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.

本研究提出一种使用全卷积网络和条件生成对抗网络从 CT 扫描中导出 PET 数据的新系统，并在测试中通过对肝脏区域的恶性肿瘤检测表现出高检出性能，未来工作包括使用更大的数据集将当前系统扩展到整个身体，以在 CT 唯一环境中用于肿瘤检测和药物治疗评估。