Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.
Centre for Clinical Brain Sciences, Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK.
J Neurosci Methods. 2024 Mar;403:110037. doi: 10.1016/j.jneumeth.2023.110037. Epub 2023 Dec 26.
Growing interest surrounds perivascular spaces (PVS) as a clinical biomarker of brain dysfunction given their association with cerebrovascular risk factors and disease. Neuroimaging techniques allowing quick and reliable quantification are being developed, but, in practice, they require optimisation as their limits of validity are usually unspecified.
We evaluate modifications and alternatives to a state-of-the-art (SOTA) PVS segmentation method that uses a vesselness filter to enhance PVS discrimination, followed by thresholding of its response, applied to brain magnetic resonance images (MRI) from patients with sporadic small vessel disease acquired at 3 T.
The method is robust against inter-observer differences in threshold selection, but separate thresholds for each region of interest (i.e., basal ganglia, centrum semiovale, and midbrain) are required. Noise needs to be assessed prior to selecting these thresholds, as effect of noise and imaging artefacts can be mitigated with a careful optimisation of these thresholds. PVS segmentation from T1-weighted images alone, misses small PVS, therefore, underestimates PVS count, may overestimate individual PVS volume especially in the basal ganglia, and is susceptible to the inclusion of calcified vessels and mineral deposits. Visual analyses indicated the incomplete and fragmented detection of long and thin PVS as the primary cause of errors, with the Frangi filter coping better than the Jerman filter.
Limits of validity to a SOTA PVS segmentation method applied to 3 T MRI with confounding pathology are given.
Evidence presented reinforces the STRIVE-2 recommendation of using T2-weighted images for PVS assessment wherever possible. The Frangi filter is recommended for PVS segmentation from MRI, offering robust output against variations in threshold selection and pathology presentation.
由于血管周围空间 (PVS) 与脑血管危险因素和疾病有关，因此作为脑功能障碍的临床生物标志物，人们对其越来越感兴趣。目前正在开发允许快速可靠量化的神经影像学技术，但实际上，由于其有效性的限制通常不明确，因此需要进行优化。
我们评估了一种最先进的 (SOTA) PVS 分割方法的修改和替代方法，该方法使用血管增强滤波器来增强 PVS 的区分能力，然后对其响应进行阈值处理，应用于从 3T 获得的患有散发性小血管疾病的患者的脑磁共振图像 (MRI)。
该方法对阈值选择的观察者间差异具有鲁棒性，但需要为每个感兴趣区域 (即基底节、半卵圆中心和中脑) 分别设置阈值。在选择这些阈值之前需要评估噪声，因为噪声和成像伪影的影响可以通过仔细优化这些阈值来减轻。仅从 T1 加权图像进行 PVS 分割会遗漏小的 PVS，因此会低估 PVS 的数量，可能会高估单个 PVS 体积，尤其是在基底节中，并且容易受到钙化血管和矿物质沉积的影响。视觉分析表明，长而细的 PVS 不完全和碎片化的检测是错误的主要原因，Frangi 滤波器比 Jerman 滤波器的处理效果更好。
给出了一种应用于具有混杂病变的 3T MRI 的 SOTA PVS 分割方法的有效性限制。
所提供的证据加强了 STRIVE-2 建议，即在可能的情况下，尽可能使用 T2 加权图像进行 PVS 评估。建议使用 Frangi 滤波器从 MRI 中分割 PVS，该滤波器提供了对阈值选择和病理表现变化的稳健输出。
