Diffusional kurtosis imaging (DKI) extends conventional diffusion tensor imaging (DTI) to the calculation of the diffusional kurtosis and related kurtosis metrics 1 to 4. For a certain diffusion direction vector n and diffusion weighting b, the DKI approximation to diffusion signal intensity S (n,b) is given by: with,

Mar 7, 2025 · Here, we implement the DP-DKI analysis for Moss et al. Linear Rotationally Invariant Kurtosis Measures from Double Diffusion Encoding MRI, MRI (2025). This work aims to characterize the complete set of linear rotationally invariant kurtosis measures provided by double diffusion encoding MRI.

Dec 26, 2013 · Diffusion kurtosis imaging (DKI) is an attempt to account for this variation to provide a more accurate model of diffusion and to capture the nongaussian diffusion behavior as a reflective marker for tissue heterogeneity [1].

Feb 15, 2019 · Diffusional Kurtosis Estimator (DKE) is a software tool for post-processing diffusional kurtosis imaging (DKI) datasets that includes a suite of command-line programs along with a graphical user interface (GUI). DKE supports 32-, 64-bit Windows and linux.

The Diffusional Kurtosis Estimator (DKE) is software for processing Diffusional Kurtosis Imaging (DKI) data.

Diffusional Kurtosis Estimator (DKE) is a software tool for post-processing diffusional kurtosis imaging (DKI) datasets that includes a suite of command-line programs along with a graphical user interface (GUI). DKE currently supports 32- and 64-bit Windows platforms.

Dec 8, 2023 · Diffusion kurtosis imaging (DKI) is an advanced neuroimaging modality which is an extension of diffusion tensor imaging by estimating the kurtosis (skewed distribution) of water diffusion based on a probability distribution function. It provides a high order diffusion of water distribution and analyzes and also quantifies the diffusion restriction.

The toolbox includes (i) a constrained weighted linear least squares algorithm for diffusion kurtosis imaging (DKI) fittings and (ii) introduces a novel DKI estimator for the more robust and reproducible quantification of kurtosis parameters.

Dec 21, 2017 · These functions are a simple implementation of the algorithm presented by Tabesh et al. (2011) to estimate Diffusion Kurtosis Imaging tensors and parameters. It should be compatible with any MATLAB version after 2014, and it can be easily adapted to at least 2012 by uncommenting a line.

Aims of this study were to investigate white matter (WM) and thalamus microstructure 72 hr and 3 months after mild traumatic brain injury (TBI) with diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI), and to relate DKI and DTI findings to postconcussional syndrome (PCS).