Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection

TL;DR

This paper introduces a novel method combining FCN and GAN networks to generate virtual PET images from CT scans, aiming to improve lesion detection and reduce reliance on costly PET/CT scans.

Contribution

The study presents a new cross-modality synthesis approach using deep learning to generate PET images from CT data, enhancing lesion detection accuracy.

Findings

28% reduction in false positives for lesion detection

Effective generation of PET images from CT scans

Potential to replace PET/CT in clinical workflows

Abstract

In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different schemes to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.