Department of Diagnostic and Interventional Neuroradiology, Universal Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Department of Clinical Radiology, University Hospital of Münster, Münster, Germany.
Clin Neuroradiol. 2019 Mar;29(1):109-115. doi: 10.1007/s00062-017-0636-2. Epub 2017 Oct 20.
The severity of white matter lesions (WML) is a risk factor of hemorrhage and predictor of clinical outcome after ischemic stroke; however, in contrast to magnetic resonance imaging (MRI) reliable quantification for this surrogate marker is limited for computed tomography (CT), the leading stroke imaging technique. We aimed to present and evaluate a CT-based automated rater-independent method for quantification of microangiopathic white matter changes.
Patients with suspected minor stroke (National Institutes of Health Stroke scale, NIHSS < 4) were screened for the analysis of non-contrast computerized tomography (NCCT) at admission and compared to follow-up MRI. The MRI-based WML volume and visual Fazekas scores were assessed as the gold standard reference. We employed a recently published probabilistic brain segmentation algorithm for CT images to determine the tissue-specific density of WM space. All voxel-wise densities were quantified in WM space and weighted according to partial probabilistic WM content. The resulting mean weighted density of WM space in NCCT, the surrogate of WML, was correlated with reference to MRI-based WML parameters.
The process of CT-based tissue-specific segmentation was reliable in 79 cases with varying severity of microangiopathy. Voxel-wise weighted density within WM spaces showed a noticeable correlation (r = -0.65) with MRI-based WML volume. Particularly in patients with moderate or severe lesion load according to the visual Fazekas score the algorithm provided reliable prediction of MRI-based WML volume.
Automated observer-independent quantification of voxel-wise WM density in CT significantly correlates with microangiopathic WM disease in gold standard MRI. This rapid surrogate of white matter lesion load in CT may support objective WML assessment and therapeutic decision-making during acute stroke triage.
脑白质病变（WML）的严重程度是出血的危险因素和缺血性卒中后临床转归的预测因子；然而，与磁共振成像（MRI）相比，计算机断层扫描（CT）作为主要的卒中成像技术，可靠地定量评估这个替代标志物的方法有限。我们旨在提出并评估一种基于 CT 的、自动、与观察者无关的方法，用于定量评估微血管性脑白质病变。
筛选疑似小卒中（美国国立卫生研究院卒中量表，NIHSS＜4）的患者进行入院时非对比 CT（NCCT）分析，并与随访 MRI 比较。将 MRI 上的 WML 体积和视觉 Fazekas 评分作为金标准参考。我们采用最近发表的用于 CT 图像的概率性脑分割算法来确定 WM 空间的组织特异性密度。对所有体素的密度进行 WM 空间定量，并根据部分概率性 WM 含量进行加权。NCCT 中 WM 空间的平均加权密度，即 WML 的替代物，与 MRI 上的 WML 参数相关。
在 79 例具有不同程度微血管病变的患者中，基于 CT 的组织特异性分割过程是可靠的。WM 空间内的体素加权密度与 MRI 上的 WML 体积具有显著相关性（r=-0.65）。特别是在视觉 Fazekas 评分显示中度或重度病变负荷的患者中，该算法能够可靠地预测 MRI 上的 WML 体积。
在 CT 上自动、与观察者无关的体素 WM 密度定量与金标准 MRI 上的微血管性 WM 疾病显著相关。CT 上快速的 WML 负荷替代物可能支持急性卒中分诊期间的客观 WML 评估和治疗决策。
