Diffusion kurtosis imaging dki is a new model in medical engineering, where a diffusion kurtosis dk tensor is involved. Dec 09, 2014 diffusion kurtosis imaging dki is an extension of diffusion tensor imaging dti, exhibiting improved sensitivity and specificity in detecting developmental and pathological changes in neural tissues. Diffusion kurtosis imaging dki is an advanced magnetic resonance imaging modality. Our purpose was to use diffusional kurtosis imaging to measure agerelated microstructural changes in both the wm and gm of the developing human brain. We aimed to find suitable b value, mpg direction, and diffusion time settings for. Optimization of scan parameters to reduce acquisition time. Diffusion kurtosis imaging with free water elimination.
Characterization of breast tumors using diffusion kurtosis. You can set flag to 0 to correct for this systematic bias. Diffusion kurtosis imaging quantifies the nongaussianity of water diffusion. However, only a few dki studies with respect to rectal cancer in preoperative staging are yet available, such as prostate cancers, breast cancers, and gliomas 4 6.
Application of diffusion kurtosis imaging and histogram. A simple isotropic phantom for diffusional kurtosis imaging. 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. For example, in fibrous tissue, water molecules tend to diffuse more along the. The dke ft module is run using a file with matlab syntax. Qsi and dki have shown promising results in clinical applications, such as in the evaluation of brain tumors. Outside of the zone, kurtosis points are flagged as nonstationary or nongaussian. Diffusion kurtosis imaging questions and answers in mri. Probably because your method can use all previous steps and matlab has to redo everything every function. Nongaussian water diffusion kurtosis imaging of prostate. It provides a high order diffusion of water distribution and analyzes and also quantifies the diffusion restriction.
Diffusional kurtosis imaging is an extension of dti but includes nongaussian diffusion effects, allowing more comprehensive characterization of microstructural changes during brain development. A matlab tool for montecarlo simulations of diffusion. Optimal experimental design for diffusion kurtosis imaging. It has recently been shown that dki parameters, such as the radial or axial kurtosis, are more sensitive to brain physiology changes than the wellknown diffusion tensor imaging dti parameters in several white and gray matter structures. The knowledge of that displacement pdf would provide information about the. 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. Recently, nongaussian diffusion weighted imaging dwi techniques, including qspace imaging qsi and diffusional kurtosis imaging dki, have emerged as advanced methods to evaluate tissue microstructure in vivo using water diffusion. For example, if the true values of a voxel are adc 0. Purpose diffusion kurtosis imaging dki is a recent improvement over. According to what i read from here, the kurtosis of a normal distribution should be around 3. However, when i use the kurtosis function provided by matlab, i could not verify it. For example, in fibrous tissue, water molecules tend to diffuse more along the fibers. More accurate estimation of diffusion tensor parameters using. Estimation of the orientation distribution function from.
It should be compatible with any matlab version after 2014, and it can be easily adapted to at least 2012 by uncommenting a line. Learn more about how to analyze the outputs, skew, kurtosis statistics and machine learning toolbox. All these algorithms were implemented in matlab on a. Spectral kurtosis from signal or spectrogram matlab.
Dwi, diffusion kurtosis imaging dki and diffusion tensor imaging dti, are different magnetic resonance imaging mri techniques which. A dk tensor is a fourthorder threedimensional fully symmetric tensor. Diffusion kurtosis imaging of the healthy human brain core. Whereas diffusion tensor imaging dti models the diffusion as a 3d gaussian function, dki takes it one step further by additionally quantifying the. Diffusional kurtosis imaging dki is a new technique based on nongaussian water diffusion analysis. Sep 07, 2011 kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution.
Structural connectivity based on diffusion kurtosis imaging dissertation submitted in partial ful. This manual is showcasing the very basic tools of exploredti. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging article in magnetic resonance in medicine 651. Besides the diffusion metrics produced by dti, for example, fractional.
