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Brain white matter tractography based on riemannian manifold

机译:基于黎曼流形的脑白质图像

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Diffusion tensor imaging (DTI) is the only noninvasive technique of analyzing and qualifying water molecule's diffusion anisotropy in brain tissues. This paper presented a novel algorithm to analyze DTI and brain white matter tractography based on Riemannian manifold. Firstly, a 3x3 symmetric positive definite covariant tensor was constructed for each voxel using DTI, so brain white matte can be represented as a tensor field. Secondly, the tensor field was regarded as Riemannian manifold, and the fluid motion in the tensor field was represented by Navier-Stoke equation, so the problem of brain white matter tractography between two voxels can be transformed into the computation of smallest distance between two points in Riemannian manifold. Finally, distances between two points in Riemannian manifold can be represented by geodesic, and the numerical solution was based on Level-Set method, which was the brain white matter tractography. In experiment, this paper compared our method and the traditional algorithm based on a digital DTI phantom. The experiment result showed that our method could accurately retrieve the DTI tractography, and was more robust than traditional algorithm.
机译:扩散张量成像(DTI)是分析和鉴定脑组织中水分子扩散各向异性的唯一非侵入性技术。本文提出了一种新的基于黎曼流形的DTI和脑白质图像分析算法。首先,使用DTI为每个体素构造一个3x3对称正定协变张量,因此脑白面可以表示为张量场。其次,将张量场视为黎曼流形,并用Navier-Stoke方程表示张量场中的流体运动,因此可以将两个体素之间的脑白质束摄影问题转化为计算两点之间最小距离的问题。在黎曼流形中最终,黎曼流形中两点之间的距离可以用测地线表示,数值解基于Level-Set方法,即脑白质束描记法。在实验中,本文将我们的方法与基于数字DTI体模的传统算法进行了比较。实验结果表明,该方法能够准确地检索DTI图像,并且比传统算法具有更好的鲁棒性。

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