The present study aimed to investigate the corticospinal tract cst changes in patients with brainstem ischemic stroke by using the diffusion kurtosis imaging dki approach materials and. Kurtosis quantifies the deviation of tissue diffusion from a gaussian pattern. Our results show that dki had significantly greater diagnostic properties than dti in grading meningiomas. Diffusion kurtosis imaging for diagnosis of parkinsons. Unbiased estimators of skewness and kurtosis cross validated. The kurtosis is a general, dimensionless statistic for quantifying the nongaussianity of any distribution 5.
The main goal of the present study was to propose to discriminate adrelated changes or their consequences and the effects of aging with a fast dki method estimating only angularlyaveraged. Diffusion weighted dw imaging is a key component of multiparametric magnetic resonance mr imaging in patients with prostate cancer that contributes to tumor detection, as well as to the assessment of tumor aggressiveness and associated extraprostatic extension 1. Diffusional kurtosis estimator dke is a software tool for postprocessing diffusional kurtosis imaging dki datasets that includes a suite of commandline programs along with a graphical user interface gui. More accurate estimation of diffusion tensor parameters using. Estimation of tensors and tensorderived measures in. Parametric mapping of brain tissues from diffusion. Parametric maps for fractional anisotropy fa, mean diffusivity md, and mean kurtosis mk were subsequently obtained. Robust estimation from dwmri using homogeneous polynomials. Our purpose was to use diffusional kurtosis imaging to measure agerelated microstructural changes in both the wm and gm of the developing human brain. Jan 24, 2019 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. Dki takes into account leading deviations from gaussian diffusion stemming from a number of effects related to the microarchitecture and compartmentalization in biological tissues. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging.
In probability theory and statistics, kurtosis from greek. Dipy is a free and open source software project for computational neuroanatomy, focusing mainly on diffusion magnetic resonance imaging dmri analysis. Diffusional kurtosis imaging using a fast heuristic. Highdimensional image data including diffusion weighted imaging. More accurate estimation of diffusion tensor parameters. Jensen and his colleagues recently proposed a new and efficient model, called diffusion kurtosis imaging dki. Diffusional kurtosis imaging dki is an extension of the diffusion tensor imaging dti method. Diffusion kurtosis imaging is an emerging technique based on the nongaussian diffusion of water in biologic systems. We have developed a new graphical toolkit named dtidki fitting which is an interactive software.
Using the new dki software enables to estimate the diffusional kurtosis and diffusion coefficients, which reflect the structural differences. Diffusional kurtosis imaging dki is a clinically feasible extension of dti which enables the characterization of nongaussian diffusion by estimating the kurtosis of the displacement distribution 58, in addition to the estimation of the standard dtiderived parameters. The excess diffusional kurtosis also known as the apparent excess kurtosis coefficient akc is a dimensionless metric that quantifies the degree of deviation from gaussian diffusion behavior. This script shows how to compute the diffusion kurtosis dki coefficients from a given diffusionweighted mri dataset. The purpose of this article is to introduce and discuss the ongoing research and potential clinical applications of this technique. Diffusional kurtosis imaging dki is a new technique based on nongaussian water diffusion analysis. Diffusional kurtosis imaging of kidneys in patients with. Diffusional kurtosis estimator dke users guide version 2. Diffusion kurtosis imaging dki extends conventional diffusion tensor imaging dti by estimating the kurtosis of the water diffusion probability distribution function. Diffusional kurtosis estimator dke users guide contents nitrc. Precision and accuracy of diffusion kurtosis estimation and the. Provided you have data acquired in at least 15 directions, the dki script should handle any combination of bvalues and directions and return diffusion and kurtosis tensors along with the. Software for axisymmetric dki analysis is freely available on. Estimation of diffusion and kurtosis model parameters, including the white matter tract integrity metrics, using diffusion kurtosis imaging.
Diffusion kurtosis imaging dki can characterize the degree of nongaussian diffusion by estimating the kurtosis of the displacement probability distribution 12. The application of diffusion kurtosis metrics in meningiomas is limited. Diffusional kurtosis imaging college of medicine musc. Second, the kurtosis tensor, which has 15 independent variables, was calculated by the leastsquares method from these results and the parameters of the applied encoding directions. When x represents a sample from a population, the kurtosis of x is biased, meaning it tends to differ from the. Mar 28, 20 diffusional kurtosis imaging dki is a new technique based on nongaussian water diffusion analysis. We first characterize the positive definiteness of dki through the positive definiteness of a tensor constructed by diffusion tensor and diffusion kurtosis tensor. First, that is not at all what pearsons kurtosis measures. The diffusional kurtosis estimator dke is software for processing diffusional kurtosis imaging dki data mamadke. Constrained maximum likelihood estimation of the diffusion kurtosis tensor using a rician noise model article in magnetic resonance in medicine 663. However, factually a variety of tissue structures in the brain restrict free water diffusion, which results in the deviation of diffusion from the gaussian. Diffusion kurtosis imaging data were acquired to derive axial ka, radial kr, and mean kurtosis mk, fractional anisotropy, axial da, radial dr, and mean diffusivity md for comparisons among the. Imageengine is free and extensible software for highdimensional medical image computing. We have developed a new graphical toolkit named dtidki fitting which is an interactive software for processing diffusion mr images is presented for the first time.
