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Attenuation and scatter correction (AC) is crucial for quantitative PositronEmission Tomography (PET) imaging. Recently, direct application of AC in theimage domain using deep learning approaches has been proposed for the hybridPET/MR and dedicated PET systems that lack accompanying transmission oranatomical imaging. This study set out to investigate deep learning-based AC inthe image domain using different input settings.

Deep Learning-Based Attenuation and Scatter Correction of Brain 18F-FDG PET Images in the Image Domain