More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging

Abstract

With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion-weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b-value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non-Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b-value-independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusionweighted rat data, which was acquired with eight different b-values, uniformly distributed in a range of [0,2800 sec/mm 2 ]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusionweighted data will result in an overestimated degree of nonGaussian diffusion and a b-value-dependent underestimation of diffusivity measures, a Rician noise model was used in this study. Diffusion tensor magnetic resonance imaging (DTI) is an important medical imaging modality in neuroscience research, because it allows the study of the complex network of myelinated axons, in vivo and noninvasively (1,2). In DTI, the diffusion of water molecules through brain structures is mathematically described by a second order 3D diffusion tensor (DT). It is generally accepted that the first eigenvector of the tensor, corresponding to the direction of maximal diffusion, is aligned with the underlying fiber structures. Furthermore, the diffusion is often quantified with diffusion parameters (i.e., fractional anisotropy (FA) and mean (MD), radial (D ⊥ ) and axial (D ) diffusivity), which provide insight in the organization, structural integrity, and development of white matter (WM) structures of the normal and pathological brain (3-9).In DTI, the diffusion of water molecules along a certain gradient direction is assumed to occur in an unrestricted environment. Consequently, the molecules' probability of diffusing from one location to another in a given time is described by a Gaussian distribution of which the standard deviation relates to the apparent diffusion coefficient (ADC). As a result, the normalized diffusion-weighted signal that is measured along a certain axis can be described by a monoexponential function; the exponent equals the ADC, weighted by the diffusion weighting strength that is given by the b-value. Several DTI studies, however, reported that the estimation of diffusion parameters depends on the b-value that is used during data acquisition. Therefore, the comparison and interpretation of various DTI studies are hampered. Jones and Basser (10) and Ande...

Diffusion-weighted imaging uncovers likely sources of processing-speed deficits in schizophrenia

“…The conventional DTI parameters are derived here using DKI and therefore, may differ from standard DTI parameters derived from a single b-value experiment. The DKI fit provides a more accurate and stable estimation of FA and is generally highly correlated with single b-value FA estimates (52). During the model fit, the multivariate regression was used to estimate the eigenvalues for the diffusion (L 1,2,3 ).…”

Feasibility and Advantages of Diffusion Weighted Imaging Atlas Construction in Q-Space

“…These shells can be sampled for any set of gradient directions, but the analytic SH representations themselves can also be used. We inspected the atlas for its quality and possibilities by performing a simple DTI fit and fODF (from SD [5]) reconstruction, as well as a full brain fiber tractography using fODF's from CSD [6], parametermaps from a DKI model fit [7,8] and a 3D signal fit and EAP reconstruction from mq-DPI [9]. The outcomes of all these reconstructions are realistic and of good quality.…”

“…This puts the angular quality of b2800 to the test. Next, we present different parameter maps resulting from a diffusion kurtosis imaging (DKI) model fit on the full atlas [7,8]. This is more of a test for the quality of the radial information, i.e.…”

“…DKI accounts for non-Gaussian diffusion (whereas DTI assumes Gaussian diffusion). This allows for an improved and bvalue-independent estimation of the classical tensor parameters and at the same time produces some new parameters that quantify the non-Gaussianity [8]. We calculated the classical fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD), as well as the kurtosis anisotropy (KA), mean kurtosis (MK), axial kurtosis (AK) and radial kurtosis (RK).…”

Fast and Robust Estimation of Diffusional Kurtosis Imaging (DKI) Parameters by General Closed-form Expressions and their Extensions

“…The kurtosis parameter is used in basically 2 ways-in isotropic analysis of the mean kurtosis value among several directions of the motion-probing gradient (MPG) and in anisotropic analysis in which several kurtosis values obtained in different MPG directions in a manner similar to that of DTI make up the kurtosis tensor. 15 Thus, DKI is expected to provide a new horizon of diagnostic imaging based on DWI by introducing new information on isotropic and anisotropic diffusion properties. Originally, the value of kurtosis is defined to characterize the form of the probability density function (PDF) of water molecules after diffusion and is therefore obtained most directly using the PDF estimated in q-space imaging (QSI), 3 which assumes no models for the water diffusion process.…”