Diffusion kurtosis imaging (DKI) extends conventional diffusion tensor imaging (DTI) by estimating the kurtosis of the water diffusion probability distribution function1 to 4. The kurtosis is a general, dimensionless statistic for quantifying the non-Gaussianity of any distribution5. A positive kurtosis means the distribution is more strongly peaked and has heavier tails than a Gaussian distribution with the same variance. Water diffusion in biological tissues is non-Gaussian due to the effects of cellular microstructure (e.g., cell membranes and organelles). This is particularly evident in brain where water diffusion is strongly restricted by myelinated axons. Qualitatively, a large diffusional kurtosis suggests a high degree of diffusional heterogeneity and microstructural complexity.
Because diffusion in brain is anisotropic, DKI requires the introduction of a diffusional kurtosis tensors (which are calculated together from a single diffusion-weighted image data set), several rotationally invariant metrics can be computed. These include standard DTI-metrics, such as the mean diffusivity and fractional anisotropy, as well as metrics reflecting the diffusional kurtosis, such as the mean, axial, and radial kurtoses. The diffusional kurtosis metrics are strongly linked to cellular microstructure, as this is the main source of diffusional non-Gaussianity in tissues. The extra information provided by DKI can also resolve intra-voxel fiber crossings and thus be used to improve fiber tractography of white matter6 to 7.
An advantage of DKI is that it is relatively simple to implement for human imaging on conventional MRI clinical scanners. DKI protocols differ from DTI-protocols in requiring at least 3 b-values (as compared to 2 b-values for DTI) and at least 30 independent diffusion gradient directions (as compared to 6 for DTI). Typical protocols for brain have b-values of 0, 1000, 2000 s/mm2 with diffusion directions. Image post-processing requires the use of specialized alogorithms4 to 8.
DKI has been most commonly used for the study of the human brain9 to 52, although applications to other body parts such as lung53, prostate cancer55 to 58, breast cancer59, calf muscle60 and the liver61 have been investigated. There are also several technical studies involving DKI62 to 71. Animal models have been studied as well using diffusional kurtosis imaging10,11,22,33,43,72,73.
The Center for Biomedical Imaging would like to acknoledge Ali Tabesh, Ph.D. and Emilie McKinnon for their hard work to help create the DKE software.
Get Started With DKI Imaging
- Gather your DKI data. More information about DKI Protocols.
- Process your DKI data. More information about DKI Data Processing
- Analyze your results.
Changes to DKE
DKE 2.6 (February 25, 2015)
- The addition of two new parametric maps: KFA and MKT.
- The diffusion and kurtosis tensors are now written as outputs.
- Bruker format support.
- Small change in median filtering.
- Minor bug fixes.
- Linux version.
DKE 2.5.1 (December 27, 2012)
- The length of user-defined gradient sets was not recognized properly.
DKE 220.127.116.11 (September 24, 2012)
- Vista read-only issue fixed.
- DCMDICTPATH not set properly in x86 package.
DKE 2.5.0 (September 17, 2012)
- Initial release.
- Only MATLAB Compiler Runtime required.
- GUI added.
The DKE Fiber Tractography (FT) Module performs white matter FT using the DKI approximation of the diffusion orientation distribution function (dODF). This module uses the diffusion and kurtosis tensors from DKE and should be run with DKE version 2.6 or later. DKE requires the installation of the MATLAB Compiler Runtime 2012a (MCR)
Gradient Vectors: Use Siemens 30-direction
B-values: [0 1000 2000]