Request permission export citation add to favorites track citation. The area between the two horizontal reddashed lines represents the zone of probable stationary and gaussian behavior, as defined by the 0. Any kurtosis points falling within this zone are likely to be stationary and gaussian. Performances of diffusion kurtosis imaging and diffusion. Structural connectivity based on diffusion kurtosis imaging. Deigenvalues of diffusion kurtosis tensors sciencedirect. 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. For example, if x is a 2by3by4 array, then kurtosisx,1,1 2 returns a 1by1by4 array. The orientation distribution function odf is used to describe the directionality of multimodal diffusion in regions with complex fiber architecture present in brain and other biological tissues. To shorten acquisition of diffusion kurtosis imaging dki in 1. Brain imaging diffusional kurtosis imaging youtube.
When x represents a sample from a population, the kurtosis of x is biased, meaning it tends to differ from the population kurtosis by a systematic amount based on the sample size. However, when i use the kurtosis function provided by matlab, i. Diffusion kurtosis imaging estimator file exchange matlab. Nov 18, 2014 aim the aim of this study was to investigate and evaluate the role of magnetic resonance mr diffusion kurtosis imaging dki in characterizing breast lesions. Feb 01, 2015 if you have the statistics toolbox, you can calculate the skewness and kurtosis directly with their respective functions. Aim the aim of this study was to investigate and evaluate the role of magnetic resonance mr diffusion kurtosis imaging dki in characterizing breast lesions. A replication script of the tutorial examples is also provided for matlab. 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. The diffusion and kurtosis tensors are now written as outputs.
Dke requires the installation of the matlab compiler runtime 2012a mcr. Diffusional kurtosis estimator dke users guide version 2. Dwi in wholebody imaging, for example, of widespread metastatic disease or. Diffusional kurtosis imaging using a fast heuristic. Optimization of white matter fiber tractography with diffusional kurtosis imaging. The video tutorial gives a brief overview of the basic steps for processing dki. Diffusion kurtosis imaging dki is based on a nongaussian diffusion. Frontiers highresolution diffusion kurtosis imaging at 3t. The sensitization of a mr image to the molecular diffusion is called diffusion weighted mri or diffusion mri, dmri. For example, if x is a 2by3by4 array, then kurtosis x,1,1 2 returns a 1by1by4 array. Hugo alexandre ferreira, auxiliary professor, faculty of sciences of university of lisbon coadviser. Diffusion kurtosis imaging provides independent and complementary.
Diffusion kurtosis imaging radiology reference article. Analogous to dti it is possible to create diffusion kurtosis tensors and, for example, estimate axial and radial components of kurtosis. If you dont have the toolbox, it would be relatively easy to code those functions. Comparative analysis of diffusional kurtosis imaging. We aimed to find suitable b value, mpg direction, and diffusion time settings for faster dki. Each element of the output array is the biased kurtosis of the elements on the corresponding page of x. Statistically speaking, the signal intensity of each voxel in a diffusion mr image depends on the displacement probability distribution function pdf of water molecules captured in the imaged volume. For example, constraints on a dki linear solution to impose plausible values of. Our purpose was to use diffusional kurtosis imaging to measure agerelated microstructural changes in both the wm and gm of the. Frontiers highresolution diffusion kurtosis imaging at. Choose a web site to get translated content where available and see local events and offers. The diffusion equation in one dimension in our context the di usion equation is a partial di erential equation describing how the concentration of a protein undergoing di usion changes over time and space.
Diffusion tensor magnetic resonance imaging dti is the currently unique invivo mri technique for probing the distribution of neural pathways and microstructures of biological tissues, thus promising for neurology and neuroanatomy research. Diffusion kurtosis imaging dki is an advanced magnetic resonance imaging modality that is known to be sensitive to. Both diffusion and kurtosis values have shown to be useful in the. This motion was corrected using linear registration to the b0 volume in matlab natick. Diffusional kurtosis imaging college of medicine musc. In this paper, we introduce deigenvalues for a dk tensor. Diffusion kurtosis imaging dki is a fairly new magnetic resonance imaging mri technique that tackles the nongaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in diffusion tensor imaging dti, where the water.
