  Deep convolutional neural networks (DCNN) have demonstrated its capability to
convert MR image to pseudo CT for PET attenuation correction in PET/MRI.
Conventionally, attenuated events are corrected in sinogram space using
attenuation maps derived from CT or MR-derived pseudo CT. Separately, scattered
events are iteratively estimated by a 3D model-based simulation using
down-sampled attenuation and emission sinograms. However, no studies have
investigated joint correction of attenuation and scatter using DCNN in image
space. Therefore, we aim to develop and optimize a DCNN model for attenuation
and scatter correction (ASC) simultaneously in PET image space without
additional anatomical imaging or time-consuming iterative scatter simulation.
For the first time, we demonstrated the feasibility of directly producing PET
images corrected for attenuation and scatter using DCNN (PET-DCNN) from
noncorrected PET (PET-NC) images.
