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diffusional_kurtosis_imaging

Diffusional kurtosis imaging

Since tissue structure is responsible for the deviation of water diffusion from the Gaussian behavior typically observed in homogeneous solutions, this method provides a specific measure of tissue structure, such as cellular compartments and membranes. The method is an extension of conventional diffusion weighted imaging that requires the use of somewhat higher b values and a modified image postprocessing procedure. In addition to the diffusion coefficient, the method provides an estimate for the excess kurtosis of the diffusion displacement probability distribution, which is a dimensionless metric of the departure from a Gaussian form. From the study of six healthy adult subjects, the excess diffusional kurtosis is found to be significantly higher in white matter than in gray matter, reflecting the structural differences between these two types of cerebral tissues. Diffusional kurtosis imaging is related to q-space imaging methods, but is less demanding in terms of imaging time, hardware requirements, and postprocessing effort. It may be useful for assessing tissue structure abnormalities associated with a variety of neuropathology's 1).

DK imaging is able to depict microstructural changes within glioma tissue and is able to help differentiate among glioma grades

DKI parameters can effectively distinguish between low- and high-grade gliomas 2).

Mean MK is the best independent predictor of differentiating glioma grades 3).

Diffusional kurtosis is a sensitive and complementary measure of microstructural compromise in patients with temporal lobe epilepsy. It provides additional information regarding the anatomic distribution and degree of damage in this condition. Diffusional kurtosis imaging may be used as a biomarker for disease severity, clinical phenotypes, and treatment monitoring in epilepsy 4).

There were significant differences in kurtosis parameters between high grade glioma and primary central nervous system lymphoma, while differences in diffusion parameters between them did not reach significance; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters 5).

1)
Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005 Jun;53(6):1432-40. PubMed PMID: 15906300.
2)
Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology. 2010 Mar;254(3):876-81. doi: 10.1148/radiol.09090819. Epub 2010 Jan 20. PubMed PMID: 20089718.
3)
Qi XX, Shi DF, Ren SX, Zhang SY, Li L, Li QC, Guan LM. Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery. Eur Radiol. 2017 Nov 16. doi: 10.1007/s00330-017-5108-1. [Epub ahead of print] PubMed PMID: 29143940.
4)
Bonilha L, Lee CY, Jensen JH, Tabesh A, Spampinato MV, Edwards JC, Breedlove J, Helpern JA. Altered microstructure in temporal lobe epilepsy: a diffusional kurtosis imaging study. AJNR Am J Neuroradiol. 2015 Apr;36(4):719-24. doi: 10.3174/ajnr.A4185. Epub 2014 Dec 11. PubMed PMID: 25500311.
5)
Pang H, Ren Y, Dang X, Feng X, Yao Z, Wu J, Yao C, DI N, Ghinda DC, Zhang Y. Diffusional kurtosis imaging for differentiating between high-grade glioma and primary central nervous system lymphoma. J Magn Reson Imaging. 2015 Nov 20. doi: 10.1002/jmri.25090. [Epub ahead of print] PubMed PMID: 26588793.
diffusional_kurtosis_imaging.txt · Last modified: 2017/11/17 11:00 by administrador