  In this work we present a novel system for generation of virtual PET images
using CT scans. We combine a fully convolutional network (FCN) with a
conditional generative adversarial network (GAN) to generate simulated PET data
from given input CT data. The synthesized PET can be used for false-positive
reduction in lesion detection solutions. Clinically, such solutions may enable
lesion detection and drug treatment evaluation in a CT-only environment, thus
reducing the need for the more expensive and radioactive PET/CT scan. Our
dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans
for training and 37 for testing. Different schemes to achieve the synthesized
output were qualitatively compared. Quantitative evaluation was conducted using
an existing lesion detection software, combining the synthesized PET as a false
positive reduction layer for the detection of malignant lesions in the liver.
Current results look promising showing a 28% reduction in the average false
positive per case from 2.9 to 2.1. The suggested solution is comprehensive and
can be expanded to additional body organs, and different modalities.
