Brain Research Imaging Centre, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), University of Edinburgh, Edinburgh, United Kingdom; SINAPSE (Scottish Imaging Network, A Platform for Scientific Excellence) collaboration, Scotland, United Kingdom.
J Magn Reson Imaging. 2013 Oct;38(4):774-85. doi: 10.1002/jmri.24047. Epub 2013 Feb 25.
Enlarged perivascular spaces (EPVS), visible in brain MRI, are an important marker of small vessel disease and neuroinflammation. We systematically evaluated the literature up to June 2012 on possible methods for their computational assessment and analyzed confounds with lacunes and small white matter hyperintensities. We found six studies that assessed/identified EPVS computationally by seven different methods, and four studies that described techniques to automatically segment similar structures and are potentially suitable for EPVS segmentation. T2-weighted MRI was the only sequence that identified all EPVS, but FLAIR and T1-weighted images were useful in their differentiation. Inconsistency within the literature regarding their diameter and terminology, and overlap in shape, intensity, location, and size with lacunes, conspires against their differentiation and the accuracy and reproducibility of any computational segmentation technique. The most promising approach will need to combine various MR sequences and consider all these features for accurate EPVS determination.
脑 MRI 可见的血管周围间隙扩大(EPVS)是小血管疾病和神经炎症的一个重要标志物。我们系统地评估了截至 2012 年 6 月有关其计算评估的可能方法的文献,并分析了与腔隙和小的脑白质高信号之间的混杂因素。我们发现了六项通过七种不同方法计算/识别 EPVS 的研究,以及四项描述自动分割类似结构的技术的研究,这些技术可能适用于 EPVS 分割。T2 加权 MRI 是唯一能识别所有 EPVS 的序列,但 FLAIR 和 T1 加权图像在区分 EPVS 方面很有用。文献中关于其直径和术语的不一致性,以及与腔隙在形状、强度、位置和大小上的重叠,使得区分 EPVS 及其任何计算分割技术的准确性和可重复性变得复杂。最有前途的方法将需要结合各种 MR 序列,并考虑所有这些特征,以准确确定 EPVS。