Microstructural abnormalities in gray matter of patients. Diffusionweighted magnetic resonance imaging dwi or dwmri is the use of specific mri sequences as well as software that generates images from the resulting data that uses the diffusion of water molecules to generate contrast in mr images. 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. To improve the estimation of diffusion parameters from the dkiderived diffusion tensor d a constant term has been added. This software application describes a method, dki which has previously been presented in an abbreviated form, can be referred to jensen jh, helpern ja for estimating the excess kurtosis of water diffusion in vivo by means of pulsed field gradient mri. The script implements several fitting methods with various constraints from the article by a. However, the original dki protocol six b values and 30 motionprobing gradient mpg directions requires more than 10 min of scanning time, which is too long for daily clinical use.
Diffusion kurtosis imaging radiology reference article. However, the kurtosis value could be overestimated significantly in certain cases, and investigators have developed a method that uses a framework for estimation of constrained maximum likelihood cml with a riciannoise model to prevent this from happening. Jensen et al 5 proposed a nongaussian diffusionweighted model called diffusion kurtosis imaging dki in 2005. Diffusion kurtosis imaging dki can be used to estimate excess kurtosis, which is a dimensionless measure for the deviation of water diffusion profile from gaussian distribution. A fast schema for parameter estimation in diffusion. A positive kurtosis means the distribution is more strongly. Provided you have data acquired in at least 15 directions, the dki script should handle any combination of bvalues and directions and return diffusion and kurtosis tensors along with the resnorm evaluating the fits quality. Positive definiteness of diffusion kurtosis imaging. A pilot study evaluating the potential of diffusional. A positive kurtosis means the distribution is more strongly peaked and has heavier tails than a gaussian distribution with the same variance. Evaluation of diffusion kurtosis imaging versus standard diffusion imaging for detection and grading of peripheral zone prostate cancer.
February 2015 diffusional kurtosis estimator dke is a software tool for postprocessing diffusional kurtosis imaging dki. Usage or distribution of the software for commercial purpose is. The closeness of the estimator to the estimand can be measured using expected squared deviation from estimator to estimand, which is equal to variance of the estimator plus squared bias of the estimator. Diffusional kurtosis estimator dke is a software tool for postprocessing diffusional kurtosis imaging dki datasets that includes a suite of commandline programs along with a graphical user interface. Diffusion kurtosis imaging estimator file exchange. Deriche, riemannian framework for estimating symmetric positive definite 4th order diffusion tensors, in, 2008. Diffusion kurtosis imaging dki is an mr imaging technique estimating excess kurtosis and which provides nongaussian water diffusion in the tissue.
Comparative analysis of diffusional kurtosis imaging. This software application describes a method, dki which has previously been presented in an abbreviated form, can be referred to jensen jh, helpern ja for estimating the excess kurtosis of. Optimization of scan parameters for diffusion kurtosis. As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white. Recent developments in fast kurtosis imaging physics frontiers.
This reflects the heterogeneous diffusion environments experienced by water molecules as they encounter barriers, move between compartments, and undergo chemical exchange. A fast schema for parameter estimation in diffusion kurtosis imaging. Therefore, diffusion kurtosis imaging dki was introduced. Diffusional kurtosis imaging is an extension of dti but includes nongaussian diffusion effects, allowing more comprehensive characterization of microstructural changes during brain development. For a sample of n values the sample excess kurtosis is. February 2015 diffusional kurtosis estimator dke is a software tool for postprocessing diffusional kurtosis imaging dki datasets that includes a suite of commandline programs along with a graphical user interface gui. The script implements several fitting methods with various. This model included calculation of kurtosis and diffusion coefficients. Based on the estimation of nongaussian diffusion, dki parameters can be calculated. Dki takes into account leading deviations from gaussian diffusion stemming from a number of effects. Frontiers recent developments in fast kurtosis imaging. Diffusionweighted images were processed using the diffusional kurtosis estimator dke software.
Diffusion kurtosis imaging combined with dwi at 3t mri. Diffusion kurtosis imaging dki is a new magnetic resonance imaging mri model to characterize the nongaussian diffusion behavior in tissues. Robust estimation from dwmri using homogeneous polynomials, in the proceedings of isbi, 2011, pp. E, q ball estimation of odf minimummaximum scaled for b value of 4000. Corticospinal tract changes in acute brainstem ischemic. Dki has been reported to provide additional information that cannot be obtained by conventional diffusion tensor imaging dti that assumes gaussian diffusion 35.
This kurtosis coefficient was then incorporated into a tensor model. Diffusional kurtosis imaging dki is a clinically feasible extension of dti which enables the characterization of nongaussian diffusion by estimating the kurtosis of the displacement. Whereas diffusion tensor imaging dti models the diffusion as a 3d gaussian function, dki takes it one step further by additionally quantifying the degree of nongaussian diffusion. Estimation of diffusion and kurtosis model parameters, including the white.
