  A standard dose of radioactive tracer must be delivered into the patients
body to obtain high-quality Positron Emission Tomography (PET) images for
diagnostic purposes, which raises the risk of radiation harm. A reduced tracer
dose, on the other hand, results in poor image quality and a noise-induced
quantitative bias in PET imaging. The partial volume effect (PVE), which is the
result of PET intrinsic limited spatial resolution, is another source of
quality and quantity degradation in PET imaging. The utilization of anatomical
information for PVE correction (PVC) is not straightforward due to the internal
organ motions, patient involuntary motions, and discrepancies in the appearance
and size of the structures in anatomical and functional images. Furthermore, an
additional MR imaging session is necessary for anatomical information, which
may not be available. We set out to build a deep learning-based framework for
predicting partial volume corrected full-dose (FD-PVC) pictures from either
standard or low-dose (LD) PET images without requiring any anatomical data in
order to provide a joint solution for PVC and denoise low-dose PET images.
