Degree Of Diffusion Anisotropy Research Articles

Transmural gradients of preferential diffusion motility in the normal rat myocardium characterized by diffusion tensor imaging

Background In recent years, diffusion tensor imaging (DTI) has emerged as a powerful noninvasive tool that infers the cardiac tissue organization through measuring water molecule motility.[1] Previous DTI studies [2] on normal hearts largely assumed that the measured diffusion magnitude along the 3 preferred directions and the measured degree of diffusion anisotropy remain the same over the endo-, meso-, and epi-cardial layers of the left ventricular (LV) wall. Our goal is to test the validity of this assumption.

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

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.

Brain dysmyelination and recovery assessment by noninvasive in vivo diffusion tensor magnetic resonance imaging

Diffusion tensor magnetic resonance imaging (DT-MRI) was applied for in vivo quantification of myelin loss and regeneration. A transgenic mouse line (Oligo-TTK) expressing a truncated form of the herpes simplex virus 1 thymidine kinase gene (hsv1-tk) in oligodendrocytes was studied along with two induced phenotypes of myelin pathology. Myelin loss and axonal abnormalities differentially affect values of DT-MRI parameters in the brain of transgenic mice. Changes in the anisotropy of the white matter were assessed by calculating and mapping the radial (D perpendicular) and axial (D parallel) water diffusion to axonal tracts and fractional anisotropy (FA). A significant increase in D perpendicular attributed to the lack of myelin was observed in all selected brain white matter tracts in dysmyelinated mice. Lower D parallel values were consistent with the histological observation of axonal modifications, including reduced axonal caliber and overexpression of neurofilaments and III beta-tubulin. We show clearly that myelination and axonal changes play a role in the degree of diffusion anisotropy, because FA was significantly decreased in dysmyelinated brain. Importantly, myelin reparation during brain postnatal development induced a decrease in the magnitude of D( perpendicular) and an increase in FA compared with the same brain before recovery. The progressive increase in D parallel values was attributed to the gain in normal axonal morphology. This regeneration was confirmed by the detection of enlarged oligodendrocyte population, newly formed myelin sheaths around additional axons, and a gradual increase in axonal caliber.

A Model for Diffusion in White Matter in the Brain

Diffusion of molecules in brain and other tissues is important in a wide range of biological processes and measurements ranging from the delivery of drugs to diffusion-weighted magnetic resonance imaging. Diffusion tensor imaging is a powerful noninvasive method to characterize neuronal tissue in the human brain in vivo. As a first step toward understanding the relationship between the measured macroscopic apparent diffusion tensor and underlying microscopic compartmental geometry and physical properties, we treat a white matter fascicle as an array of identical thick-walled cylindrical tubes arranged periodically in a regular lattice and immersed in an outer medium. Both square and hexagonal arrays are considered. The diffusing molecules may have different diffusion coefficients and concentrations (or densities) in different domains, namely within the tubes’ inner core, membrane, myelin sheath, and within the outer medium. Analytical results are used to explore the effects of a large range of microstructural and compositional parameters on the apparent diffusion tensor and the degree of diffusion anisotropy, allowing the characterization of diffusion in normal physiological conditions as well as changes occurring in development, disease, and aging. Implications for diffusion tensor imaging and for the possible in situ estimation of microstructural parameters from diffusion-weighted MR data are discussed in the context of this modeling framework.

Contrast‐to‐noise ratios of diffusion anisotropy indices

The degree of diffusion anisotropy in different brain regions is usually measured by a diffusion anisotropy index (DAI) such as relative anisotropy (RA) and fractional anisotropy (FA). FA has been reported to have a higher contrast-to-noise ratio (CNR) than RA. The present work compares the CNRs of seven DAIs in theoretical propagation-of-error calculations, in simulations, and in human brain measurements over small and large anisotropy differences. In simulations all seven CNRs were similar for small anisotropy differences. Small differences among the DAIs appeared at higher anisotropy levels and lower signal-to-noise ratios with certain tensor orientations. The DAIs fell into three groups based on algebraic relationships and small CNR differences. The group with RA and FA had the best CNR. Human brain regions with small anisotropy differences had similar CNR for all seven DAIs, and the scatter in the data was greater than any expected differences. With large anisotropy differences, a small advantage appeared for RA over FA in some simulations and for FA over RA in other simulations. The CNR between brain regions with very different anisotropies was different for each DAI. The apparent reported advantage of FA over RA is explained by biologic heterogeneity and by noise-induced bias in the DAI values and their standard deviations.

Microstructural and Physiological Features of Tissues Elucidated by Quantitative-Diffusion-Tensor MRI

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,Dis decomposed into its isotropic and anisotropic parts, 〈D〉IandD− 〈D〉I, respectively (where 〈D〉 = Trace(D)/3 is the mean diffusivity, andIis 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 whichDis 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.