Improving reproducibility and performance of radiomics in low-dose CT using cycle GANs.

ABSTRACT:

Background

As a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low-dose CT scans are still poor, mostly due to noise. Deep learning generative models can be used to denoise these images and in turn improve radiomics' reproducibility and performance. However, most generative models are trained on paired data, which can be difficult or impossible to collect.

Purpose

In this article, we investigate the possibility of denoising low-dose CTs using cycle generative adversarial networks (GANs) to improve radiomics reproducibility and performance based on unpaired datasets.

Methods and materials

Two cycle GANs were trained: (1) from paired data, by simulat

SUBMITTER: Chen J 

PROVIDER: S-EPMC9588275 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature