Stratification of Heterogeneous Diffusion MRI Ischemic Lesion With Kurtosis Imaging Evaluation of Mean Diffusion and Kurtosis MRI Mismatch in an Animal Model of Transient Focal Ischemia
ABSTRACT Ischemic tissue damage is heterogeneous, resulting in complex patterns in the widely used diffusion-weighted MRI. Our study examined the spatiotemporal characteristics of diffusion kurtosis imaging in an animal model of transient middle cerebral artery occlusion.
Adult male Wistar rats (N=18) were subjected to 90 minutes middle cerebral artery occlusion. Multiparametric MR images were obtained during middle cerebral artery occlusion and 20 minutes after reperfusion with diffusion-weighted MRI obtained using 8 b-values from 250 to 3000 s/mm(2) in 6 diffusion gradient directions. Diffusion and kurtosis lesions were outlined in shuffled images by 2 investigators independently. T(2) MRI was obtained 24 hours after middle cerebral artery occlusion to evaluate stroke outcome.
Mean diffusion lesion (23.5%±8.1%, percentage of the brain slice) was significantly larger than mean kurtosis lesion (13.2%±2.0%) during middle cerebral artery occlusion. Mean diffusion lesion decreased significantly after reperfusion (13.8%±4.3%), whereas mean kurtosis lesion showed little change (13.0%±2.5%) with their lesion size difference being insignificant.
We demonstrated that mean diffusion/mean kurtosis mismatch recovered reasonably well on reperfusion, whereas regions with concurrent mean diffusion and mean kurtosis deficits showed poor recovery. Diffusion kurtosis imaging may help stratify heterogeneous diffusion-weighted MRI lesions for enhanced characterization of ischemic tissue injury.
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- "Furthermore, studies on brain maturation (Cheung et al., 2009), temporal lobe epilepsy (Gao et al., 2012) and stroke lesion visualization (Grinberg et al., 2012) have reported the enhanced sensitivity of DKI parameters over DTI parameters in discerning differences in tissue status. In other studies (Blockx et al., 2012; Cheung et al., 2012; Grossman et al., 2012), a combined use of kurtosis and DTI parameters proved to be advantageous in detecting tissue changes. These observations indicate that kurtosis parameters can discriminate healthy from abnormal tissue under various pathophysiological conditions. "
ABSTRACT: Imaging techniques that provide detailed insights into structural tissue changes after stroke can vitalize development of treatment strategies and diagnosis of disease. Diffusion-weighted MRI has been playing an important role in this regard. Diffusion kurtosis imaging (DKI), a recent addition to this repertoire, has opened up further possibilities in extending our knowledge about structural tissue changes related to injury as well as plasticity. In this study we sought to discern the microstructural alterations characterized by changes in diffusion tensor imaging (DTI) and DKI parameters at a chronic time point after experimental stroke. Of particular interest was the question of whether DKI parameters provide additional information in comparison to DTI parameters in understanding structural tissue changes, and if so, what their histological origins could be. Region-of-interest analysis and a data-driven approach to identify tissue abnormality were adopted to compare DTI- and DKI-based parameters in post mortem rat brain tissue, which were compared against immunohistochemistry of various cellular characteristics. The unilateral infarcted area encompassed the ventrolateral cortex and the lateral striatum. Results from region-of-interest analysis in the lesion borderzone and contralateral tissue revealed significant differences in DTI and DKI parameters between ipsi- and contralateral sensorimotor cortex, corpus callosum, internal capsule and striatum. This was reflected by a significant reduction in ipsilateral mean diffusivity (MD) and fractional anisotropy (FA) values, accompanied by significant increases in kurtosis parameters in these regions. Data-driven analysis to identify tissue abnormality revealed that the use of kurtosis-based parameters improved the detection of tissue changes in comparison with FA and MD, both in terms of dynamic range and in being able to detect changes to which DTI parameters were insensitive. This was observed in gray as well as white matter. Comparison against immunohistochemical stainings divulged no straightforward correlation between diffusion-based parameters and individual neuronal, glial or inflammatory tissue features. Our study demonstrates that DKI allows sensitive detection of structural tissue changes that reflect post-stroke tissue remodeling. However, our data also highlights the generic difficulty in unambiguously asserting specific causal relationships between tissue status and MR diffusion parameters.NeuroImage 01/2014; 97:363–373. · 6.36 Impact Factor
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ABSTRACT: The aim of this study was to investigate the microstructural sensitivity of the statistical distribution and diffusion kurtosis (DKI) models of non-monoexponential signal attenuation in the brain using diffusion-weighted MRI (DWI). We first developed a simulation of 2-D water diffusion inside simulated tissue consisting of semi-permeable cells and a variable cell size. We simulated a DWI acquisition of the signal in a volume using a pulsed gradient spin echo (PGSE) pulse sequence, and fitted the models to the simulated DWI signals using b-values up to 2500s/mm(2). For comparison, we calculated the apparent diffusion coefficient (ADC) of the monoexponential model (b-value=1000s/mm(2)). In separate experiments, we varied the cell size (5-10-15μm), cell volume fraction (0.50-0.65-0.80), and membrane permeability (0.001-0.01-0.1mm/s) to study how the fitted parameters tracked simulated microstructural changes. The ADC was sensitive to all the simulated microstructural changes except the decrease in membrane permeability. The ADC increased with larger cell size, smaller cell volume fraction, and larger membrane permeability. The σ(stat) of the statistical distribution model increased exclusively with a decrease in cell volume fraction. The K(app) of the DKI model was exclusively increased with decreased cell size and decreased with increasing membrane permeability. These results suggest that the non-monoexponential models of water diffusion have different, specific microstructural sensitivity, and a combination of the models may give insights into the microstructural underpinning of tissue pathology.Journal of Magnetic Resonance 02/2013; 230C:19-26. DOI:10.1016/j.jmr.2013.01.014 · 2.32 Impact Factor
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ABSTRACT: PURPOSE: Amyloid deposition in the brain is considered an initial event in the progression of Alzheimer's disease. We hypothesized that the presence of amyloid plaques in the brain of APP/presenilin 1 mice leads to higher diffusion kurtosis measures due to increased microstructural complexity. As such, our purpose was to provide an in vivo proof of principle for detection of amyloidosis by diffusion kurtosis imaging (DKI). METHODS: APPKM670/671NL /presenilin 1 L166P mice (n = 5) and wild-type littermates (n = 5) underwent DKI at the age of 16 months. Averaged diffusion and diffusion kurtosis parameters were obtained for multiple regions (hippocampus-cortex-thalamus-cerebellum). After DKI, mice were sacrificed for amyloid staining. RESULTS: Histograms of the frequency distribution of the DKI parameters tended to shift to higher values. After normalization of absolute values to the cerebellum, a nearly plaque-free region, mean, radial, and axial diffusion kurtosis were significantly higher in APP/presenilin 1 mice as compared to wild-type in the cortex and thalamus, regions demonstrating substantial amyloid staining. CONCLUSION: The current study, although small-scale, suggests increased DKI metrics, in the absence of alterations in diffusion tensor imaging metrics in the cortex and thalamus of APP/presenilin 1 mice with established amyloidosis. These results warrant further investigations on the potential of DKI as a sensitive marker for Alzheimer's disease. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.Magnetic Resonance in Medicine 04/2013; 69(4). DOI:10.1002/mrm.24680 · 3.40 Impact Factor