Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Neuroimage. 2024 Sep;298:120803. doi: 10.1016/j.neuroimage.2024.120803. Epub 2024 Aug 23.
Perivascular spaces (PVS) visible on magnetic resonance imaging (MRI) are significant markers associated with various neurological diseases. Although quantitative analysis of PVS may enhance sensitivity and improve consistency across studies, the field lacks a universally validated method for analyzing images from multi-center studies.
We annotated PVS on multi-center 3D T1-weighted (T1w) images acquired using scanners from three major vendors (Siemens, General Electric, and Philips). A neural network, mcPVS-Net (multi-center PVS segmentation network), was trained using data from 40 subjects and then tested in a separate cohort of 15 subjects. We assessed segmentation accuracy against ground truth masks tailored for each scanner vendor. Additionally, we evaluated the agreement between segmented PVS volumes and visual scores for each scanner. We also explored correlations between PVS volumes and various clinical factors such as age, hypertension, and white matter hyperintensities (WMH) in a larger sample of 1020 subjects. Furthermore, mcPVS-Net was applied to a new dataset comprising both T1w and T2-weighted (T2w) images from a United Imaging scanner to investigate if PVS volumes could discriminate between subjects with differing visual scores. We also compared the mcPVS-Net with a previously published method that segments PVS from T1 images.
In the test dataset, mcPVS-Net achieved a mean DICE coefficient of 0.80, with an average Precision of 0.81 and Recall of 0.79, indicating good specificity and sensitivity. The segmented PVS volumes were significantly associated with visual scores in both the basal ganglia (r = 0.541, p < 0.001) and white matter regions (r = 0.706, p < 0.001), and PVS volumes were significantly different among subjects with varying visual scores. Segmentation performance was consistent across different scanner vendors. PVS volumes exhibited significant associations with age, hypertension, and WMH. In the United Imaging scanner dataset, PVS volumes showed good associations with PVS visual scores evaluated on either T1w or T2w images. Compared to a previously published method, mcPVS-Net showed a higher accuracy and improved PVS segmentation in the basal ganglia region.
The mcPVS-Net demonstrated good accuracy for segmenting PVS from 3D T1w images. It may serve as a useful tool for future PVS research.
磁共振成像（MRI）上可见的血管周围间隙（PVS）是与各种神经疾病相关的重要标志物。尽管 PVS 的定量分析可以提高敏感性并提高研究间的一致性，但该领域缺乏一种普遍验证的方法来分析多中心研究的图像。
我们对来自三个主要供应商（西门子、通用电气和飞利浦）的扫描仪采集的多中心 3D T1 加权（T1w）图像进行了 PVS 注释。使用来自 40 个受试者的数据训练了一个神经网络，mcPVS-Net（多中心 PVS 分割网络），然后在 15 个独立受试者的队列中进行了测试。我们针对每个扫描仪供应商的专用地面真实掩模评估了分割准确性。此外，我们还评估了分割的 PVS 体积与每个扫描仪的视觉评分之间的一致性。我们还在 1020 名受试者的更大样本中探讨了 PVS 体积与年龄、高血压和脑白质高信号（WMH）等各种临床因素之间的相关性。此外，mcPVS-Net 应用于一个新的数据集，该数据集包含联影扫描仪的 T1w 和 T2 加权（T2w）图像，以研究 PVS 体积是否可以区分视觉评分不同的受试者。我们还比较了 mcPVS-Net 与之前发表的从 T1 图像分割 PVS 的方法。
在测试数据集上，mcPVS-Net 的平均 DICE 系数为 0.80，平均精度为 0.81，召回率为 0.79，表明特异性和敏感性都很好。分割的 PVS 体积与基底节（r = 0.541，p < 0.001）和白质区域的视觉评分显着相关（r = 0.706，p < 0.001），并且在视觉评分不同的受试者中 PVS 体积存在显着差异。分割性能在不同的扫描仪供应商之间保持一致。PVS 体积与年龄、高血压和 WMH 显着相关。在联影扫描仪数据集上，PVS 体积与在 T1w 或 T2w 图像上评估的 PVS 视觉评分之间存在良好的相关性。与之前发表的方法相比，mcPVS-Net 在基底节区域的分割精度更高。
mcPVS-Net 对从 3D T1w 图像中分割 PVS 具有良好的准确性。它可能成为未来 PVS 研究的有用工具。
