Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China.
Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China.
J Healthc Eng. 2021 Nov 11;2021:4463975. doi: 10.1155/2021/4463975. eCollection 2021.
Our objective was to study the predictive value of CT perfusion imaging based on automatic segmentation algorithm for evaluating collateral blood flow status in the outcome of reperfusion therapy for ischemic stroke. All data of 30 patients with ischemic stroke reperfusion in our hospital were collected and examined by CT perfusion imaging. Convolutional neural network (CNN) algorithm was used to segment perfusion imaging map and evaluate the results. The patients were grouped by regional leptomeningeal collateral score (rLMCs). Binary logistic regression was used to analyze the independent influencing factors of collateral blood flow on brain CT perfusion. The modified Scandinavian Stroke Scale was used to evaluate the prognosis of patients, and the effects of different collateral flow conditions on prognosis were obtained. The accuracy of CNN segmentation image is 62.61%, the sensitivity is 87.42%, the similarity coefficient is 93.76%, and the segmentation result quality is higher. Blood glucose (95% CI = 0.943, =0.028) and ischemic stroke history (95% CI = 0.855, =0.003) were independent factors affecting the collateral blood flow status of stroke patients. CBF (95% CI = 0.818, =0.008) and CBV (95% CI = 0.796, =0.016) were independent influencing factors of CT perfusion parameters. After 3 weeks of onset, the prognostic function defect score of the good collateral flow group (11.11%) was lower than that of the poor group (41.67%) ( < 0.05). The automatic segmentation algorithm has more accurate segmentation ability for stroke CT perfusion imaging and plays a good auxiliary role in the diagnosis of clinical stroke reperfusion therapy. The collateral blood flow state based on CT perfusion imaging is helpful to predict the treatment outcome of patients with ischemic stroke and further predict the prognosis of patients.
我们的目的是研究基于自动分割算法的 CT 灌注成像对评估缺血性卒中再灌注治疗中侧支血流状态的预测价值。收集我院 30 例缺血性卒中再灌注患者的 CT 灌注成像数据，采用卷积神经网络（CNN）算法对灌注成像图进行分割评估。将患者按区域性软脑膜侧支评分（rLMCs）分组。采用二元逻辑回归分析侧支血流对脑 CT 灌注的独立影响因素。采用改良斯堪的纳维亚卒中量表评估患者预后，获得不同侧支血流状态对预后的影响。CNN 分割图像的准确率为 62.61%，灵敏度为 87.42%，相似系数为 93.76%，分割结果质量较高。血糖（95%CI=0.943，=0.028）和缺血性卒中史（95%CI=0.855，=0.003）是影响卒中患者侧支血流状态的独立因素。CBF（95%CI=0.818，=0.008）和 CBV（95%CI=0.796，=0.016）是 CT 灌注参数的独立影响因素。发病后 3 周，良好侧支血流组（11.11%）的预后功能缺陷评分低于不良组（41.67%）（<0.05）。自动分割算法对卒中 CT 灌注成像具有更准确的分割能力，对临床卒中再灌注治疗的诊断具有良好的辅助作用。基于 CT 灌注成像的侧支血流状态有助于预测缺血性卒中患者的治疗效果，并进一步预测患者的预后。