Diffusion kurtosis imaging dki was first reported by jensen et al. Fitting this model voxelwise to a set of diffusion mr images to estimate d and w, and as such, directly quantifying the directiondependent diffusion and kurtosis information, is called diffusion kurtosis imaging 15, 21, 22. These functions are a simple implementation of the algorithm presented by tabesh et al. Clinical application of qspace imaging and diffusional kurtosis imaging of the brain and spine. A simple tutorial carolina tropini biophysics program, stanford university dated. However, the complexity of normal and pathologic tissue leads to hindered diffusion of water molecules and, therefore, should follow a nongaussian distribution. That is, data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly, and have heavy tails. Diffusion kurtosis imaging dki is a new magnetic resonance imaging mri model that describes the nongaussian diffusion behavior in tissues.
The purpose of this article is to introduce and discuss the ongoing research and potential clinical applications of this technique. To evaluate the nongaussian water diffusion properties of prostate cancer pca and determine the diagnostic performance of diffusion kurtosis dk imaging for distinguishing pca from benign tissues within the peripheral zone pz, and assessing tumor lesions with different gleason scores. Materials and methods one hundred and twentyfour lesions in 103 patients mean age. 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. Effects of diffusional kurtosis imaging parameters on. White matter biomarkers from fast protocols using axially. Diffusional kurtosis estimator dke users guide contents nitrc.
Diffusion kurtosis imaging dki extends conventional diffusion tensor imaging. Currently dki is acquired only at low spatial resolution 23 mm isotropic. In this paper, an approximation for the odf of water diffusion from diffusional kurtosis imaging dki is presented. Several even more complex methods, including qball imaging and diffusion spectrum imaging, are. Diffusion kurtosis imaging dki is an attempt to account for this variation and in a more refined approach overcomes this problem by quantifying the deviation from the gaussian distribution of. The sensitivity of the diffusion kurtosis model to the microstructural heterogeneity of biological tissues can be studied using montecarlo simulations, which being computationally expensive, can be better applied using parallel computing. Extensive experiments conducted on synthetic data verify a matlab. Mar 28, 20 diffusional kurtosis imaging dki is a new technique based on nongaussian water diffusion analysis. Diffusion kurtosis imaging dki is an extension of diffusion tensor imaging dti, exhibiting improved sensitivity and specificity in detecting developmental and pathological changes in neural tissues.
We obtained 2 image datasets with 30 gradient directions, 6 bvalues up to 2500 smm 2, and 2. The toolbox is developed in matlab as a stand alone application and main. Currently dki is acquired only at low spatial resolution 23 mm. These parameter values are similar to those observed in vivo for human brain. Texture analysis and support vector machineassisted. Diffusion kurtosis imaging is an emerging technique based on the nongaussian diffusion of water in biologic systems. If the pdf 5 is viewed as a function of the unknown parameter vector. Diffusion kurtosis imaging estimator makers of matlab.
Diffusion kurtosis imaging detects microstructural changes in. It has recently been shown that dki parameters, such as the radial or axial kurtosis, are more sensitive to brain physiology changes than the wellknown diffusion tensor imaging dti parameters in several white and gray matter. Diffusion kurtosis imaging dki is a new model in magnetic resonance. However, little attention was paid to the performances. However, the 3dimensional dti tractography dtt suffers from the overwhelmingly dense resulting fiber tracts in its. However, little attention was paid to the performances of dki and dti in detecting white matter abnormality in schizophrenia. Each element of the output array is the biased kurtosis of. A replication script of the tutorial examples is also provided for matlab users. This module uses the diffusion and kurtosis tensors from dke. Diffusion kurtosis imaging detects microstructural changes. The mrvisible protons of cream exhibit slow and fast diffusion components, attributed to the fat and water protons, respectively, which give rise to a diffusion coefficient of 1. Diffusion kurtosis imaging dki is more sensitive to microstructural differences and can be related to more specific microscale metrics e. Keep in mind that md and fa extracted from this diffusion tensor d will be different from those extracted from dti for example if you are using.
Dec 21, 2017 these functions are a simple implementation of the algorithm presented by tabesh et al. Diffusion kurtosis imaging dki is a recent imaging method that probes the diffusion of water molecules. Pdf diffusion kurtosis imaging with free water elimination. For the cnls fit, the lsqnonlin matlab function 51 was used with the.
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