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Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms.
NMR in Biomedicine ( IF 2.9 ) Pub Date : 2020-02-17 , DOI: 10.1002/nbm.4267
Alexis Reymbaut 1, 2 , Paolo Mezzani 1, 3 , João P de Almeida Martins 1, 2 , Daniel Topgaard 1, 2
Affiliation  

In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor‐valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one‐dimensional Gamma fit, a generalized two‐term cumulant approach, and two‐dimensional and four‐dimensional Monte‐Carlo‐based inversion techniques, using a clinically feasible tensor‐valued acquisition scheme. In particular, we compare their performance at different signal‐to‐noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two‐dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite‐SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite‐SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two‐dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four‐dimensional Monte Carlo inversion presents no infinite‐SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated.

中文翻译:

从多维扩散信号反演算法获得的统计描述符的准确性和精度。

在生物组织中,典型的 MRI 体素包括多个微观环境,其局部组织可以被微观扩散张量捕获。因此,测量的扩散 MRI 信号可以写为体素内扩散张量分布 (DTD) 的多维拉普拉斯变换。已经设计了张量值扩散编码方案来探测 DTD 的特定特征,并且已经引入了几种算法来反转这些数据并估计 DTD 的统计描述符,例如平均扩散率、各向同性扩散率的方差和均值平方扩散各向异性。然而,这些估计的准确性和精确度尚未经过系统评估和跨方法比较。在本文中,我们执行并比较此类估计电脑模拟对于一维 Gamma 拟合、广义的两项累积量方法以及基于二维和四维蒙特卡罗的反演技术,使用临床上可行的张量值采集方案。特别是,我们比较了它们在不同信噪比 (SNR) 下的性能,因为体素内容在上述统计描述符、方向顺序以及各向同性和各向异性分量的分数方面有所不同。我们发现所有反演技术都具有相似的精度(除了二维蒙特卡罗反演的精度较低),但在精度方面有所不同。虽然 Gamma 拟合在信号强烈偏离单指数且不受方向顺序影响时表现出无限 SNR 偏差,当这种偏差源于各向同性扩散率的方差或各向异性扩散分量的低取向顺序时,广义累积量方法显示出无限 SNR 偏差。二维蒙特卡罗反演在所有研究的系统中都显示出非凡的准确性,因为该采集方案具有足够的方向来产生旋转不变的粉末平均值。四维蒙特卡罗反演没有无限 SNR 偏差,但数据中的噪声显着,同时在大多数研究的系统中保持良好的对比度。鉴于采集方案具有足够的方向来产生旋转不变的粉末平均值。四维蒙特卡罗反演没有无限 SNR 偏差,但数据中的噪声显着,同时在大多数研究的系统中保持良好的对比度。鉴于采集方案具有足够的方向来产生旋转不变的粉末平均值。四维蒙特卡罗反演没有无限 SNR 偏差,但数据中的噪声显着,同时在大多数研究的系统中保持良好的对比度。
更新日期:2020-02-17
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