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

Harvard University, Cambridge, Massachusetts, United States
Stroke (Impact Factor: 5.72). 07/2012; 43(8):2252-4. DOI: 10.1161/STROKEAHA.112.661926
Source: PubMed


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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    • "These metrics are believed to reflect the heterogeneity of the intra-voxel diffusion environment and are thus indicators of microstructural complexity. A number of studies have shown that kurtosis-based diffusion metrics are altered for a variety of neuropathologies , such as stroke (Cheung et al., 2012; Hui et al., 2012; Jensen et al., 2011), cancer (Raab et al., 2010; Van Cauter et al., 2012 ), Alzheimer's disease (Benitez et al., 2014; Falangola et al., 2013; Fieremans et al., 2013; Gong et al., 2013), epilepsy (Gao et al., 2012; Lee et al., 2013 Lee et al., , 2014 Zhang et al., 2013), Parkinson's disease (Kamagata et al., 2013Kamagata et al., , 2014), attention deficit hyperactivity disorder (Adisetiyo et al., 2014; Helpern et al., 2011), trauma (Grossman et al., 2012Grossman et al., , 2013 Zhuo et al., 2012), and autism (Lazar et al., 2014). Since the kurtosis tensor is a pure diffusion measure, without any explicit connections to specific properties of brain tissue microstructure, a clear-cut biophysical interpretation of the information it provides for a particular circumstance (e.g., brain region or disease) is often challenging (Rudrapatna et al., 2014). "
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    ABSTRACT: A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects. Copyright © 2014 Elsevier Inc. All rights reserved.
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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. "
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