Zeng Cuidie, Wu Xiaoling, Ouyang Fusheng, Guo Baoliang, Zhang Xiao, Ma Jianghua, Zeng Dong, Zhang Bin
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
Research (Wash D C). 2025 Apr 30;8:0689. doi: 10.34133/research.0689. eCollection 2025.
Computed tomography perfusion (CTP) plays a crucial role in guiding reperfusion therapy and patient selection for acute ischemic stroke (AIS) through perfusion parameter maps of the brain; however, its widespread use is limited by the complexity of acquisition protocols and high radiation dose. Previous studies have attempted to reduce radiation exposure by equally lowering the temporal sampling rate; however, it may miss the peak of arterial enhancement, leading to underestimation of blood flow parameter. Here, we investigate the feasibility of using a generative adversarial network (GAN) to generate perfusion maps from 3 phases of CTP (mCTP). The three phases were chosen based on the multiphase computed tomography angiography scanning protocol: the peak arterial input function phase, the peak venous output function phase, and the delayed venous output function phase. The findings demonstrate that the GAN model achieved high visual overlap and performance for cerebral blood flow and time-to-maximum maps, with a mean structural similarity index measure of 0.921 to 0.971 and 0.817 to 0.883, a mean normalized root mean squared error of 0.019 to 0.108 and 0.058 to 0.064, and a mean learned perceptual image patch similarity of 0.039 to 0.088 and 0.141 to 0.146, respectively. For the 2 external datasets, the volume agreement between the model- and CTP-derived infarct and hypoperfusion areas was the intraclass correlation coefficient of 0.731 to 0.883 and 0.499 to 0.635, and the Spearman correlation coefficient of 0.720 to 0.808 and 0.533 to 0.6540, respectively. Qualitative assessments of diagnostic quality further confirmed that the mCTP-derived maps were comparable to those obtained from traditional CTP. In conclusion, the GAN-based model is effective in generating perfusion maps from mCTP, which could serve as a viable alternative to traditional CTP in the diagnostic evaluation of AIS.
计算机断层扫描灌注成像（CTP）通过脑灌注参数图在指导急性缺血性卒中（AIS）的再灌注治疗和患者选择方面发挥着关键作用；然而，其广泛应用受到采集协议复杂性和高辐射剂量的限制。以往的研究试图通过同等降低时间采样率来减少辐射暴露；然而，这可能会错过动脉强化的峰值，导致血流参数估计不足。在此，我们研究了使用生成对抗网络（GAN）从CTP的三个阶段（mCTP）生成灌注图的可行性。这三个阶段是根据多期计算机断层扫描血管造影扫描协议选择的：动脉输入函数峰值阶段、静脉输出函数峰值阶段和延迟静脉输出函数阶段。研究结果表明，GAN模型在脑血流量和达峰时间图方面实现了高度的视觉重叠和性能，平均结构相似性指数测量值分别为0.921至0.971和0.817至0.883，平均归一化均方根误差分别为0.019至0.108和0.058至0.064，平均学习感知图像块相似性分别为0.039至0.088和0.141至0.146。对于2个外部数据集，模型衍生的梗死灶和灌注不足区域与CTP衍生区域之间的体积一致性，组内相关系数分别为0.731至0.883和0.499至0.635，Spearman相关系数分别为0.720至0.808和0.533至0.6540。诊断质量的定性评估进一步证实，mCTP衍生的图与传统CTP获得的图相当。总之，基于GAN的模型在从mCTP生成灌注图方面是有效的，这在AIS的诊断评估中可作为传统CTP的可行替代方案。
