Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging

By Jens H. Jensen, Joseph A. Helpern, A. Ramani, Hanzhang Lu, K. Kaczynski. Magnetic Resonance in Medicine, 2005

2,431 citations recorded by the source corpus.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/62f5553e3db964055b36ece9f3ed319d7e038191

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Generalized <formula formulatype="inline"><tex Notation="TeX">${ q}$</tex></formula>-Sampling Imaging (2010, background): “composite hindered and restricted model [16], [17], diffusion kurtosis model [18]–[20], and spherical harmonic deconvolution [21]–[24].”
Neurite density from magnetic resonance diffusion measurements at ultrahigh field: Comparison with light microscopy and electron microscopy (2009, positive): “We examine, in particular, the directionally averaged W hW θ; u ð Þi = 1 5 W zzzz + W xxxx + W yyyy + 3W xxyy + 3W xxzz + 3W yyzz ð1:7Þ as well as mean kurtosis (Jensen et al., 2005; Lu et al., 2006).”
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, neutral): “(Jensen et al., 2005), and neurite orientation dispersion and density imaging (Zhang et al.”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, positive): “kurtosis imaging (DKI) has been proposed (Jensen and Helpern, 2010; Jensen et al., 2005; Lu et al., 2006).”
The Development of Brain White Matter Microstructure (2018, background): “Diffusion kurtosis imaging (DKI) is a model-free fitting that measures deviations from the Gaussian signal caused by restricted diffusion (Jensen et al., 2005).”
Diffusion Tensor Imaging and Beyond (2011, neutral): “More recently, diffusion kurtosis imaging has been proposed as a way of characterizing non-gaussian behavior in DWI (137).”
Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice (2015, methodology): “This more advanced technique has greater hardware, acquisition time, and postprocessing requirements and is beyond the scope of this article.(7,26) Compared with the brain, tissues in the body generally exhibit faster signal decays at increasing b-values, as well as faster T2 decays.”
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans (2015, methodology): “1, we show the radial kurtosis [21] measure based on DKI because of its particular susceptibility to noise and because no analytical solutions are known.”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, background): “In a recently published study looking at DKI in a rat model of traumatic brain injury (Zhuo et al., 2012), the MK significantly 426 ALEXANDER ET AL. increased at the sub-acute stages of injury in all ipsilateral and contralateral regions, while standard DTI parameters gave inconsistent results.”
White matter characterization with diffusional kurtosis imaging (2011, methodology): “7) follows from the properties of the kurtosis tensor as described by (Jensen et al., 2005; Lu et al., 2006) but is omitted due to length considerations.”
Imaging brain microstructure with diffusion MRI: practicality and applications (2019, methodology): “This is in contrast to signal models, such as DTI, diffusion kurtosis imaging (DKI) [125], q-space imaging [80,”
Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review (2007, background): “Compartmentalization and restricted diffusion certainly lead to non-Gaussian diffusion (Assaf and Cohen 2000; Beaulieu 2002; Assaf and Basser 2005; Alexander et al. 2002; Jensen et al. 2005).”
Model-based analysis of multi-shell diffusion MR data for tractography: How to get over fitting problems (2012, background): “sis imaging (10), and statistical models (continuous dis-”
Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls (2013, background): “Typical examples of such diffusion models are diffusion tensor imaging (DTI; Basser et al., 1994a) and diffusion kurtosis imaging (DKI; Jensen et al., 2005; Veraart et al., 2011a).”
Assessing the effects of age on long white matter tracts using diffusion tensor tractography (2009, background): “…fiber tracts that have been shown to be highly representative of the underlying white matter anatomy (Burgel et al., 2006; Wakana et al., 2007), newer sampling methods which are less susceptible to partial volume effects (Jensen et al., 2005) may improve the inferences made on DTI imaging data.”
MRI quantification of non‐Gaussian water diffusion by kurtosis analysis (2010, methodology): “Early investigations of DKI utilized 6 b-values ranging from 0 to 2500 s/mm(2) in increments of 500 s/mm(2) (4,5).”
What can we see with IVIM MRI? (2017, neutral): “Several models have been suggested to empirically handle this non-Gaussian behavior, such as the polynomial or Kurtosis model (Chabert et al., 2004) (also called Diffusion Kurtosis Imaging, DKI (Jensen et al., 2005)), the biexponential model (Mulkern et al., 2009), the statistical model (Yablonskiy ”
Q-space trajectory imaging for multidimensional diffusion MRI of the human brain (2016, neutral): “Hence, theywould be indistinguishable with conventional SDE-based dMRI sequences such as DTI, HARDI, DSI, and DKI.”