Boespflug Erin L, Schwartz Daniel L, Lahna David, Pollock Jeffrey, Iliff Jeffrey J, Kaye Jeffrey A, Rooney William, Silbert Lisa C
From the Department of Neurology (E.L.B., D.L.S., D.L., J.A.K., L.C.S.), Advanced Imaging Research Center (E.L.B., D.L.S., W.R.), Department of Radiology (J.P.), and Department of Anesthesiology and Perioperative Medicine (J.J.I.), Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239-3098.
Radiology. 2018 Feb;286(2):632-642. doi: 10.1148/radiol.2017170205. Epub 2017 Aug 29.
Purpose To describe a fully automated segmentation method that yields object-based morphologic estimates of enlarged perivascular spaces (ePVSs) in clinical-field-strength (3.0-T) magnetic resonance (MR) imaging data. Materials and Methods In this HIPAA-compliant study, MR imaging data were obtained with a 3.0-T MR imager in research participants without dementia (mean age, 85.3 years; range, 70.4-101.2 years) who had given written informed consent. This method is built on (a) relative normalized white matter, ventricular and cortical signal intensities within T1-weighted, fluid-attenuated inversion recovery, T2-weighted, and proton density data and (b) morphologic (width, volume, linearity) characterization of each resultant cluster. Visual rating was performed by three raters, including one neuroradiologist, after established single-section guidelines. Correlations between visual counts and automated counts, as well session-to-session correlation of counts within each participant, were assessed with the Pearson correlation coefficient r. Results There was a significant correlation between counts by visual raters and automated detection of ePVSs in the same section (r = 0.65, P < .001; r = 0.69, P < .001; and r = 0.54, P < .01 for raters 1, 2, and 3, respectively). With regard to visual ratings and whole-brain count consistency, average visual rating scores were highly correlated with automated detection of total burden volume (r = 0.58, P < .01) and total ePVS number (r = 0.76, P < .01). Morphology of clusters across 28 data sets was consistent with published radiographic estimates of ePVS; mean width of clusters segmented was 3.12 mm (range, 1.7-13.5 mm). Conclusion This MR imaging-based method for multimodal autoidentification of perivascular spaces yields individual whole-brain morphologic characterization of ePVS in clinical MR imaging data and is an important tool in the detailed assessment of these features. RSNA, 2017 Online supplemental material is available for this article.
目的 描述一种全自动分割方法，该方法可在临床场强（3.0-T）磁共振（MR）成像数据中得出基于对象的扩大血管周围间隙（ePVS）形态学估计值。材料与方法 在这项符合健康保险流通与责任法案（HIPAA）的研究中，使用3.0-T MR成像仪获取了无痴呆症研究参与者（平均年龄85.3岁；范围70.4 - 101.2岁）的MR成像数据，这些参与者均已签署书面知情同意书。该方法基于（a）T1加权、液体衰减反转恢复、T2加权和质子密度数据中的相对归一化白质、脑室和皮质信号强度，以及（b）每个所得聚类的形态学（宽度、体积、线性度）特征。在既定的单节段指南之后，由包括一名神经放射科医生在内的三名评估者进行视觉评分。使用Pearson相关系数r评估视觉计数与自动计数之间的相关性，以及每个参与者计数的不同检查之间的相关性。结果 在同一节段中，视觉评估者的计数与ePVS的自动检测之间存在显著相关性（评估者1、2和3的r分别为0.65，P <.001；r = 0.69，P <.001；r = 0.54，P <.01）。关于视觉评分和全脑计数一致性，平均视觉评分与总负担体积的自动检测（r = 0.58，P <.01）和ePVS总数（r = 0.76，P <.01）高度相关。28个数据集的聚类形态与已发表的ePVS影像学估计一致；分割聚类的平均宽度为3.12毫米（范围1.7 - 13.5毫米）。结论 这种基于MR成像的血管周围间隙多模态自动识别方法可在临床MR成像数据中得出ePVS的个体全脑形态学特征，是详细评估这些特征的重要工具。RSNA，2017 本文提供在线补充材料。
