Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Xi'an Jiaotong University, Xi'an, Xi'an, China.
J Magn Reson Imaging. 2021 Oct;54(4):1326-1336. doi: 10.1002/jmri.27702. Epub 2021 May 17.
Perivascular spaces (PVSs) are important component of the brain glymphatic system. While visual rating has been widely used to assess PVS, computational measures may have higher sensitivity for capturing PVS characteristics under disease conditions.
To compute quantitative and morphological PVS features and to assess their associations with vascular risk factors and cerebral small vessel disease (CSVD).
Prospective.
One hundred sixty-one middle-aged/later middle-aged subjects (age = 60.4 ± 7.3).
3D T1-weighted, T2-weighted and T2-FLAIR sequences, and susceptibility-weighted multiecho gradient-echo sequence on a 3 T scanner.
Automated PVS segmentation was performed on sub-millimeter T2-weighted images. Quantitative and morphological PVS features were calculated in white matter (WM) and basal ganglia (BG) regions, including volume, count, size, length (L ), width (L ), and linearity. Visual PVS scores were also acquired for comparison.
Simple and multiple linear regression analyses were used to explore the associations among variables.
WM-PVS visual score and count were associated with hypertension (β = 0.161, P < 0.05; β = 0.193, P < 0.05), as were BG-PVS rating score, volume, count and L (β = 0.197, P < 0.05; β = 0.170, P < 0.05; β = 0.200, P < 0.05; β = 0.172, P < 0.05). WM-PVS size was associated with diabetes (β = 0.165, P < 0.05). WM-PVS and BG-PVS were associated with CSVD markers, especially white matter hyperintensities (WMHs) (P < 0.05). Multiple regression analysis showed that WM/BG-PVS quantitative measures were widely associated with vascular risk factors and CSVD markers (P < 0.05). Morphological measures were associated with WMH severity in WM region and also associated with lacunes and microbleeds (P < 0.05) in BG region.
These novel PVS measures may capture mild PVS alterations driven by different pathologies.
2 TECHNICAL EFFICACY: Stage 2.
血管周围空间（PVS）是脑淋巴系统的重要组成部分。虽然视觉评分已被广泛用于评估 PVS，但计算测量值可能具有更高的敏感性，可以在疾病状态下捕获 PVS 特征。
计算定量和形态学 PVS 特征，并评估它们与血管危险因素和脑小血管疾病（CSVD）的相关性。
前瞻性。
161 名中年/中老年受试者（年龄=60.4±7.3）。
3T 扫描仪上的 3D T1 加权、T2 加权和 T2-FLAIR 序列以及磁化传递加权多回波梯度回波序列。
在亚毫米 T2 加权图像上进行自动 PVS 分割。在白质（WM）和基底节（BG）区域计算定量和形态学 PVS 特征，包括体积、计数、大小、长度（L）、宽度（L）和线性度。还获得了视觉 PVS 评分进行比较。
使用简单和多元线性回归分析来探讨变量之间的相关性。
WM-PVS 视觉评分和计数与高血压相关（β=0.161，P<0.05；β=0.193，P<0.05），BG-PVS 评分、体积、计数和 L 也与高血压相关（β=0.197，P<0.05；β=0.170，P<0.05；β=0.200，P<0.05；β=0.172，P<0.05）。WM-PVS 大小与糖尿病相关（β=0.165，P<0.05）。WM-PVS 和 BG-PVS 与 CSVD 标志物相关，尤其是与脑白质高信号（WMHs）相关（P<0.05）。多元回归分析表明，WM/BG-PVS 定量测量与血管危险因素和 CSVD 标志物广泛相关（P<0.05）。形态学测量与 WM 区域的 WMH 严重程度相关，也与 BG 区域的腔隙和微出血相关（P<0.05）。
这些新的 PVS 指标可能可以捕捉由不同病理引起的轻度 PVS 改变。
2 级
2 级。
