Full-dose PET synthesis from low-dose PET using 2D high efficiency denoising diffusion probabilistic model

The purpose of this study is to reduce radiation exposure in PET imaging while preserving high-quality clinical PET images. We propose the PET Consistency Model (PET-CM), an efficient diffusion-model-based approach, to estimate full-dose PET images from low-dose PETs. PET-CM delivers synthetic images of comparable quality to state-of-the-art diffusion-based methods but with significantly higher efficiency. The process involves adding Gaussian noise to full-dose PETs through a forward diffusion process and then using a PET U-shaped network (PET-Unet) for denoising in a reverse diffusion process, conditioned on corresponding low-dose PETs. In experiments denoising one-eighth dose images to full-dose images, PET-CM achieved an MAE of 1.321±0.134%, a PSNR of 33.587±0.674 dB, an SSIM of 0.960±0.008, and an NCC of 0.967±0.011. In scenarios of reducing from 1/4 dose to full dose, PET-CM further showcased its capability with an MAE of 1.123±0.112%, a PSNR of 35.851±0.871 dB, an SSIM of 0.975±0.003, and an NCC of 0.990±0.003.