A novel framework for in-vivo diffusion tensor distribution MRI of the human brain.
A novel framework for in-vivo diffusion tensor distribution MRI of the human brain.
复制标题
人脑体内扩散张量分布 MRI 的新框架。
DOI：
10.1016/j.neuroimage.2023.120003
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发表时间：
2023
期刊：
影响因子：
5.7
通讯作者：
Basser,PeterJ
中科院分区：
文献类型：
--
作者：
Magdoom,KulamNajmudeen;Avram,AlexandruV;Sarlls,JoelleE;Dario,Gasbarra;Basser,PeterJ
神经组织微观结构在发育、生理和病理生理过程中发挥着重要作用。扩散张量分布 (DTD) MRI 通过使用以扩散张量的概率密度函数为特征的非交换室集合来描述体素内的水扩散，从而帮助探测亚体素异质性。在这项研究中，我们提供了一个新的框架，用于获取多重扩散编码（MDE）图像并从中估计人脑体内的 DTD。我们将脉冲场梯度 (iPFG) 融入单个自旋回波中，以生成任意一阶、二阶或三阶 b 张量，而不会引入伴随的梯度伪影。采用明确定义的扩散编码参数，我们证明 iPFG 保留了传统多 PFG (mPFG/MDE) 序列的显着特征，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，其张量随机变量被限制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尺寸、形状和方向分布的微扩散张量，以最好地拟合测量的 MDE 图像。从这些张量中，我们获得了扩散张量椭球尺寸和形状的光谱，以及解开体素内潜在异质性的微观取向分布函数 (μ ODF) 和微观分数各向异性 (μ FA)。使用 DTD 衍生的 μ ODF，我们引入了一种新方法来执行纤维束成像，能够解析复杂的纤维配置。结果揭示了灰质和白质各个区域的微观各向异性以及小脑灰质中以前未观察到的偏态 MD 分布。 DTD MRI 纤维束成像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) 相关的一些简并性，并阐明了扩散异质性的来源，这可能有助于改善各种神经系统疾病和紊乱的诊断。
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 (μ ODF) and microscopic fractional anisotropy (μ FA) that disentangle the underlying heterogeneity within a voxel. Using the DTD-derived μ ODF, 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.