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

Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection