  In this work we present a novel system for PET estimation using CT scans. We
explore the use of fully convolutional networks (FCN) and conditional
generative adversarial networks (GAN) to export PET data from CT data. Our
dataset includes 25 pairs of PET and CT scans where 17 were used for training
and 8 for testing. The system was tested for detection of malignant tumors in
the liver region. Initial results look promising showing high detection
performance with a TPR of 92.3% and FPR of 0.25 per case. Future work entails
expansion of the current system to the entire body using a much larger dataset.
Such a system can be used for tumor detection and drug treatment evaluation in
a CT-only environment instead of the expansive and radioactive PET-CT scan.
