  High noise and low spatial resolution are two key confounding factors that
limit the qualitative and quantitative accuracy of PET images. AI models for
image denoising and deblurring are becoming increasingly popular for
post-reconstruction enhancement of PET images. We present here a detailed
review of recent efforts for AI-based PET image enhancement with a focus on
network architectures, data types, loss functions, and evaluation metrics. We
also highlight emerging areas in this field that are quickly gaining
popularity, identify barriers to large-scale adoption of AI models for PET
image enhancement, and discuss future directions.
