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Score-based generative models have demonstrated highly promising results formedical image reconstruction tasks in magnetic resonance imaging or computedtomography. However, their application to Positron Emission Tomography (PET) isstill largely unexplored. PET image reconstruction involves a variety ofchallenges, including Poisson noise with high variance and a wide dynamicrange. To address these challenges, we propose several PET-specific adaptationsof score-based generative models. The proposed framework is developed for both2D and 3D PET. In addition, we provide an extension to guided reconstructionusing magnetic resonance images. We validate the approach through extensive 2Dand 3D $\textit{in-silico}$ experiments with a model trained onpatient-realistic data without lesions, and evaluate on data without lesions aswell as out-of-distribution data with lesions. This demonstrates the proposedmethod's robustness and significant potential for improved PET reconstruction.

Score-Based Generative Models for PET Image Reconstruction