Perfusion Parameter Map Generation from 3 Phases of Computed Tomography Perfusion in Stroke Using Generative Adversarial Networks

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.