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

• Published in: European journal of nuclear medicine and molecular imaging 2024-11, Vol.51 (13), p.3874-3887
• Main Authors: Cui, Jianan, Luo, Yi, Chen, Donghe, Shi, Kuangyu, Su, Xinhui, Liu, Huafeng
• Format: Article
• Language: English
• Subjects: Background noise; Cardiology; Correlation coefficients; Datasets; Hospitals; Image acquisition; Image contrast; Image enhancement; Image filters; Image quality; Imaging; Medicine; Medicine & Public Health; Nuclear Medicine; Oncology; Original Article; Orthopedics; Positron emission; Positron emission tomography; Production costs; Radiology; Scanners
• ISSN: 1619-7070; 1619-7089; 1619-7089
• DOI: 10.1007/s00259-024-06823-6

Purpose 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. Methods In this paper, we proposed an improved CycleGAN (IE-CycleGAN) model for unpaired PET image enhancement. The proposed method 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 proposed method was validated on unpaired uEXPLORER datasets and Biograph Vision local hospital datasets. Results For the uEXPLORER dataset, the proposed method achieved better results 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 proposed method achieved higher contrast-to-noise ratios (CNR) and tumor-to-background SUVmax ratios (TBR) than NLM, BM3D, and DIP. In addition, the proposed method showed higher contrast, SUV max , and TBR than Unet (supervised) and CycleGAN (supervised) when applied to images from different scanners. Conclusion The proposed unpaired PET image enhancement method 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.