Department of Radiology, Binhai County People's Hospital, Yancheng 224500, Jiangsu, China.
Department of Neurology, Binhai County People's Hospital, Yancheng 224500, Jiangsu, China.
Comput Intell Neurosci. 2022 Jun 13;2022:2286413. doi: 10.1155/2022/2286413. eCollection 2022.
This study was aimed at investigating the application of deep learning 4D computed tomography angiography (CTA) combined with whole brain CT perfusion (CTP) imaging in acute ischemic stroke (AIS). A total of 46 patients with ischemic stroke were selected from the hospital as the research objects. Image quality was analyzed after the 4D CTA images were obtained by perfusion imaging. The results showed that whole brain perfusion imaging based on FCN can achieve automatic segmentation. FCN segmentation results took a short time, an average of 2-3 seconds, and the Dice similarity coefficient (DSC) and mean absolute distance (MAD) were lower than those of other algorithms. FCN segmentation distance was 17.87. The parameters of the central area, the peripheral area, and the mirror area of the perfusion map were compared, and the mean transit time (MTT) and time to peak (TTP) of the lesion were prolonged compared with the mirror area. Moreover, the peripheral CBV was increased, and the differences between the parameters were significant ( < 0.05). In conclusion, using the deep learning FCN network, 4D CTA combined with whole brain CTP imaging technology can effectively analyze the perfusion state and achieve clinically personalized treatment.
本研究旨在探讨深度学习四维 CT 血管造影（CTA）联合全脑 CT 灌注（CTP）成像在急性缺血性脑卒中（AIS）中的应用。选取医院收治的缺血性脑卒中患者 46 例作为研究对象，通过灌注成像获得 4D CTA 图像后分析图像质量。结果表明，基于 FCN 的全脑灌注成像可实现自动分割，FCN 分割用时较短，平均为 2-3 秒，Dice 相似系数（DSC）和平均绝对距离（MAD）均低于其他算法，分割距离为 17.87。比较灌注图的中央区、周边区和镜像区的参数，病灶的平均通过时间（MTT）和达峰时间（TTP）较镜像区延长，且外周 CBV 升高，参数差异有统计学意义（<0.05）。结论：应用深度学习 FCN 网络，4D CTA 联合全脑 CTP 成像技术可有效分析灌注状态，实现临床个体化治疗。