Diffusional kurtosis imaging dki is a clinically feasible extension of dti which enables the characterization of nongaussian diffusion by estimating the kurtosis of the displacement distribution. Diffusional kurtosis estimator dke is a software tool for postprocessing. Diffusion kurtosis imaging estimator file exchange matlab. 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 kurtosis imaging dki is a new diffusion magnetic resonance imaging mri technique to go beyond the shortages of conventional diffusion tensor imaging dti from the assumption that water. With the increased usage of dti and dki in the recent years, the need for software packages for processing magnetic resonancemr diffusion data has also gained much importance. Second, if you want a measure of peakedness, you first have to define what that means. Dec 21, 2017 provided you have data acquired in at least 15 directions, the dki script should handle any combination of bvalues and directions and return diffusion and kurtosis tensors along with the resnorm evaluating the fits quality. Diffusion kurtosis tensor estimation file exchange matlab. Diffusion kurtosis imaging dki extends conventional diffusion tensor imaging dti by estimating the kurtosis of the water diffusion probability distribution function 1 to 4. Dki therefore offers increased sensitivity to subtle microstructural alterations over conventional diffusion.
This sequence has showed promise for evaluating brain, head and neck and spinal disorders in vivo 4, 5 because it is possible to. However, the original dki protocol six b values and 30 motionprobing gradient. Diffusional kurtosis imaging is an extension of dti but includes nongaussian diffusion effects, allowing more comprehensive characterization of. Empirical comparison of diffusion kurtosis imaging and. Diffusion kurtosis imaging is an emerging technique based on the non. Diffusionweighted dw imaging is a key component of multiparametric magnetic resonance mr imaging in patients with prostate cancer that contributes to tumor detection, as well. Diffusion in pure fluids is gaussian, but biological tissues are characterized by a positive diffusion kurtosis. Diffusion kurtosis imaging dki is a recent imaging method that probes the diffusion of water molecules. Diffusion kurtosis tensor estimation file exchange.
Comparing to other free software, imageengine is equipped with a graphic user interface which is. Diffusion kurtosis imaging for diagnosis of parkinsons disease. Estimating diffusion propagator and its moments using directional radial basis functions. Diffusiontensorbased method for robust and practical. May 04, 2015 jensen et al 5 proposed a nongaussian diffusion weighted model called diffusion kurtosis imaging dki in 2005. Estimating diffusion propagator and its moments using. Diffusion kurtosis imaging dki is an extension of the popular diffusion tensor imaging dti technique. Histogram analysis of diffusion kurtosis imaging estimates. The proper apparent diffusional kurtosis k and corrected diffusion coefficient d of each encoding direction were obtained by this step. Diffusion kurtosis imaging questions and answers in mri. Diffusion kurtosis imaging in mild traumatic brain injury.
Estimating a positive diffusion function has also been. Whereas diffusion tensor imaging dti models the diffusion as a 3d gaussian function. Diffusion kurtosis imaging combined with dwi at 3t mri for. Diffusion kurtosis imaging dki is an extension of diffusion tensor imaging that. Parametric mapping of brain tissues from diffusion kurtosis.
It implements a broad range of algorithms for denoising, registration, reconstruction, tracking, clustering, visualization, and statistical analysis of mri data. A fast schema for parameter estimation in diffusion kurtosis. The purpose of this article is to introduce and discuss the. We here provide the code to estimate the diffusion kurtosis tensors from diffusion weighted images. Kurtosis is used to measure the deviation of water diffusion from gaussian model, which is called nongaussian, in dki. Constrained maximum likelihood estimation of the diffusion kurtosis tensor using a. The closeness of the estimator to the estimand can be measured using expected squared deviation from estimator to. The choice of bvalues with minimum mkt estimation error and. Effects of diffusional kurtosis imaging parameters on. Diffusionweighted magnetic resonance imaging dwi or dwmri is the use of specific mri sequences as well as software that generates images from the resulting data that uses the diffusion of water.
In addition, by estimating gaussian and nongaussian diffusion, more accurate dti parameters can be obtained. 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. D is the diffusion tensor 45, and the kurtosis tensor w and the. Diffusiontensorbased method for robust and practical estimation of axial and radial diffusional kurtosis. Using the new dki software enables to estimate the diffusional kurtosis and diffusion coefficients, which reflect the structural differences between. Diffusion kurtosis imaging dki is a new diffusion magnetic resonance imaging mri technique to go beyond the shortages of conventional diffusion tensor imaging dti from the assumption that water diffuse in biological tissue is gaussian. Diffusional kurtosis imaging of the developing brain. Diffusion kurtosis imaging in mild traumatic brain injury and. Constrained maximum likelihood estimation of the diffusion. In this paper, we analyze the positive definiteness of dki. Jun 17, 2011 this script shows how to compute the diffusion kurtosis dki coefficients from a given diffusion weighted mri dataset.