MR guided PET image denoising based on denoising diffusion probabilistic model and data consistency constraint

Due to various physical degradation factors, the signal-to-noise ratio (SNR) and image quality of PET needs further improvements. In this work, we proposed a denoising diffusion probabilistic model-based framework for PET image denoising, where the MR prior image was supplied as the additional network input, and the PET information was included in the iterative refinement steps based on Gaussian distribution assumption. 140 ¹⁸F-MK- 6240 datasets were used in the evaluation, with 1/4 and 1/8 low-dose levels tested for different methods. Global and regional quantifications show that the proposed framework can outperform the Unet-based denoising and MR-guided nonlocal mean denoising methods.