Dataset Information

Stroke infarct volume estimation in fixed tissue: Comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model.

ABSTRACT: Diffusion kurtosis imaging (DKI) is a new promising MRI technique with microstructural sensitivity superior to conventional diffusion tensor (DTI) based methods. In stroke, considerable mismatch exists between the infarct lesion outline obtained from the two methods, kurtosis and diffusion tensor derived metrics. We aim to investigate if this mismatch can be examined in fixed tissue. Our investigation is based on estimates of mean diffusivity (MD) and mean (of the) kurtosis tensor (MKT) obtained using recent fast DKI methods requiring only 19 images. At 24 hours post stroke, rat brains were fixed and prepared. The infarct was clearly visible in both MD and MKT maps. The MKT lesion volume was roughly 31% larger than the MD lesion volume. Subsequent histological analysis (hematoxylin) revealed similar lesion volumes to MD. Our study shows that structural components underlying the MD/MKT mismatch can be investigated in fixed tissue and therefore allows a more direct comparison between lesion volumes from MRI and histology. Additionally, the larger MKT infarct lesion indicates that MKT do provide increased sensitivity to microstructural changes in the lesion area compared to MD.

SUBMITTER: Bay V

PROVIDER: S-EPMC5919652 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Stroke infarct volume estimation in fixed tissue: Comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model.

Bay Vibeke V Kjølby Birgitte F BF Iversen Nina K NK Mikkelsen Irene K IK Ardalan Maryam M Nyengaard Jens R JR Jespersen Sune N SN Drasbek Kim R KR Østergaard Leif L Hansen Brian B

PloS one 20180426 4

Diffusion kurtosis imaging (DKI) is a new promising MRI technique with microstructural sensitivity superior to conventional diffusion tensor (DTI) based methods. In stroke, considerable mismatch exists between the infarct lesion outline obtained from the two methods, kurtosis and diffusion tensor derived metrics. We aim to investigate if this mismatch can be examined in fixed tissue. Our investigation is based on estimates of mean diffusivity (MD) and mean (of the) kurtosis tensor (MKT) obtained ...[more]

PMID: 29698450

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Project description:PURPOSE:Diffusion kurtosis imaging (DKI) is an advanced magnetic resonance imaging modality that is known to be sensitive to changes in the underlying microstructure of the brain. Image voxels in diffusion weighted images, however, are typically relatively large making them susceptible to partial volume effects, especially when part of the voxel contains cerebrospinal fluid. In this work, we introduce the "Diffusion Kurtosis Imaging with Free Water Elimination" (DKI-FWE) model that separates the signal contributions of free water and tissue, where the latter is modeled using DKI. THEORY AND METHODS:A theoretical study of the DKI-FWE model, including an optimal experiment design and an evaluation of the relative goodness of fit, is carried out. To stabilize the ill-conditioned estimation process, a Bayesian approach with a shrinkage prior (BSP) is proposed. In subsequent steps, the DKI-FWE model and the BSP estimation approach are evaluated in terms of estimation error, both in simulation and real data experiments. RESULTS:Although it is shown that the DKI-FWE model parameter estimation problem is ill-conditioned, DKI-FWE was found to describe the data significantly better compared to the standard DKI model for a large range of free water fractions. The acquisition protocol was optimized in terms of the maximally attainable precision of the DKI-FWE model parameters. The BSP estimator is shown to provide reliable DKI-FWE model parameter estimates. CONCLUSION:The combination of the DKI-FWE model with BSP is shown to be a feasible approach to estimate DKI parameters, while simultaneously eliminating free water partial volume effects. Magn Reson Med 80:802-813, 2018. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Project description:PurposeTo quantitatively compare the potential of various diffusion parameters obtained from monoexponential, biexponential, and stretched exponential diffusion-weighted imaging models and diffusion kurtosis imaging in the grading of gliomas.Materials and methodsThis study was approved by the local ethics committee, and written informed consent was obtained from all subjects. Both diffusion-weighted imaging and diffusion kurtosis imaging were performed in 69 patients with pathologically proven gliomas by using a 3-T magnetic resonance (MR) imaging unit. An isotropic apparent diffusion coefficient (ADC), true ADC, pseudo-ADC, and perfusion fraction were calculated from diffusion-weighted images by using a biexponential model. A water molecular diffusion heterogeneity index and distributed diffusion coefficient were calculated from diffusion-weighted images by using a stretched exponential model. Mean diffusivity, fractional anisotropy, and mean kurtosis were calculated from diffusion kurtosis images. All values were compared between high-grade and low-grade gliomas by using a Mann-Whitney U test. Receiver operating characteristic and Spearman rank correlation analysis were used for statistical evaluations.ResultsADC, true ADC, perfusion fraction, water molecular diffusion heterogeneity index, distributed diffusion coefficient, and mean diffusivity values were significantly lower in high-grade gliomas than in low-grade gliomas (U = 109, 56, 129, 6, 206, and 229, respectively; P < .05). Pseudo-ADC and mean kurtosis values were significantly higher in high-grade gliomas than in low-grade gliomas (U = 98 and 8, respectively; P < .05). Both water molecular diffusion heterogeneity index (area under the receiver operating characteristic curve [AUC] = 0.993) and mean kurtosis (AUC = 0.991) had significantly greater AUC values than ADC (AUC = 0.866), mean diffusivity (AUC = 0.722), and fractional anisotropy (AUC = 0.500) in the differentiation of low-grade and high-grade gliomas (P < .05).ConclusionWater molecular diffusion heterogeneity index and mean kurtosis values may provide additional information and improve the grading of gliomas compared with conventional diffusion parameters.

