Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior

Abstract

Parametric imaging obtained from kinetic modeling analysis of dynamic positron emission tomography (PET) data is a useful tool for quantifying tracer kinetics. However, pixel-wise time-activity curves have high noise levels which lead to poor quality of parametric images. To solve this limitation, we proposed a new image denoising method based on deep image prior (DIP). Like the original DIP method, the proposed DIP method is an unsupervised method, in which no training dataset is required. However, the difference is that our method can simultaneously denoise all dynamic PET images. Moreover, we propose a modified version of the DIP method called double DIP (DDIP), which has two DIP architectures. The additional DIP model is used to generate high-quality input data for the second DIP model. Computer simulations were performed to evaluate the performance of the proposed DIP-based methods. Our simulation results showed that the DDIP method outperformed the single DIP method. In addition, the DDIP method combined with data augmentation could generate PET parametric images with superior image quality compared to the spatiotemporal-based non-local means filtering and high constrained backprojection. Our preliminary results show that our proposed DDIP method is a novel and effective unsupervised method for simultaneously denoising dynamic PET images.

Appendix

Fig. 9 Parametric images of K₁, k₂, k₃, and K_i obtained using KEM-reconstructed dynamic PET images with the proposed DIP-based denoising methods: SDIP and SDIP-DA. Both SDIP and SDIP-DA methods were repeated twice

Fig. 10 RMSE of K₁, k₂, k₃, and K_i for SDIP and SDIP-DA. Both SDIP and SDIP-DA were repeated twice

Fig. 11 The four time-averaged PET images (x_mean and ${\mathrm{x}}_{\mathrm{mean}}^{*}$) obtained from noisy dynamic PET images (top) and the first DIP output of the DDIP model (bottom)