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      The Relationship between Neurite Density Measured with Confocal Microscopy in a Cleared Mouse Brain and Metrics Obtained from Diffusion Tensor and Diffusion Kurtosis Imaging

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          Abstract

          Purpose:

          Diffusional kurtosis imaging (DKI) enables sensitive measurement of tissue microstructure by quantifying the non-Gaussian diffusion of water. Although DKI is widely applied in many situations, histological correlation with DKI analysis is lacking. The purpose of this study was to determine the relationship between DKI metrics and neurite density measured using confocal microscopy of a cleared mouse brain.

          Methods:

          One thy-1 yellow fluorescent protein 16 mouse was deeply anesthetized and perfusion fixation was performed. The brain was carefully dissected out and whole-brain MRI was performed using a 7T animal MRI system. DKI and diffusion tensor imaging (DTI) data were obtained. After the MRI scan, brain sections were prepared and then cleared using aminoalcohols (CUBIC). Confocal microscopy was performed using a two-photon confocal microscope with a laser. Forty-eight ROIs were set on the caudate putamen, seven ROIs on the anterior commissure, and seven ROIs on the ventral hippocampal commissure on the confocal microscopic image and a corresponding MR image. In each ROI, histological neurite density and the metrics of DKI and DTI were calculated. The correlations between diffusion metrics and neurite density were analyzed using Pearson correlation coefficient analysis.

          Results:

          Mean kurtosis (MK) ( P = 5.2 × 10 −9, r = 0.73) and radial kurtosis ( P = 2.3 × 10 −9, r = 0.74) strongly correlated with neurite density in the caudate putamen. The correlation between fractional anisotropy (FA) and neurite density was moderate ( P = 0.0030, r = 0.42). In the anterior commissure and the ventral hippocampal commissure, neurite density and FA are very strongly correlated ( P = 1.3 × 10 −5, r = 0.90). MK in these areas were very high value and showed no significant correlation ( P = 0.48).

          Conclusion:

          DKI accurately reflected neurite density in the area with crossing fibers, potentially allowing evaluation of complex microstructures.

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          Most cited references22

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          Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging.

          This article presents two related advancements to the diffusional kurtosis imaging estimation framework to increase its robustness to noise, motion, and imaging artifacts. The first advancement substantially improves the estimation of diffusion and kurtosis tensors parameterizing the diffusional kurtosis imaging model. Rather than utilizing conventional unconstrained least squares methods, the tensor estimation problem is formulated as linearly constrained linear least squares, where the constraints ensure physically and/or biologically plausible tensor estimates. The exact solution to the constrained problem is found via convex quadratic programming methods or, alternatively, an approximate solution is determined through a fast heuristic algorithm. The computationally more demanding quadratic programming-based method is more flexible, allowing for an arbitrary number of diffusion weightings and different gradient sets for each diffusion weighting. The heuristic algorithm is suitable for real-time settings such as on clinical scanners, where run time is crucial. The advantage offered by the proposed constrained algorithms is demonstrated using in vivo human brain images. The proposed constrained methods allow for shorter scan times and/or higher spatial resolution for a given fidelity of the diffusional kurtosis imaging parametric maps. The second advancement increases the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed-form formulae. Copyright © 2010 Wiley-Liss, Inc.
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            Gliomas: diffusion kurtosis MR imaging in grading.

            To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P<.05 significance level, Mann-Whitney-Wilcoxon test, Bonferroni correction). MD, FA, mean kurtosis, radial kurtosis, and axial kurtosis in solid tumors were also normalized to the corresponding values in contralateral normal-appearing white matter (NAWM) and the contralateral posterior limb of the internal capsule (PLIC) after age correction and were compared among tumor grades. Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-grade from low-grade gliomas were 0.52 and 0.51, respectively. There were significant differences in kurtosis parameters between high-grade and low-grade gliomas; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters.
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              Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences.

              To characterize the non-Gaussian diffusion patterns of cerebral glioma microstructure with respect to the different glioma grades by using a new method called diffusional kurtosis (DK) imaging. In this study with institutional review board approval and patient consent, diffusional measures of mean kurtosis (MK), fractional anisotropy (FA), and apparent diffusion coefficient (ADC) were compared prospectively. Data were normalized to the contralateral white matter. A Mann-Whitney test was used to compare each histologic glioma subtype regarding the diffusion measurements. Receiver operating characteristic curves were used to test for the parameter with the best sensitivity and specificity for glioma grade discrimination. In 34 patients with cerebral gliomas (five World Health Organization [WHO] grade II astrocytomas, 13 WHO grade III astrocytomas, and 16 WHO grade IV glioblastomas multiforme), significantly different diffusion patterns were found among the three glioma groups. MK values increased with higher glioma malignancy, whereas ADCs tended to decrease with higher malignancy; FA values did not differ significantly among tumor groups. Significant differences between astrocytoma grades WHO II and WHO III were demonstrated only by DK values. Area under the receiver operating characteristic curve was highest for normalized MK (0.972) during testing to discriminate between low- and high-grade gliomas. This study demonstrates specific diffusion patterns for low- and high-grade gliomas, showing that DK imaging is able to depict microstructural changes within glioma tissue and is able to help differentiate among glioma grades. (c) RSNA, 2010.
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                Author and article information

                Journal
                Magn Reson Med Sci
                Magn Reson Med Sci
                mrms
                Magnetic Resonance in Medical Sciences
                Japanese Society for Magnetic Resonance in Medicine
                1347-3182
                1880-2206
                2018
                07 December 2017
                : 17
                : 2
                : 138-144
                Affiliations
                [1 ]Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
                [2 ]Department of Radiology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan
                [3 ]Research Institute for Diseases of Old Age, Juntendo University Graduate School of Medicine, Tokyo, Japan
                [4 ]Department of Radiological Sciences, Tokyo Metropolitan University Graduate School of Human Health Sciences, Tokyo, Japan
                [5 ]Research & Development Group, Hitachi Ltd., Tokyo, Japan
                [6 ]Department of Plastic and Reconstructive Surgery, Juntendo University Graduate School of Medicine, Tokyo, Japan
                [7 ]Department of Plastic and Reconstructive Surgery, Juntendo University Urayasu Hospital, Chiba, Japan
                [8 ]Department of Neuropathology, Tokyo Medical and Dental University, Tokyo, Japan
                [9 ]Department of Psychology, Chukyo University, Aichi, Japan
                [10 ]Araya Brain Imaging, Tokyo, Japan
                Author notes
                [* ]Corresponding author, Phone: +81-3-3813-3111, Fax: +81-3-3816-0958, E-mail: ririe@ 123456juntendo.ac.jp
                Article
                mrms-17-138
                10.2463/mrms.mp.2017-0031
                5891339
                29213008
                0411e676-8bde-4a7e-91ef-931f84d10158
                © 2017 Japanese Society for Magnetic Resonance in Medicine

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 06 March 2017
                : 14 September 2017
                Categories
                Major Paper

                diffusion magnetic resonance imaging,non-gaussian,histocytological preparation techniques,confocal microscopy,mice,mutant strains

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