Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

中文翻译：

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中风动脉自旋标记半影组织的深度学习检测。

背景和目的——急性缺血性卒中患者血管内治疗的选择通常依赖于动态磁敏感对比磁共振成像或计算机断层扫描灌注。动态磁化率对比磁共振成像需要注射对比剂，而计算机断层扫描灌注需要高剂量的电离辐射。这项工作的目的是开发和评估一种基于深度学习 (DL) 的算法，以帮助选择合适的急性缺血性卒中患者进行基于 3 维伪连续动脉自旋标记 (pCASL) 的血管内治疗。方法 - 137 名急性缺血性卒中患者在 1.5T 和 3.0T 西门子 MR 系统上扫描的总共 167 组 3 维 pCASL 数据图像集用于神经网络训练。同时获得的动态磁化率对比磁共振成像用于生成低灌注大脑区域的标签，并使用商业软件进行分析。DL 和 6 种机器学习 (ML) 算法经过 10 倍交叉验证的训练。血管内治疗的资格是根据 DEFUSE 3 试验（缺血性卒中影像评估后的血管内治疗）中灌注/弥散不匹配的标准回顾性确定的。经过训练的 DL 算法进一步应用于在 1.5T 和 3T 通用电气 MR 系统上获取的十二个 3 维 pCASL 数据集，无需对参数进行微调。结果 - DL 算法可以预测 pCASL 中动态磁化率对比度定义的低灌注区域，曲线下的体素面积为 0.958，而 6 种 ML 算法的范围从 0.897 到 0.933。对于受试者级别血管内治疗资格的回顾性确定，DL 算法实现了 92% 的准确度，灵敏度为 0.89，特异性为 0.95。当应用于 GE pCASL 数据时，DL 算法实现了 0.94 的曲线下体素面积和 92% 的血管内治疗资格的受试者级准确率。结论 - pCASL 灌注磁共振成像结合 DL 算法为辅助急性缺血性卒中患者的血管内治疗决策提供了一种有前景的方法。当应用于 GE pCASL 数据时，DL 算法实现了 0.94 的曲线下体素面积和 92% 的血管内治疗资格的受试者级准确率。结论 - pCASL 灌注磁共振成像结合 DL 算法为辅助急性缺血性卒中患者的血管内治疗决策提供了一种有前景的方法。当应用于 GE pCASL 数据时，DL 算法实现了 0.94 的曲线下体素面积和 92% 的血管内治疗资格的受试者级准确率。结论 - pCASL 灌注磁共振成像结合 DL 算法为辅助急性缺血性卒中患者的血管内治疗决策提供了一种有前景的方法。