Abstract
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.
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Acknowledgment
This research was supported by the Israel Science Foundation (grant No. 1918/16).
Part of this work was funded by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI).
Avi Ben-Cohen’s scholarship was funded by the Buchmann Scholarships Fund.
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Ben-Cohen, A., Klang, E., Raskin, S.P., Amitai, M.M., Greenspan, H. (2017). Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2017. Lecture Notes in Computer Science(), vol 10557. Springer, Cham. https://doi.org/10.1007/978-3-319-68127-6_6
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