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