  Positron emission tomography (PET) is widely used in various clinical
applications, including cancer diagnosis, heart disease and neuro disorders.
The use of radioactive tracer in PET imaging raises concerns due to the risk of
radiation exposure. To minimize this potential risk in PET imaging, efforts
have been made to reduce the amount of radio-tracer usage. However, lowing dose
results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of
which will heavily affect clinical diagnosis. Besides, the ill-conditioning of
low-dose PET image reconstruction makes it a difficult problem for iterative
reconstruction algorithms. Previous methods proposed are typically complicated
and slow, yet still cannot yield satisfactory results at significantly low
dose. Here, we propose a deep learning method to resolve this issue with an
encoder-decoder residual deep network with concatenate skip connections.
Experiments shows the proposed method can reconstruct low-dose PET image to a
standard-dose quality with only two-hundredth dose. Different cost functions
for training model are explored. Multi-slice input strategy is introduced to
provide the network with more structural information and make it more robust to
noise. Evaluation on ultra-low-dose clinical data shows that the proposed
method can achieve better result than the state-of-the-art methods and
reconstruct images with comparable quality using only 0.5% of the original
regular dose.
