Generation of Conventional ¹⁸F-FDG PET Images from ¹⁸F-Florbetaben PET Images Using Generative Adversarial Network: A...

Background and Objectives: ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PET_FDG) image can visualize neuronal injury of the brain in Alzheimer's disease. Early-phase amyloid PET image is reported to be similar to PET_FDG image. This study aimed to generate PET_FDG images from ¹⁸F-florbetaben PET (PET_FBB) images using a generative adversarial network (GAN) and compare the generated PET_FDG (PET_GE-FDG) with real PET_FDG (PET_RE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). Materials and Methods: Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, 110 participants with both PET_FDG and PET_FBB images at baseline were included. The paired PET_FDG and PET_FBB images included six and four subset images, respectively. Each subset image had a 5 min acquisition time. These subsets were randomly sampled and divided into 249 paired PET_FDG and PET_FBB subset images for the training datasets and 95 paired subset images for the validation datasets during the deep-learning process. The deep learning model used in this study is composed of a GAN with a U-Net. The differences in the SSIM and PSNR values between the PET_GE-FDG and PET_RE-FDG images in the cycleGAN and pix2pix models were evaluated using the independent Student's t-test. Statistical significance was set at p ≤ 0.05. Results: The participant demographics (age, sex, or diagnosis) showed no statistically significant differences between the training (82 participants) and validation (28 participants) groups. The mean SSIM between the PET_GE-FDG and PET_RE-FDG images was 0.768 ± 0.135 for the cycleGAN model and 0.745 ± 0.143 for the pix2pix model. The mean PSNR was 32.4 ± 9.5 and 30.7 ± 8.0. The PET_GE-FDG images of the cycleGAN model showed statistically higher mean SSIM than those of the pix2pix model (p < 0.001). The mean PSNR was also higher in the PET_GE-FDG images of the cycleGAN model than those of pix2pix model (p < 0.001). Conclusions: We generated PET_FDG images from PET_FBB images using deep learning. The cycleGAN model generated PET_GE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PET_FDG images. These may provide additional information for the management of Alzheimer's disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses.