Quantitative assessment of diffusional kurtosis anisotropy

Abstract

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared to diffusion tensor imaging (DTI), and consequently, it can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), to the diffusion-tensor derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA regarding diffusional anisotropy and may be particularly advantageous for assessing diffusion in complex tissue environments.

Epilepsy-related cytoarchitectonic abnormalities along white matter pathways

“…DKI analysis included characterisation of mean diffusivity (MD) and FA from the diffusion tensor and corresponding mean kurtosis (MK) and kurtosis fractional anisotropy (KFA) 30. DKI-derived tractography16 31 was performed using diffusional kurtosis estimator software (https://www.nitrc.org/projects/dke/).…”

“…Accordingly, DKI has demonstrated improved sensitivity for detecting neuropathology in a variety of conditions including epilepsy,19–22 stroke,23–26 Alzheimer's disease27–29 and numerous others. More recently, the advantages of DKI have been leveraged to provide more comprehensive assessment of diffusion in complex neural environments, including the characterisation of diffusion anisotropy beyond the conventional fractional anisotropy (FA)30 and computation of DKI-based WM tractography, enabling the resolution of multiple intravoxel fibre bundles 16 31. These advantages are improved by using DKI in conjunction with automated fibre quantification (AFQ),32 for characterisation of tissue microstructure along WM pathways, by incorporating a more comprehensive and potentially more sensitive collection of parameters for detecting disease-related pathology than does DTI.…”

Recent Developments in Fast Kurtosis Imaging

“…Overall, the parameters can be seen to provide vastly different contrast, except for K and W which are seen to be almost indistinguishable although a few blank pixels are seen in the W map due to noise (see also comments on preprocessing below). The level of agreement between K and W presented in Figure 4 has been quantified (linear correlations exceed 0.9 in most cases but will depend on the data foundation) and demonstrated in both fixed and live brain in a number of studies [59][60][61] and also in fixed kidney [62]. In the diseased brain, the 1-3-9 method has been validated in an animal model of stroke [63,64] and was found to yield diffusion and kurtosis lesions in good agreement with conventional DKI.…”

“…Similarly, studies have been performed to optimize diffusion sampling schemes for DKI [3] and to assess the DKI metric reproducibility across field strengths [94]. Field strength dependence was found to be most pronounced for KFA which is known to be more SNR dependent than the remaining DKI metrics [37,61]. In validation studies, high resolution is often desirable in order to identify specific regions or sub-regions, since varying response is sometimes seen in sub-regions as for example in the hippocampus in relation to stress [60,95].…”

“…KFA can be estimated in any region but is likely to be of most interest in anisotropic tissues and in regions where more than one dominant fiber orientation exists. KFA was investigated using simulations and human brain data in Hansen and Jespersen [37] and Glenn et al [61] and found to provide contrast in regions where complex WM fiber composition causes the FA to vanish, manifesting as dark bands in the FA maps in Figure 8. This is because D cannot resolve more than one primary direction and is related to the well-known crossing fiber problem in DTI.…”