Estimation of full-dose 4D CT perfusion images from low-dose images using conditional generative adversarial networks

CT Perfusion (CTP) imaging is one of the most common regimes for evaluation of acute ischemic stroke patients. The CTP imaging protocol typically involves the rapid acquisition of several frames of the brain volume over ~1 minute, following contrast administration. Therefore, it is associated with a relatively high radiation dose. The ability to reduce this dose, while maintaining the accuracy of image-based stroke analysis is highly desirable. However, a reduction in dose is accompanied by an increase in noise, which can compromise computation of important haemodynamic parameters during stroke analysis. In this paper, we investigate the feasibility of using 3D conditional generative adversarial networks (3D c-GANs) to achieve CTP dose reduction while preserving image quality. We simulated low-dose CTP images corresponding to tube currents of 100 mAs and 45 mAs for 18 positive acute stroke subjects and applied a 3D c-GANs model to estimate the standard dose CTP images from the simulated low-dose images. We also compared two different strategies for handling the 4D nature of the CTP data in the 3D c-GANs model. Qualitatively, the results showed excellent agreement between the estimated low-noise images and the true images. Quantitative assessment also showed good performance of the model associated with high peak signal-to-noise ratio (PNSR) around 40 dB, normalized mean squared error (NMSE) close to zero, and structural similarity index (SSIM) close to 1. By stacking the original data rather than concatenating all volumes, the results were improved by 1.05 dB PNSR, 0.005 NMSE, and 0.01 SSIM at the simulated exposure of 100mAs, and by 0.93dB PNSR, 0.019 NMSE at the simulated tube current of 45 mAs. The results show good promise for dose reduction in CTP, however we are currently performing full stroke modelling analysis on the synthetic images to validate the method.