Literature DB >> 25977481

Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting.

A Tietze1, M B Hansen2, L Østergaard3, S N Jespersen4, R Sangill2, T E Lund2, M Geneser3, M Hjelm5, B Hansen2.   

Abstract

BACKGROUND AND
PURPOSE: Diffusional kurtosis imaging is an MR imaging technique that provides microstructural information in biologic systems. Its application in clinical studies, however, is hampered by long acquisition and postprocessing times. We evaluated a new and fast (2 minutes 46 seconds) diffusional kurtosis imaging method with regard to glioma grading, compared it with conventional diffusional kurtosis imaging, and compared the diagnostic accuracy of fast mean kurtosis (MK') to that of the widely used mean diffusivity.
MATERIALS AND METHODS: MK' and mean diffusivity were measured in the contrast-enhancing tumor core, the perifocal hyperintensity (indicated on T2 FLAIR images), and the contralateral normal-appearing white and gray matter of 34 patients (22 with high-grade and 12 with low-grade gliomas). MK' and mean diffusivity in the different tumor grades were compared by using a Wilcoxon rank sum test. Receiver operating characteristic curves and the areas under the curve were calculated to determine the diagnostic accuracy of MK' and mean diffusivity.
RESULTS: MK' in the tumor core, but not mean diffusivity, differentiated high-grade from low-grade gliomas, and MK' differentiated glioblastomas from the remaining gliomas with high accuracy (area under the curveMK' = 0.842; PMK' < .001). MK' and mean diffusivity identified glioblastomas in the group of high-grade gliomas with similar significance and accuracy (area under the curveMK' = 0.886; area under the curvemean diffusivity = 0.876; PMK' = .003; Pmean diffusivity = .004). The mean MK' in all tissue types was comparable to that obtained by conventional diffusional kurtosis imaging.
CONCLUSIONS: The diffusional kurtosis imaging approach used here is considerably faster than conventional diffusional kurtosis imaging methods but yields comparable results. It can be accommodated in clinical protocols and enables exploration of the role of MK' as a biomarker in determining glioma subtypes or response evaluation.
© 2015 by American Journal of Neuroradiology.

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Mesh:

Year:  2015        PMID: 25977481      PMCID: PMC7964677          DOI: 10.3174/ajnr.A4311

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  23 in total

1.  Gliomas: diffusion kurtosis MR imaging in grading.

Authors:  Sofie Van Cauter; Jelle Veraart; Jan Sijbers; Ronald R Peeters; Uwe Himmelreich; Frederik De Keyzer; Stefaan W Van Gool; Frank Van Calenbergh; Steven De Vleeschouwer; Wim Van Hecke; Stefan Sunaert
Journal:  Radiology       Date:  2012-03-08       Impact factor: 11.105

2.  Three-dimensional characterization of non-gaussian water diffusion in humans using diffusion kurtosis imaging.

Authors:  Hanzhang Lu; Jens H Jensen; Anita Ramani; Joseph A Helpern
Journal:  NMR Biomed       Date:  2006-04       Impact factor: 4.044

Review 3.  Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain.

Authors:  Andrew J Steven; Jiachen Zhuo; Elias R Melhem
Journal:  AJR Am J Roentgenol       Date:  2014-01       Impact factor: 3.959

4.  Experimentally and computationally fast method for estimation of a mean kurtosis.

Authors:  Brian Hansen; Torben E Lund; Ryan Sangill; Sune Nørhøj Jespersen
Journal:  Magn Reson Med       Date:  2013-04-15       Impact factor: 4.668

5.  Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response.

Authors:  Bradford A Moffat; Thomas L Chenevert; Theodore S Lawrence; Charles R Meyer; Timothy D Johnson; Qian Dong; Christina Tsien; Suresh Mukherji; Douglas J Quint; Stephen S Gebarski; Patricia L Robertson; Larry R Junck; Alnawaz Rehemtulla; Brian D Ross
Journal:  Proc Natl Acad Sci U S A       Date:  2005-04-01       Impact factor: 11.205

Review 6.  Diffusion and diffusion tensor imaging in brain cancer.

Authors:  Elizabeth R Gerstner; A Gregory Sorensen
Journal:  Semin Radiat Oncol       Date:  2011-04       Impact factor: 5.934

7.  Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging.

