Macro- and microvascular contributions to cerebral structural alterations in patients with asymptomatic carotid artery stenosis.

Atherosclerosis can underly internal carotid artery stenosis (ICAS), a major risk factor for ischemic stroke, as well as small vessel disease (SVD). This study aimed to investigate hemodynamics and structural alterations associated with SVD in ICAS patients. 28 patients with unilateral asymptomatic ICAS and 30 age-matched controls underwent structural (T1-/T2-weighted and diffusion tensor imaging [DTI]) and hemodynamic (pseudo-continuous arterial spin labeling and dynamic susceptibility contrast) magnetic resonance imaging. SVD-related alterations were assessed using free water (FW), FW-corrected DTI, and peak-width of skeletonized mean diffusivity (PSMD). Furthermore, cortical thickness, cerebral blood flow (CBF), and capillary transit time heterogeneity (CTH) were analyzed. Ipsilateral to the stenosis, cortical thickness was significantly decreased in the posterior dorsal cingulate cortex (p = 0.024) and temporal pole (p = 0.028). ICAS patients exhibited elevated PSMD (p = 0.005), FW (p < 0.001), and contralateral alterations in FW-corrected DTI metrics. We found significantly lateralized CBF (p = 0.011) and a tendency for lateralized CTH (p = 0.067) in the white matter (WM) related to ICAS. Elevated PSMD and FW may indicate a link between SVD and WM changes. Contralateral alterations were seen in FW-corrected DTI, whereas hemodynamic and cortical changes were mainly ipsilateral, suggesting SVD might influence global brain changes concurrent with ICAS-related hemodynamic alterations.

Hemodynamic MRI parameters to predict asymptomatic unilateral carotid artery stenosis with random forest machine learning.

Background: Internal carotid artery stenosis (ICAS) can cause stroke and cognitive decline. Associated hemodynamic impairments, which are most pronounced within individual watershed areas (iWSA) between vascular territories, can be assessed with hemodynamic-oxygenation-sensitive MRI and may help to detect severely affected patients. We aimed to identify the most sensitive parameters and volumes of interest (VOI) to predict high-grade ICAS with random forest machine learning. We hypothesized an increased predictive ability considering iWSAs and a decreased cognitive performance in correctly classified patients. Materials and methods: Twenty-four patients with asymptomatic, unilateral, high-grade carotid artery stenosis and 24 age-matched healthy controls underwent MRI comprising pseudo-continuous arterial spin labeling (pCASL), breath-holding functional MRI (BH-fMRI), dynamic susceptibility contrast (DSC), T2 and T2* mapping, MPRAGE and FLAIR. Quantitative maps of eight perfusion, oxygenation and microvascular parameters were obtained. Mean values of respective parameters within and outside of iWSAs split into gray (GM) and white matter (WM) were calculated for both hemispheres and for interhemispheric differences resulting in 96 features. Random forest classifiers were trained on whole GM/WM VOIs, VOIs considering iWSAs and with additional feature selection, respectively. Results: The most sensitive features in decreasing order were time-to-peak (TTP), cerebral blood flow (CBF) and cerebral vascular reactivity (CVR), all of these inside of iWSAs. Applying iWSAs combined with feature selection yielded significantly higher receiver operating characteristics areas under the curve (AUC) than whole GM/WM VOIs (AUC: 0.84 vs. 0.90, p = 0.039). Correctly predicted patients presented with worse cognitive performances than frequently misclassified patients (Trail-making-test B: 152.5s vs. 94.4s, p = 0.034). Conclusion: Random forest classifiers trained on multiparametric MRI data allow identification of the most relevant parameters and VOIs to predict ICAS, which may improve personalized treatments.

Imaging effective oxygen diffusivity in the human brain with multiparametric magnetic resonance imaging.

Cerebrovascular diseases can impair blood circulation and oxygen extraction from the blood. The effective oxygen diffusivity (EOD) of the capillary bed is a potential biomarker of microvascular function that has gained increasing interest, both for clinical diagnosis and for elucidating oxygen transport mechanisms. Models of capillary oxygen transport link EOD to measurable oxygen extraction fraction (OEF) and cerebral blood flow (CBF). In this work, we confirm that two well established mathematical models of oxygen transport yield nearly equivalent EOD maps. Furthermore, we propose an easy-to-implement and clinically applicable multiparametric magnetic resonance imaging (MRI) protocol for quantitative EOD mapping. Our approach is based on imaging OEF and CBF with multiparametric quantitative blood oxygenation level dependent (mq-BOLD) MRI and pseudo-continuous arterial spin labeling (pCASL), respectively. We evaluated the imaging protocol by comparing MRI-EOD maps of 12 young healthy volunteers to PET data from a published study in different individuals. Our results show comparably good correlation between MRI- and PET-derived cortical EOD, OEF and CBF. Importantly, absolute values of MRI and PET showed high accordance for all three parameters. In conclusion, our data indicates feasibility of the proposed MRI protocol for EOD mapping, rendering the method promising for future clinical evaluation of patients with cerebrovascular diseases.

Decreasing Spatial Variability of Individual Watershed Areas by Revascularization Therapy in Patients With High-Grade Carotid Artery Stenosis.

BACKGROUND: Carotid artery stenosis can impair cerebral hemodynamics especially within watershed areas (WSAs) between vascular territories. WSAs can shift because of collateral flow, which may be an indicator for increased hemodynamic implications and hence higher risk for ischemic stroke. However, whether revascularization treatment can reverse the spatial displacement of individual WSAs (iWSAs) and impaired hemodynamics remains unknown. HYPOTHESIS: That iWSAs spatially normalize because of hemodynamic improvement resulting from revascularization treatment. STUDY TYPE: Prospective. POPULATION: Sixteen patients with unilateral, high-grade carotid artery stenosis confirmed by duplex ultrasonography and 17 healthy controls. FIELD STRENGTH/SEQUENCES: A 3 T-magnetization-prepared rapid acquisition gradient echo (MPRAGE), gradient-echo echo planar dynamic susceptibility contrast (DSC), and fluid-attenuated inversion recovery (FLAIR) sequences. Additionally, contrast-enhanced 3D gradient echo magnetic resonance angiography (MRA) and diffusion-tensor imaging (DTI) spin-echo echo planar imaging were performed. ASSESSMENT: iWSAs were delineated by a recently proposed procedure based on time-to-peak maps from DSC perfusion MRI, which were also used to evaluate perfusion delay. We spatially compared iWSAs and perfusion delay before and after treatment (endarterectomy or stenting). Additionally, the Circle of Willis collateralization status was evaluated, and basic cognitive testing was conducted. STATISTICAL TESTS: Statistical tests included two-sample t-tests and Chi-squared tests. A P value < 0.05 was considered to be statistically significant. RESULTS: After revascularization, patients showed a significant spatial shift of iWSAs and significantly reduced perfusion delay ipsilateral to the stenosis. Spatial shift of iWSA (P = 0.007) and cognitive improvement (P = 0.013) were more pronounced in patients with poor pre-existing collateralization. Controls demonstrated stable spatial extent of iWSAs (P = 0.437) and symmetric perfusion delays between hemispheres over time (P = 0.773). DATA CONCLUSION: These results demonstrate the normalization of iWSA and impaired hemodynamics after revascularization in patients with high-grade carotid artery stenosis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.