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Background: Intracranial atherosclerotic disease tends to affect multiple arterial segments. Using whole-brain vessel wall imaging, we sought to study the differences in plaque features among various types of plaques in patients with a recent unilateral anterior circulation ischemic stroke., Methods and Results: Sixty-one patients with unilateral anterior circulation ischemic stroke were referred to undergo whole-brain vessel wall imaging (before and after contrast) within 1 month of symptom onset for intracranial atherosclerotic disease evaluations. Each plaque was classified as a culprit, probably culprit, or nonculprit lesion, according to its likelihood of causing the stroke. The associations between plaque features (thickening pattern, plaque-wall contrast ratio, high signal on T1-weighted images, plaque contrast enhancement ratio, enhancement grade, and enhancement pattern) and culprit lesions were estimated using mixed multivariable logistic regression after adjustment for maximum wall thickness. In 52 patients without motion corruption in whole-brain vessel wall imaging, a total of 178 intracranial plaques in the anterior circulation were identified, including 52 culprit lesions (29.2%), 51 probably culprit lesions (28.7%), and 75 nonculprit lesions (42.1%). High signal on T1-weighted images (adjusted odds ratio, 9.1; 95% confidence interval, 1.9-44.1; P =0.006), grade 2 (enhancement ratio of plaque ≥ enhancement ratio of pituitary) contrast enhancement (adjusted odds ratio, 17.4; 95% confidence interval, 1.8-164.9; P =0.013), and type 2 (≥50% cross-sectional wall involvement) enhancement pattern (adjusted odds ratio, 10.1; 95% confidence interval, 1.3-82.2; P =0.030) were independently associated with culprit lesions., Conclusions: High signal on T1-weighted images, grade 2 contrast enhancement, and type 2 enhancement pattern are associated with cerebrovascular ischemic events, which may provide valuable insights into risk stratification., (© 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.)

Background: It has been shown that intracranial atherosclerotic stenosis (ICAS) has heterogeneous features in terms of plaque instability and vascular remodeling. Therefore, quantitative information on the changes of intracranial atherosclerosis and lenticulostriate arteries (LSAs) may potentially improve understanding of the pathophysiological mechanisms underlying stroke and may guide the treatment and work-up strategies. Our present study aimed to use a novel whole-brain high-resolution cardiovascular magnetic resonance imaging (WB-HRCMR) to assess both ICAS plaques and LSAs in recent stroke patients., Methods: Twenty-nine symptomatic and 23 asymptomatic ICAS patients were enrolled in this study from Jan 2015 through Sep 2017 and all patients underwent WB-HRCMR. Intracranial atherosclerotic plaque burden, plaque enhancement volume, plaque enhancement index, as well as the number and length of LSAs were evaluated in two groups. Enhancement index was calculated as follows: ([Signal intensity (SI) plaque /SI normal wall on post-contrast imaging] - [SI plaque /SI normal wall on matched pre-contrast imaging])/(SI plaque / SI normal wall on matched pre-contrast imaging). Logistic regression analysis was used to investigate the independent high risk plaque and LSAs features associated with stroke., Results: Symptomatic ICAS patients exhibited larger enhancement plaque volume (20.70 ± 3.07 mm 3 vs. 6.71 ± 1.87 mm 3 P = 0.001) and higher enhancement index (0.44 ± 0.08 vs. 0.09 ± 0.06 P = 0.001) compared with the asymptomatic ICAS. The average length of LSAs in symptomatic ICAS (20.95 ± 0.87 mm) was shorter than in asymptomatic ICAS (24.04 ± 0.95 mm) (P = 0.02). Regression analysis showed that the enhancement index (100.43, 95% CI - 4.02-2510.96; P = 0.005) and the average length of LSAs (0.80, 95% CI - 0.65-0.99; P = 0.036) were independent factors for predicting of stroke., Conclusion: WB-HRCMR enabled the comprehensive quantitative evaluation of intracranial atherosclerotic lesions and perforating arteries. Symptomatic ICAS had distinct plaque characteristics and shorter LSA length compared with asymptomatic ICAS.

Objective: To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD). Methods: Vessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straighten, and resample the interested vessel segment into 2-D slices. A U-net-like fully convolutional networks (FCN) method was proposed for automated vessel wall segmentation by hierarchical extraction of low- and high-order convolutional features. Results: The network was trained and validated on 1160 slices and tested on 545 slices. The proposed segmentation method demonstrated satisfactory agreement with manual segmentations with Dice coefficient of 0.89 for the lumen and 0.77 for the vessel wall. The method was further applied to a clinical study of additional 12 symptomatic and 12 asymptomatic patients with >50% ICAD stenosis at the middle cerebral artery (MCA). Normalized wall index at the focal MCA ICAD lesions was found significantly larger in symptomatic patients compared to asymptomatic patients. Conclusion: We have presented an automated vessel wall segmentation method based on FCN as well as the IVA framework for 3-D intracranial MR VWI. Significance: This approach would make large-scale quantitative plaque analysis more realistic and promote the adoption of MR VWI in ICAD management. [ABSTRACT FROM AUTHOR]