PET Image Denoising Based on 3D Denoising Diffusion Probabilistic Model: Evaluations on Total-Body Datasets

Boxiao Yu, Savas Ozdemir, Yafei Dong, Wei Shao, Kuangyu Shi, Kuang Gong

Abstract: Due to various physical degradation factors and limited photon counts detected, obtaining high-quality images from low-dose Positron emission tomography (PET) scans is challenging. The Denoising Diffusion Probabilistic Model (DDPM), an advanced distribution learning-based generative model, has shown promising performance across various computer-vision tasks. However, currently DDPM is mainly investigated in 2D mode, which has limitations for PET image denoising, as PET is usually acquired, reconstructed, and analyzed in 3D mode. In this work, we proposed a 3D DDPM method for PET image denoising, which employed a 3D convolutional network to train the score function, enabling the network to learn 3D distribution. The total-body -FDG PET datasets acquired from the Siemens Biograph Vision Quadra scanner (axial field of view > 1 m) were employed to evaluate the 3D DDPM method, as these total-body datasets needed 3D operations the most to leverage the rich information from different axial slices. All models were trained on 1/20 low-dose images and then evaluated on 1/4, 1/20, and 1/50 low-dose images, respectively. Experimental results indicated that 3D DDPM significantly outperformed 2D DDPM and 3D UNet in qualitative and quantitative assessments, capable of recovering finer structures and more accurate edge contours from low-quality PET images. Moreover, 3D DDPM revealed greater robustness when there were noise level mismatches between training and testing data. Finally, comparing 3D DDPM with 2D DDPM in terms of uncertainty revealed 3D DDPM’s higher confidence in reproducibility.