Three‐dimensional characterization of non‐gaussian water diffusion in humans using diffusion kurtosis imaging

Abstract

Conventional diffusion tensor imaging (DTI) measures water diffusion parameters based on the assumption that the spin displacement distribution is a Gaussian function. However, water movement in biological tissue is often non-Gaussian and this non-Gaussian behavior may contain useful information related to tissue structure and pathophysiology. Here we propose an approach to directly measure the non-Gaussian property of water diffusion, characterized by a four-dimensional matrix referred to as the diffusion kurtosis tensor. This approach does not require the complete measurement of the displacement distribution function and, therefore, is more time efficient compared with the q-space imaging technique. A theoretical framework of the DK calculation is established, and experimental results are presented for humans obtained within a clinically feasible time of about 10 min. The resulting kurtosis maps are shown to be robust and reproducible. Directionally-averaged apparent kurtosis coefficients (AKC, a unitless parameter) are 0.74 +/- 0.03, 1.09 +/- 0.01 and 0.84 +/- 0.02 for gray matter, white matter and thalamus, respectively. The three-dimensional kurtosis angular plots show tissue-specific geometry for different brain regions and demonstrate the potential of identifying multiple fiber structures in a single voxel. Diffusion kurtosis imaging is a useful method to study non-Gaussian diffusion behavior and can provide complementary information to that of DTI.

Hybrid diffusion imaging

“…The model errors were considerably higher in WM than GM (particularly cortical GM) where the RMS residuals are close to zero. These results were consistent with the measurement of DT Kurtosis by Lu et al and Jensen et al (Jensen et al, 2005;Lu et al, 2006). A similar map was generated by taking geometric mean of the QBI images normalized by the signal at b = 0 (see Fig.…”

“…Several researchers have described methods for characterizing non-Gaussian diffusion including diffusion kurtosis (Jensen et al, 2005;Lu et al, 2006) and stretched exponentials (Bennett et al 2003(Bennett et al , 2004. In this study, non-Gaussian behavior was characterized by using the root-mean-square (RMS) of the residuals from the diffusion tensor fit.…”

Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting

“…6,7 Larger studies and the more widespread use of DKI, however, have been held back by relatively long acquisition times that result from a model requirement for data to be obtained along several directions at each of many b-values. 8 Such protocols are clearly incompatible with the daily routines in busy clinical imaging departments and are often not tolerable by critically ill patients.…”

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

“…We are currently working on both approaches in addition to a non-tensor approach based on direct analysis of the orientational kurtosis profile using spherical harmonics. 34 From the experimental results for evaluation of PRC and PDT errors, RMS error, and parameter estimation bias, we may tentatively conclude that BNS performs best among the 6 methods. As mentioned earlier, however, this study is intended to clarify the characteristics of each method; thus, combinations of the methods should be considered for building a more robust method.…”