Abstract：
Research into magnetic resonance imaging (MRI)-visible perivascular spaces (PVS) has recently increased, as results from studies in different diseases and populations are cementing their association with sleep, disease phenotypes, and overall health indicators. With the establishment of worldwide consortia and the availability of large databases, computational methods that allow to automatically process all this wealth of information are becoming increasingly relevant. Several computational approaches have been proposed to assess PVS from MRI, and efforts have been made to summarise and appraise the most widely applied ones. We systematically reviewed and meta-analysed all publications available up to September 2023 describing the development, improvement, or application of computational PVS quantification methods from MRI. We analysed 67 approaches and 60 applications of their implementation, from 112 publications. The two most widely applied were the use of a morphological filter to enhance PVS-like structures, with Frangi being the choice preferred by most, and the use of a U-Net configuration with or without residual connections. Older adults or population studies comprising adults from 18 years old onwards were, overall, more frequent than studies using clinical samples. PVS were mainly assessed from T2-weighted MRI acquired in 1.5T and/or 3T scanners, although combinations using it with T1-weighted and FLAIR images were also abundant. Common associations researched included age, sex, hypertension, diabetes, white matter hyperintensities, sleep and cognition, with occupation-related, ethnicity, and genetic/hereditable traits being also explored. Despite promising improvements to overcome barriers such as noise and differentiation from other confounds, a need for joined efforts for a wider testing and increasing availability of the most promising methods is now paramount.
摘要：
磁共振成像（MRI）的研究-可见血管周围空间（PVS）最近有所增加，因为对不同疾病和人群的研究结果正在巩固它们与睡眠的联系，疾病表型，和整体健康指标。随着世界范围内联盟的建立和大型数据库的可用性，允许自动处理所有这些丰富信息的计算方法正变得越来越重要。已经提出了几种计算方法来评估MRI的PVS，并努力总结和评估应用最广泛的方法。我们系统地审查和荟萃分析了截至2023年9月的所有出版物，描述了这一发展，改进,或应用MRI计算PVS定量方法。我们分析了67种方法和60种实施方法的应用，112种出版物两个应用最广泛的是使用形态滤波器来增强PVS样结构，Frangi是大多数人的首选，以及使用具有或不具有剩余连接的U-Net配置。从18岁开始的老年人或由成年人组成的人口研究是，总的来说,比使用临床样本的研究更频繁。PVS主要通过1.5T和/或3T扫描仪获得的T2加权MRI进行评估，尽管使用它与T1加权和FLAIR图像的组合也很丰富。研究的常见关联包括年龄，性别,高血压,糖尿病,白质高强度，睡眠和认知，与职业有关，种族,和遗传/可遗传特征也在探索中。尽管有希望的改进可以克服诸如噪音和与其他困惑的区别等障碍，现在最重要的是，需要共同努力进行更广泛的测试，并增加最有前途的方法的可用性。
