  Low-dose emission tomography (ET) plays a crucial role in medical imaging,
enabling the acquisition of functional information for various biological
processes while minimizing the patient dose. However, the inherent randomness
in the photon counting process is a source of noise which is amplified in
low-dose ET. This review article provides an overview of existing
post-processing techniques, with an emphasis on deep neural network (NN)
approaches. Furthermore, we explore future directions in the field of NN-based
low-dose ET. This comprehensive examination sheds light on the potential of
deep learning in enhancing the quality and resolution of low-dose ET images,
ultimately advancing the field of medical imaging.
