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Generative models improve radiomics reproducibility in low dose CTs: a simulation study.

Junhua Chen1, Chong Zhang1, Alberto Traverso1

  • 1Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, 6229 ET, The Netherlands.

Physics in Medicine and Biology
|July 21, 2021
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Summary

Generative models like encoder-decoder networks and conditional generative adversarial networks can improve radiomic feature reproducibility in noisy low-dose CT scans. This approach enhances the reliability of radiomic analysis for clinical applications.

Keywords:
computed tomographydenoisinggenerative modelsradiomicsreproducibility

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Area of Science:

  • Medical Image Analysis
  • Radiomics
  • Artificial Intelligence in Healthcare

Background:

  • Reproducibility of radiomic features is crucial for clinical application but is hampered by image noise, particularly in low-dose CT scans.
  • Traditional denoising methods may not sufficiently preserve radiomic information.
  • Generative models offer a potential solution for enhancing image quality and feature reproducibility.

Purpose of the Study:

  • To investigate the use of generative models (encoder-decoder network and conditional generative adversarial network) for denoising low-dose CT images.
  • To assess the impact of generative model-based denoising on the reproducibility of radiomic features.
  • To compare the performance of generative models against a traditional non-local means denoising algorithm.

Main Methods:

  • Simulated low-dose CT images were created by adding noise to full-dose CT sinograms at two levels: low-noise and high-noise.
  • Encoder-decoder networks (EDN) and conditional generative adversarial networks (CGAN) were trained on high-noise CTs to denoise low-noise CTs.
  • Performance was evaluated using concordance correlation coefficients (CCC) on simulated noisy images and real-world same-day repeated low-dose CT scans.

Main Results:

  • Both EDN and CGAN significantly improved the concordance correlation coefficients (CCC) for radiomic features in low-noise images (from 0.87 to 0.92) and high-noise images (from 0.68 to 0.92).
  • Generative models enhanced the test-retest reliability of radiomic features on real low-dose CT data, increasing the mean CCC from 0.89 to 0.94.
  • The models demonstrated the ability to denoise images at different noise levels without re-training, provided noise intensity was not excessively high.

Conclusions:

  • Denoising using EDN and CGANs can effectively improve the reproducibility of radiomic features derived from noisy low-dose CT scans.
  • Generative models offer a promising approach to enhance the reliability of radiomics for clinical decision-making.
  • This study represents a novel application of generative models to improve radiomic feature reproducibility in low-dose CT imaging.