ArticleIEEE transactions on medical imaging2026
Deep Image Prior-Incorporated Direct PET Parametric Image Reconstruction.
Article in IEEE transactions on medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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