用磁共振灌注成像区分远端脑梗与癫痫发作，准确率超九成。
Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI
- 提取动态对比增强MRI的灌注图特征，分析脑区差异。
- 模型区分远端梗死与癫痫发作的准确率达90%，特异性强。
- 结果可解释，适合临床快速鉴别疑难脑卒中病例。
急性缺血性脑卒中（AIS）与卒中模拟症（SMs），尤其是中、小血管闭塞情况，诊断仍具挑战。尽管急诊常采用计算机断层扫描（CT）方案，但其对远端闭塞的检出敏感性有限。本研究探讨磁共振灌注（MRP）成像在区分远端AIS与癫痫发作（常见SM）中的潜力。基于162例患者数据（129例AIS，33例癫痫发作），从动态对比增强（DSC）图像中提取区域灌注图描述符（PMDs）。统计分析发现颞叶和枕叶多个脑区的PMD存在显著组间差异。半球不对称性分析进一步凸显这些区域的判别能力。以PMDs为输入的逻辑回归模型，在受试者工作特征曲线下面积（AUROC）达0.90，精确率-召回率曲线下面积（AUPRC）为0.74，特异性92%，敏感性73%，表明其具备优异的区分能力。研究支持进一步探索基于MRP的PMD作为可解释特征，用于鉴别真实卒中与多种模拟症。代码已开源：https://github.com/Marijn311/PMD_extraction_and_analysis
原文摘要 · Abstract (English)
Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge. While computed tomography (CT) based protocols are commonly used in emergency settings, their sensitivity for detecting distal occlusions is limited. This study explores the potential of magnetic resonance perfusion (MRP) imaging as a tool for differentiating distal AIS from epileptic seizures, a prevalent SM. Using a retrospective dataset of 162 patients (129 AIS, 33 seizures), we extracted region-wise perfusion map descriptors (PMDs) from dynamic susceptibility contrast (DSC) images. Statistical analyses identified several brain regions, located mainly in the temporal and occipital lobe, exhibiting significant group differences in certain PMDs. Hemispheric asymmetry analyses further highlighted these regions as discriminative. A logistic regression model trained on PMDs achieved an area under the receiver operating characteristic (AUROC) curve of 0.90, and an area under the precision recall curve (AUPRC) of 0.74, with a specificity of 92% and a sensitivity of 73%, suggesting strong performance in distinguishing distal AIS from seizures. These findings support further exploration of MRP-based PMDs as interpretable features for distinguishing true strokes from various mimics. The code is openly available at our GitHub https://github.com/Marijn311/PMD_extraction_and_analysis{github.com/Marijn311/PMD\_extraction\_and\_analysis
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