Project description:PurposeTo retrospectively determine the accuracy of diffusion-weighted (DW) magnetic resonance (MR) imaging for identifying cancer in the prostate peripheral zone (PZ) and to assess the accuracy of tumor volume measurements made with T2-weighted imaging and combined T2-weighted and DW MR imaging by using surgical pathologic examination as the reference standard.Materials and methodsThe institutional review board issued a waiver of informed consent for this HIPAA-compliant study. Forty-two patients underwent endorectal MR at 1.5 T before undergoing radical prostatectomy for prostate cancer and had at least one PZ tumor larger than 0.1 cm(3) at surgical pathologic examination. On T2-weighted images, an experienced radiologist outlined suspected PZ tumors. Two apparent diffusion coefficient (ADC) cutoff values were identified by using the Youden index and published literature. Image cluster analysis was performed on voxels within the suspected tumor regions. Associations between volume measurements from imaging and from pathologic examination were assessed by using concordance correlation coefficients (CCCs). The sensitivity and specificity of ADCs for identifying malignant PZ voxels were calculated.ResultsIn identifying malignant voxels, respective ADC cutoff values of 0.0014 and 0.0016 mm(2)/sec yielded sensitivity of 82% and 95% and specificity of 85% and 65%, respectively. Sixty PZ cancer lesions larger than 0.1 cm(3) were found at pathologic examination; 43 were detected by the radiologist. CCCs between imaging and pathologic tumor volume measurements were 0.36 for T2-weighted imaging, and 0.46 and 0.60 for combined T2-weighted and DW MR imaging with ADC cutoffs of 0.0014 and 0.0016 mm(2)/sec, respectively; the CCC of combined T2-weighted and DW MR imaging (ADC cutoff, 0.0016 mm(2)/sec) was significantly higher (P = .006) than that of T2-weighted imaging alone.ConclusionAdding DW MR to T2-weighted imaging can significantly improve the accuracy of prostate PZ tumor volume measurement.Supplemental materialhttp://radiology.rsnajnls.org/cgi/content/full/252/2/449/DC1.

Dataset Information

Stroke infarct volume estimation in fixed tissue: Comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model.

Publications

Stroke infarct volume estimation in fixed tissue: Comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model.

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