Background and Purpose:Intracranial atherosclerosis is one of the main causes of stroke, and high-resolution magnetic resonance imaging provides useful imaging biomarkers related to the risk of ischemic events. This study aims to evaluate differences in histogram features between culprit and nonculprit intracranial atherosclerosis using high-resolution magnetic resonance imaging.Methods:Two hundred forty-seven patients with intracranial atherosclerosis who underwent high-resolution magnetic resonance imaging sequentially between January 2015 and December 2016 were recruited. Quantitative features, including stenosis, plaque burden, minimum luminal area, intraplaque hemorrhage, enhancement ratio, and dispersion of signal intensity (coefficient of variation), were analyzed based on T2-, T1-, and contrast-enhanced T1-weighted images. Step-wise regression analysis was used to identify key determinates differentiating culprit and nonculprit plaques and to calculate the odds ratios (ORs) with 95% CIs.Results:In total, 190 plaques were identified, of which 88 plaques (37 culprit and 51 nonculprit) were located in the middle cerebral artery and 102 (57 culprit and 45 nonculprit) in the basilar artery. Nearly 90% of culprit lesions had a degree of luminal stenosis of <70%. Multiple logistic regression analyses showed that intraplaque hemorrhage (OR, 16.294 [95% CI, 1.043–254.632]; P=0.047), minimum luminal area (OR, 1.468 [95% CI, 1.032–2.087]; P=0.033), and coefficient of variation (OR, 13.425 [95% CI, 3.987–45.204]; P<0.001) were 3 significant features in defining culprit plaques in middle cerebral artery. The enhancement ratio (OR, 9.476 [95% CI, 1.256–71.464]; P=0.029), intraplaque hemorrhage (OR, 2.847 [95% CI, 0.971–10.203]; P=0.046), and coefficient of variation (OR, 10.068 [95% CI, 2.820–21.343]; P<0.001) were significantly associated with plaque type in basilar artery. Coefficient of variation was a strong independent predictor in defining plaque type for both middle cerebral artery and basilar artery with sensitivity, specificity, and accuracy being 0.79, 0.80, and 0.80, respectively.Conclusions:Features characterized by high-resolution magnetic resonance imaging provided complementary values over luminal stenosis in defined lesion type for intracranial atherosclerosis; the dispersion of signal intensity in histogram analysis was a particularly effective predictive parameter.

中文翻译：

---

颅内动脉粥样硬化斑块的定量直方图分析：高分辨率磁共振成像研究。

背景与目的：颅内动脉粥样硬化是中风的主要原因之一，高分辨率磁共振成像提供了与缺血事件风险相关的有用的成像生物标志物。本研究旨在使用高分辨率磁共振成像评估罪犯和非罪犯颅内动脉粥样硬化之间直方图特征的差异。方法：247 名颅内动脉粥样硬化患者在 2015 年 1 月至 2016 年 12 月期间依次接受了高分辨率磁共振成像。招募。基于 T2、T1 和对比增强 T1 加权图像分析定量特征，包括狭窄、斑块负荷、最小管腔面积、斑块内出血、增强率和信号强度分散（变异系数）。逐步回归分析用于确定区分罪魁祸首和非罪魁祸首斑块的关键决定因素，并计算具有 95% CIs 的优势比 (OR)。结果：总共确定了 190 个斑块，其中 88 个斑块（37 个罪魁祸首和 51 个非罪魁祸首) 位于大脑中动脉，102 例（57 例罪犯和 45 例非罪犯）位于基底动脉。近 90% 的罪魁祸首病变的管腔狭窄程度小于 70%。多元逻辑回归分析显示斑块内出血 (OR, 16.294 [95% CI, 1.043–254.632]; 其中大脑中动脉有88个斑块（37个罪魁祸首和51个非罪魁祸首），基底动脉有102个（57个罪魁祸首和45个非罪魁祸首）。近 90% 的罪魁祸首病变的管腔狭窄程度小于 70%。多元逻辑回归分析显示斑块内出血 (OR, 16.294 [95% CI, 1.043–254.632]; 其中大脑中动脉有88个斑块（37个罪魁祸首和51个非罪魁祸首），基底动脉有102个（57个罪魁祸首和45个非罪魁祸首）。近 90% 的罪魁祸首病变的管腔狭窄程度小于 70%。多元逻辑回归分析显示斑块内出血 (OR, 16.294 [95% CI, 1.043–254.632];P = 0.047)、最小管腔面积 (OR, 1.468 [95% CI, 1.032–2.087]; P = 0.033) 和变异系数 (OR, 13.425 [95% CI, 3.987–45.204]; P <0.001)定义大脑中动脉罪魁祸首斑块的 3 个显着特征。增强比 (OR, 9.476 [95% CI, 1.256–71.464]; P = 0.029)，斑块内出血 (OR, 2.847 [95% CI, 0.971–10.203]; P = 0.046) 和变异系数 (OR, 10.068 [95% CI，2.820–21.343]；P<0.001) 与基底动脉斑块类型显着相关。变异系数是确定大脑中动脉和基底动脉斑块类型的强独立预测因子，敏感性、特异性和准确性分别为 0.79、0.80 和 0.80。结论：以高分辨率磁共振成像为特征的特征提供了补充颅内动脉粥样硬化定义病变类型中管腔狭窄的值；直方图分析中信号强度的分散是一个特别有效的预测参数。