A likelihood-scheduled diffusion method reconstructs low-count PET images by matching the reverse-diffusion likelihood to a precomputed MLEM schedule, reducing hyperparameters and enabling real 3D reconstruction.
Model-Based Deep Learning PET Image Reconstruction Using Forward-Backward Splitting Expectation- Maximization,
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Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction
A likelihood-scheduled diffusion method reconstructs low-count PET images by matching the reverse-diffusion likelihood to a precomputed MLEM schedule, reducing hyperparameters and enabling real 3D reconstruction.