Deep Image Prior-Incorporated Direct PET Parametric Image Reconstruction

Direct parametric reconstruction algorithms have been developed to improve the statistical reliability of parametric images estimated from dynamic PET imaging data. However, these estimates are degraded by noise due to measurement error and noise propagation during reconstruction. In this study, we develop a deep image prior (DIP) regularized direct reconstruction method, where the DIP network is used to represent the estimated parametric image. By initializing the DIP with pre-trained weights and updating its network to learn the intermediate information during reconstruction, the DIP regularization leverages the available population and subject-specific features. The proposed method is applied to reconstruct ${K}_{1}$ from both simulated and patient data acquired by ⁸²Rb dynamic PET myocardial perfusion imaging. Benefiting from the nonlinear representation capability of the DIP network, the proposed method achieves superior noise versus bias/mean performance compared with the indirect and direct reconstruction methods with various regularizations formed by quadratic smoothness, dictionary learning, or fully-connected neural network. To summarize, the proposed method demonstrates its potential in improving the precision of dynamic PET imaging measurements, which will contribute to diagnostic accuracy and disease monitoring.