Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results

TL;DR

This paper introduces a deep learning system that generates virtual PET images from CT scans, aiming to facilitate tumor detection without the need for actual PET scans.

Contribution

It presents a novel approach using FCN and GAN architectures to synthesize PET data from CT scans, demonstrating initial promising results.

Findings

High tumor detection accuracy with 92.3% TPR

Low false positive rate of 0.25 per case

Potential for CT-only tumor detection applications

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.