Clinical PET in oncology.

Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review

In modern medicine, combined PET-CT is a commonly-used tool in clinical diagnostics, which is especially important in oncology for staging or treatment planning. Variations in FDG uptake visible in a PET image, which indicate variances in metabolic activity, are not visually recognizable within a CT scan from the same region, making both imaging modalities necessary for diagnosis and exposing the patient to a high amount of radiation. In this study, we investigate the possibility of using generative adversarial networks (GANs) to synthesize a PET image from a CT scan to predict metabolic activity.

Prediction of FDG uptake in Lung Tumors from CT Images Using Generative Adversarial Networks

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Prediction of FDG uptake in Lung Tumors from CT Images Using Generative Adversarial Networks