Enlarged perivascular spaces in brain MRI: Automated quantification in four regions
Enlarged perivascular spaces in brain MRI: Automated quantification in four regions
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DOI：
10.1016/j.neuroimage.2018.10.026
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发表时间：
2019-01-15
期刊：
影响因子：
5.7
通讯作者：
de Bruijne, Marleen
中科院分区：
文献类型：
--
作者：
Dubost, Florian;Yilmaz, Pinar;de Bruijne, Marleen
血管周围间隙扩大（PVS）是MRI中可见的脑结构变化，在衰老中很常见，被认为是脑小血管疾病的反映。因此，评估PVS的负担有希望作为脑成像标记物。PVS的视觉和手动评分是一项繁琐且依赖于计算机的任务。自动化方法将推进对PVS病因学的研究，可以帮助评估衰老中的“正常”负担，并可以评估PVS作为脑小血管疾病生物标志物的潜力。在这项工作中，我们提出并评估了一种自动化的方法来量化PVS在中脑，脑脊膜，基底神经节和半卵圆中心。我们还比较了（早期建立的）PVS和视觉PVS评分与自动PVS评分的决定因素之间的关联，以验证自动PVS评分是否可以在流行病学和临床研究中取代PVS的视觉评分。我们的方法是一种基于卷积神经网络回归的深度学习算法，并且取决于成功的大脑结构分割。在我们的工作中，我们使用FreeSurfer分割。我们训练和验证了我们的方法T2对比MR图像从2115名受试者参与了一项基于人群的研究。这些扫描由一位专家评分员进行视觉评分，他计算了每个大脑区域中PVS的数量。视觉和自动评分之间的协议被认为是优秀的所有四个地区，组内相关系数（ICC）在0.75和0.88之间。这些值高于观察者间视觉评分的一致性（ICC在0.62和0.80之间）。扫描-再扫描再现性高（ICC在0.82和0.93之间）。包括年龄在内的PVS的20个决定因素与自动评分之间的关联与PVS的20个决定因素与视觉评分之间的关联相似。我们的结论是，这种方法可以取代视觉评分，并促进大规模的流行病学和临床研究的PVS。
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a "normal" burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease. In this work, we propose and evaluate an automated method to quantify PVS in the midbrain, hippocampi, basal ganglia and centrum semiovale. We also compare associations between (earlier established) determinants of PVS and visual PVS scores versus the automated PVS scores, to verify whether automated PVS scores could replace visual scoring of PVS in epidemiological and clinical studies. Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations. We trained and validated our method on T2-contrast MR images acquired from 2115 subjects participating in a population-based study. These scans were visually scored by an expert rater, who counted the number of PVS in each brain region. Agreement between visual and automated scores was found to be excellent for all four regions, with intraclass correlation coefficients (ICCs) between 0.75 and 0.88. These values were higher than the inter-observer agreement of visual scoring (ICCs between 0.62 and 0.80). Scan-rescan reproducibility was high (ICCs between 0.82 and 0.93). The association between 20 determinants of PVS, including aging, and the automated scores were similar to those between the same 20 determinants of PVS and visual scores. We conclude that this method may replace visual scoring and facilitate large epidemiological and clinical studies of PVS.