Authors:  Jens H Jensen; Joseph A Helpern; Anita Ramani; Hanzhang Lu; Kyle Kaczynski
Journal:  Magn Reson Med       Date:  2005-06       Impact factor: 4.668

8.  Preliminary observations of increased diffusional kurtosis in human brain following recent cerebral infarction.

Authors:  Jens H Jensen; Maria F Falangola; Caixia Hu; Ali Tabesh; Otto Rapalino; Calvin Lo; Joseph A Helpern
Journal:  NMR Biomed       Date:  2010-10-19       Impact factor: 4.044

9.  Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences.

Authors:  Peter Raab; Elke Hattingen; Kea Franz; Friedhelm E Zanella; Heinrich Lanfermann
Journal:  Radiology       Date:  2010-01-20       Impact factor: 11.105

10.  Validation of fast diffusion kurtosis MRI for imaging acute ischemia in a rodent model of stroke.

Authors:  Phillip Zhe Sun; Yu Wang; Emiri Mandeville; Suk-Tak Chan; Eng H Lo; Xunming Ji
Journal:  NMR Biomed       Date:  2014-09-10       Impact factor: 4.044

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  30 in total

1.  Comparison of image sensitivity between conventional tensor-based and fast diffusion kurtosis imaging protocols in a rodent model of acute ischemic stroke.

Authors:  Yin Wu; Jinsuh Kim; Suk-Tak Chan; Iris Yuwen Zhou; Yingkun Guo; Takahiro Igarashi; Hairong Zheng; Gang Guo; Phillip Zhe Sun
Journal:  NMR Biomed       Date:  2016-02-26       Impact factor: 4.044

2.  Diffusion Kurtosis Imaging as a Tool in Neurotoxicology.

Authors:  Brian Hansen
Journal:  Neurotox Res       Date:  2019-08-17       Impact factor: 3.911

3.  Fast diffusion kurtosis imaging of fibrotic mouse kidneys.

Authors:  B F Kjølby; A R Khan; A Chuhutin; L Pedersen; J B Jensen; S Jakobsen; D Zeidler; R Sangill; J R Nyengaard; S N Jespersen; B Hansen
Journal:  NMR Biomed       Date:  2016-10-12       Impact factor: 4.044

4.  Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery.

Authors:  Xi-Xun Qi; Da-Fa Shi; Si-Xie Ren; Su-Ya Zhang; Long Li; Qing-Chang Li; Li-Ming Guan
Journal:  Eur Radiol       Date:  2017-11-16       Impact factor: 5.315

5.  Precision and accuracy of diffusion kurtosis estimation and the influence of b-value selection.

Authors:  Andrey Chuhutin; Brian Hansen; Sune Nørhøj Jespersen
Journal:  NMR Biomed       Date:  2017-08-25       Impact factor: 4.044

6.  Fast imaging of mean, axial and radial diffusion kurtosis.

Authors:  Brian Hansen; Noam Shemesh; Sune Nørhøj Jespersen
Journal:  Neuroimage       Date:  2016-08-15       Impact factor: 6.556

7.  Fast diffusion kurtosis imaging (DKI) with Inherent COrrelation-based Normalization (ICON) enhances automatic segmentation of heterogeneous diffusion MRI lesion in acute stroke.

Authors:  Iris Yuwen Zhou; Yingkun Guo; Takahiro Igarashi; Yu Wang; Emiri Mandeville; Suk-Tak Chan; Lingyi Wen; Mark Vangel; Eng H Lo; Xunming Ji; Phillip Zhe Sun
Journal:  NMR Biomed       Date:  2016-10-03       Impact factor: 4.044

8.  The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).

Authors:  Filip Szczepankiewicz; Danielle van Westen; Elisabet Englund; Carl-Fredrik Westin; Freddy Ståhlberg; Jimmy Lätt; Pia C Sundgren; Markus Nilsson
Journal:  Neuroimage       Date:  2016-07-20       Impact factor: 6.556

9.  Experimental considerations for fast kurtosis imaging.

Authors:  Brian Hansen; Torben E Lund; Ryan Sangill; Ebbe Stubbe; Jürgen Finsterbusch; Sune Nørhøj Jespersen
Journal:  Magn Reson Med       Date:  2015-11-26       Impact factor: 4.668

10.  Diffusion kurtosis imaging to assess correlations with clinicopathologic factors for bladder cancer: a comparison between the multi-b value method and the tensor method.

Authors:  Fang Wang; Hai-Ge Chen; Rui-Yun Zhang; Di Jin; Shuai-Shuai Xu; Guang-Yu Wu; Jian-Rong Xu
Journal:  Eur Radiol       Date:  2019-01-21       Impact factor: 5.315

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