IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement.

Abstract

Technological advances in instruments have greatly promoted the development of positron emission tomography (PET) scanners. State-of-the-art PET scanners such as uEXPLORER can collect PET images of significantly higher quality. However, these scanners are not currently available in most local hospitals due to the high cost of manufacturing and maintenance. Our study aims to convert low-quality PET images acquired by common PET scanners into images of comparable quality to those obtained by state-of-the-art scanners without the need for paired low- and high-quality PET images. In this paper, we proposed an improved CycleGAN (IE-CycleGAN) model for unpaired PET image enhancement. The proposedmethod is based on CycleGAN, and the correlation coefficient loss and patient-specific prior loss were added to constrain the structure of the generated images. Furthermore, we defined a normalX-to-advanced training strategy to enhance the generalization ability of the network. The proposedmethod was validated on unpaired uEXPLORER datasets and Biograph Vision local hospital datasets. For the uEXPLORER dataset, the proposedmethod achieved betterresults than non-local mean filtering (NLM), block-matching and 3D filtering (BM3D), and deep image prior (DIP), which are comparable to Unet (supervised) and CycleGAN (supervised). For the Biograph Vision local hospital datasets, the proposedmethod achieved higher contrast-to-noise ratios(CNR) and tumor-to-background SUVmax ratios (TBR) than NLM, BM3D, and DIP. In addition, the proposedmethod showed higher contrast, SUVmax,and TBR than Unet (supervised) and CycleGAN (supervised) when applied to images from different scanners. The proposed unpaired PET image enhancementmethod outperforms NLM, BM3D, and DIP. Moreover, it performs better than the Unet(supervised) and CycleGAN(supervised) when implemented on local hospital datasets, which demonstrates its excellent generalization ability.