Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results
This system could enable tumor detection and drug treatment evaluation in CT-only environments, reducing the need for expensive and radioactive PET-CT scans, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of estimating PET images from CT scans using deep convolutional networks, achieving a true positive rate of 92.3% and a false positive rate of 0.25 per case for liver tumor detection.
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.