Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study

• URL: http://arxiv.org/abs/2104.15050v1
• Date: Fri, 30 Apr 2021 15:18:57 GMT
• Title: Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study
• Authors: Junhua Chen, Chong Zhang, Alberto Traverso, Ivan Zhovannik, Andre Dekker, Leonard Wee and Inigo Bermejo
• Abstract summary: Radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans. In this article, we investigate the possibility of improving the radiomic features calculated on noisy CTs by using generative models for denoising. The results show that denoising using encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) can improve the radiomic features calculated on noisy CTs.
• Score: 4.7849095200575045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radiomics is an active area of research in medical image analysis, the low reproducibility of radiomics has limited its applicability to clinical practice. This issue is especially prominent when radiomic features are calculated from noisy images, such as low dose computed tomography (CT) scans. In this article, we investigate the possibility of improving the reproducibility of radiomic features calculated on noisy CTs by using generative models for denoising.One traditional denoising method - non-local means - and two generative models - encoder-decoder networks (EDN) and conditional generative adversarial networks (CGANs) - were selected as the test models. We added noise to the sinograms of full dose CTs to mimic low dose CTs with two different levels of noise: low-noise CT and high-noise CT. Models were trained on high-noise CTs and used to denoise low-noise CTs without re-training. We also test the performance of our model in real data, using dataset of same-day repeat low dose CTs to assess the reproducibility of radiomic features in denoised images. The EDN and the CGAN improved the concordance correlation coefficients (CCC) of radiomic features for low-noise images from 0.87 to 0.92 and for high-noise images from 0.68 to 0.92 respectively. Moreover, the EDN and the CGAN improved the test-retest reliability of radiomic features (mean CCC increased from 0.89 to 0.94) based on real low dose CTs. The results show that denoising using EDN and CGANs can improve the reproducibility of radiomic features calculated on noisy CTs. Moreover, images with different noise levels can be denoised to improve the reproducibility using these models without re-training, as long as the noise intensity is equal or lower than that in high-noise CTs. To the authors' knowledge, this is the first effort to improve the reproducibility of radiomic features calculated on low dose CT scans.

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