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定量扩散张量磁共振成像所阐明的组织微观结构和生理特征。

Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI.

作者信息

Basser P J, Pierpaoli C

机构信息

Biomedical Engineering and Instrumentation Program, NCRR, NINDS, Bethesda, Maryland 20892-5766, USA.

出版信息

J Magn Reson B. 1996 Jun;111(3):209-19. doi: 10.1006/jmrb.1996.0086.

Abstract

Quantitative-diffusion-tensor MRI consists of deriving and displaying parameters that resemble histological or physiological stains, i.e., that characterize intrinsic features of tissue microstructure and microdynamics. Specifically, these parameters are objective, and insensitive to the choice of laboratory coordinate system. Here, these two properties are used to derive intravoxel measures of diffusion isotropy and the degree of diffusion anisotropy, as well as intervoxel measures of structural similarity, and fiber-tract organization from the effective diffusion tensor, D, which is estimated in each voxel. First, D is decomposed into its isotropic and anisotropic parts, [D] I and D - [D] I, respectively (where [D] = Trace(D)/3 is the mean diffusivity, and I is the identity tensor). Then, the tensor (dot) product operator is used to generate a family of new rotationally and translationally invariant quantities. Finally, maps of these quantitative parameters are produced from high-resolution diffusion tensor images (in which D is estimated in each voxel from a series of 2D-FT spin-echo diffusion-weighted images) in living cat brain. Due to the high inherent sensitivity of these parameters to changes in tissue architecture (i.e., macromolecular, cellular, tissue, and organ structure) and in its physiologic state, their potential applications include monitoring structural changes in development, aging, and disease.

摘要

定量扩散张量磁共振成像包括推导和显示类似于组织学或生理学染色的参数,即表征组织微观结构和微动力学内在特征的参数。具体而言,这些参数是客观的,并且对实验室坐标系的选择不敏感。在此,利用这两个特性从有效扩散张量D中推导出体素内扩散各向同性和扩散各向异性程度的测量值,以及体素间结构相似性和纤维束组织的测量值,其中有效扩散张量D是在每个体素中估计得到的。首先,将D分别分解为其各向同性和各向异性部分,即[D]I和D - [D]I(其中[D] = Trace(D)/3是平均扩散率,I是单位张量)。然后,使用张量(点)积算子生成一系列新的旋转和平移不变量。最后,从活体猫脑的高分辨率扩散张量图像(其中通过一系列二维傅里叶变换自旋回波扩散加权图像在每个体素中估计D)生成这些定量参数的图谱。由于这些参数对组织结构(即大分子、细胞、组织和器官结构)及其生理状态变化具有高度的内在敏感性,它们的潜在应用包括监测发育、衰老和疾病过程中的结构变化。

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