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一种用于人体大脑活体扩散张量分布 MRI 的新框架。

A novel framework for in-vivo diffusion tensor distribution MRI of the human brain.

机构信息

Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20892, USA; Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF) Inc., Bethesda, MD, USA.

National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.

出版信息

Neuroimage. 2023 May 1;271:120003. doi: 10.1016/j.neuroimage.2023.120003. Epub 2023 Mar 11.

Abstract

Neural tissue microstructure plays an important role in developmental, physiological and pathophysiological processes. Diffusion tensor distribution (DTD) MRI helps probe subvoxel heterogeneity by describing water diffusion within a voxel using an ensemble of non-exchanging compartments characterized by a probability density function of diffusion tensors. In this study, we provide a new framework for acquiring multiple diffusion encoding (MDE) images and estimating DTD from them in the human brain in vivo. We interfused pulsed field gradients (iPFG) in a single spin echo to generate arbitrary b-tensors of rank one, two, or three without introducing concomitant gradient artifacts. Employing well-defined diffusion encoding parameters we show that iPFG retains salient features of a traditional multiple-PFG (mPFG/MDE) sequence while reducing the echo time and coherence pathway artifacts thereby extending its applications beyond DTD MRI. Our DTD is a maximum entropy tensor-variate normal distribution whose tensor random variables are constrained to be positive definite to ensure their physicality. In each voxel, the second-order mean and fourth-order covariance tensors of the DTD are estimated using a Monte Carlo method that synthesizes micro-diffusion tensors with corresponding size, shape, and orientation distributions to best fit the measured MDE images. From these tensors we obtain the spectrum of diffusion tensor ellipsoid sizes and shapes, and the microscopic orientation distribution function (μODF) and microscopic fractional anisotropy (μFA) that disentangle the underlying heterogeneity within a voxel. Using the DTD-derived μODF, we introduce a new method to perform fiber tractography capable of resolving complex fiber configurations. The results revealed microscopic anisotropy in various gray and white matter regions and skewed MD distributions in cerebellar gray matter not observed previously. DTD MRI tractography captured complex white matter fiber organization consistent with known anatomy. DTD MRI also resolved some degeneracies associated with diffusion tensor imaging (DTI) and elucidated the source of diffusion heterogeneity which may help improve the diagnosis of various neurological diseases and disorders.

摘要

神经组织微观结构在发育、生理和病理生理过程中起着重要作用。弥散张量分布(DTD)MRI 通过使用由扩散张量概率密度函数描述的具有非交换隔室特征的集合来描述体素内的水扩散,从而有助于探测亚体素异质性。在这项研究中,我们提供了一种在体内人脑获取多个扩散编码(MDE)图像并从这些图像中估计 DTD 的新框架。我们将脉冲场梯度(iPFG)融合到单个自旋回波中,以在不引入伴随梯度伪影的情况下生成任意秩一、二或三的 b-张量。采用定义明确的扩散编码参数,我们表明 iPFG 保留了传统的多梯度场(mPFG/MDE)序列的显著特征,同时减少了回波时间和相干路径伪影,从而将其应用扩展到 DTD MRI 之外。我们的 DTD 是最大熵张量变量正态分布,其张量随机变量被约束为正定以确保其物理性。在每个体素中,使用蒙特卡罗方法估计 DTD 的二阶均值和四阶协方差张量,该方法合成具有相应大小、形状和方向分布的微扩散张量,以最佳拟合测量的 MDE 图像。从这些张量中,我们获得了扩散张量椭球大小和形状的谱,以及微观取向分布函数(μODF)和微观各向异性分数(μFA),它们可以分解体素内的潜在异质性。使用 DTD 衍生的 μODF,我们引入了一种新的方法来进行纤维追踪,能够解析复杂的纤维构型。结果显示,在各种灰质和白质区域存在微观各向异性,以及以前未观察到的小脑灰质 MD 分布偏斜。DTD MRI 纤维追踪捕获了与已知解剖结构一致的复杂白质纤维组织。DTD MRI 还解决了与扩散张量成像(DTI)相关的一些退化问题,并阐明了扩散异质性的来源,这可能有助于提高各种神经疾病和障碍的诊断水平。

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