结合这两种模型,我们提出了一种更好的光纤跟踪方式。最后,我们将其应用于某些神经系统疾病。

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