  Positron emission tomography (PET) is a cornerstone of modern radiology. The
ability to detect cancer and metastases in whole body scans fundamentally
changed cancer diagnosis and treatment. One of the main bottlenecks in the
clinical application is the time it takes to reconstruct the anatomical image
from the deluge of data in PET imaging. State-of-the art methods based on
expectation maximization can take hours for a single patient and depend on
manual fine-tuning. This results not only in financial burden for hospitals but
more importantly leads to less efficient patient handling, evaluation, and
ultimately diagnosis and treatment for patients. To overcome this problem we
present a novel PET image reconstruction technique based on a deep
convolutional encoder-decoder network, that takes PET sinogram data as input
and directly outputs full PET images. Using realistic simulated data, we
demonstrate that our network is able to reconstruct images >100 times faster,
and with comparable image quality (in terms of root mean squared error)
relative to conventional iterative reconstruction techniques.
