Supervised learning with cyclegan for low-dose FDG PET image denoising.

Abstract

PET imaging involves radiotracer injections, raising concerns about the risk of radiation exposure. To minimize the potential risk, one way is to reduce the injected tracer. However, this will lead to poor image quality with conventional image reconstruction and processing. In this paper, we proposed a supervised deep learning model, CycleWGANs, to boost low-dose PET image quality. Validations were performed on a low dose dataset simulated from a real dataset with biopsy-proven primary lung cancer or suspicious radiological abnormalities. Low dose PET images were reconstructed on reduced PET raw data by randomly discarding events in the PET list mode data towards the count level of 1 million. Traditional image denoising methods (Non-Local Mean (NLM) and block-matching 3D(BM3D)) and two recently-published deep learning methods (RED-CNN and 3D-cGAN) were included for comparisons. At the count level of 1 million (true counts), the proposed model can accurately estimate full-dose PET image from low-dose input image, which is superior to the other four methods in terms of the mean and maximum standardized uptake value (SUVmean and SUVmax) bias for lesions and normal tissues. The bias of SUV (SUVmean, SUVmax) for lesions and normal tissues are (-2.06±3.50%,-0.84±6.94%) and (-0.45±5.59%, N/A) in the estimated PET images, respectively. However, the RED-CNN achieved the best score in traditional metrics, such as structure similarity (SSIM), peak signal to noise ratio (PSNR) and normalized root mean square error (NRMSE). Correlation and profile analyses have successfully explained this phenomenon and further suggested that our method could effectively preserve edge and also SUV values than RED-CNN, 3D-cGAN and NLM with a slightly higher noise.