  As PET imaging is accompanied by substantial radiation exposure and cancer
risk, reducing radiation dose in PET scans is an important topic. However,
low-count PET scans often suffer from high image noise, which can negatively
impact image quality and diagnostic performance. Recent advances in deep
learning have shown great potential for recovering underlying signal from noisy
counterparts. However, neural networks trained on a specific noise level cannot
be easily generalized to other noise levels due to different noise amplitude
and variances. To obtain optimal denoised results, we may need to train
multiple networks using data with different noise levels. But this approach may
be infeasible in reality due to limited data availability. Denoising dynamic
PET images presents additional challenge due to tracer decay and continuously
changing noise levels across dynamic frames. To address these issues, we
propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks
with varying denoising power to generate optimal denoised results regardless of
the input noise levels. Evaluated using large-scale data from two medical
centers with different vendors, presented results showed that the UNN can
consistently produce promising denoised results regardless of input noise
levels, and demonstrate superior performance over networks trained on single
noise level data, especially for extremely low-count data.
