Full‐dose whole‐body PET synthesis from low‐dose PET using high‐efficiency denoising diffusion probabilistic model: PET consistency model

Purpose Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. Methods We introduce PET Consistency Model (PET‐CM), an efficient diffusion‐based method for generating high‐quality full‐dose PET images from low‐dose PET images. It employs a two‐step process, adding Gaussian noise to full‐dose PET images in the forward diffusion, and then denoising them using a PET Shifted‐window Vision Transformer (PET‐VIT) network in the reverse diffusion. The PET‐VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full‐dose PET images. PET‐CM achieves state‐of‐the‐art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal‐to‐noise ratio (PSNR), multi‐scale structure similarity index (SSIM), normalized cross‐correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full‐dose PET images from low‐dose inputs. Results In experiments comparing eighth‐dose to full‐dose images, PET‐CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state‐of‐the‐art diffusion‐based model with PET‐CM reaching this result 12× faster. Similarly, in the quarter‐dose to full‐dose image experiments, PET‐CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. Conclusions We propose PET‐CM, the first efficient diffusion‐model‐based method, for estimating full‐dose PET images from low‐dose images. PET‐CM provides comparable quality to the state‐of‐the‐art diffusion model with higher efficiency. By utilizing this approach, it