A new framework for MR diffusion tensor distribution.

A new framework for MR diffusion tensor distribution.
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DOI:
10.1038/s41598-021-81264-x
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发表时间:
2021-02-02
期刊:
影响因子:
4.6
通讯作者:
Basser PJ
Basser PJ
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Magdoom KN;Pajevic S;Dario G;Basser PJ

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在体内非侵入性地表征宏观 MRI 体素内的异质和各向异性水扩散过程的能力是生物学、神经科学和医学的迫切需要。虽然 MRI 体素可能包含大约一微升的组织,但我们的目标是检查皮升量级的体素内扩散过程。在这里,我们提出了一个新的理论框架和有效的实验设计来描述和测量这种体素内结构异质性和各向异性。我们假设约束正态张量变量分布(CNTVD)描述了体素内正定扩散张量的可变性,这将其适用性扩展到广泛的 b 值,同时保留了与现有模型不同的扩散张量分布(DTD)范式的丰富性。我们引入了一种新的蒙特卡罗 (MC) 方案来合成真实的 6D DTD 数值模型并反转 MR 信号。我们证明信号反演是适定的,并通过利用 CNTVD 均值和协方差张量的不同对称性来简单地估计 CNTVD 参数。通过向计算的 MR 信号添加噪声并与地面实况进行比较来评估估计管道的鲁棒性。还提出了表征体素内微观形状、大小和方向异质性的一系列不变参数和字形。
The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.
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