The reduction of the sorting bias in the eigenvalues of the diffusion tensor
The reduction of the sorting bias in the eigenvalues of the diffusion tensor
复制标题
DOI：
10.1016/s0730-725x(99)00021-1
复制
发表时间：
1999-07-01
影响因子：
2.5
通讯作者：
Carpenter, TA
中科院分区：
文献类型：
--
作者：
Martin, KM;Papadakis, NG;Carpenter, TA
在测量系统的扩散特性时，最本质的量之一是主扩散系数的集合，它表示沿纤维轴的扩散。众所周知，当距离很近时，系统噪声是系统排序偏差的一个原因，扩散系数是根据它们的大小排序的，并导致它们的估计不准确。本文描述了一种利用局部光纤定向相干作为分类基础的主扩散系数排序新方法。将该方法应用于各向同性水幻影和健康人大脑的计算机模拟和实验数据中进行了验证。我们的结果表明，与其他技术相比，这种方法可以显著减少排序偏差，从而更准确地估计特征值。该方法优于其他提出的替代方法，因为它不依赖于大的兴趣区域平均方案。(C) 1999 Elsevier Science Inc.；
One of the most intrinsic quantities when measuring the diffusion properties of a system is the set of principal diffusivities, which represents diffusion along the fibre axes. System noise is a well-known cause of systematic sorting bias when closely spaced, diffusivities are ordered according to their magnitude-and leads to their inaccurate estimation. This paper describes a new method for the ordering of the principal diffusivities in which local fibre directional coherence was used as a basis for sorting. The method was applied and tested in computer simulations and experimental data acquired in an isotropic water phantom and healthy human brain. Our results demonstrate that this method leads to significant reduction in the sorting bias in comparison to other techniques and thus a more accurate estimation of the eigenvalues. The method is advantageous-over other proposed alternatives to the conventional magnitude sorting method because it is not reliant on a large region-of-interest averaging scheme. (C) 1999 Elsevier Science Inc.