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DeCoGAN: MVCT image denoising via coupled generative adversarial network.

Kunpeng Zhang1, Tianye Niu2,3, Lei Xu1

  • 1Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.

Physics in Medicine and Biology
|July 9, 2024
PubMed
Summary

A novel deep learning method, DeCoGAN, significantly enhances image quality in megavoltage computed tomography (MVCT) used for image-guided radiotherapy. This advanced denoising technique preserves crucial details while reducing noise, improving targeting accuracy in radiation therapy.

Keywords:
MVCT denoisingcycle-consistencydeep learninggenerative adversarial networkshared-latent space

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Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Image-guided radiotherapy utilizes megavoltage computed tomography (MVCT) for precise patient targeting.
  • MVCT images suffer from substantial noise due to high-voltage radiation, compromising clarity and accuracy.
  • Effective denoising is crucial for improving MVCT image quality in helical tomotherapy.

Purpose of the Study:

  • To develop and evaluate a deep learning-based method for enhancing MVCT image quality.
  • To improve the clarity and structural integrity of noisy MVCT images.
  • To provide a more reliable imaging solution for image-guided radiotherapy.

Main Methods:

  • Proposed an unpaired MVCT denoising network using a coupled generative adversarial network framework (DeCoGAN).
  • Employed an encoder to enforce a shared latent space constraint for image reconstruction.
  • Utilized adversarial training and leveraged marginal distributions for effective denoising.

Main Results:

  • DeCoGAN demonstrated superior performance in preserving image details and visual perception compared to BM3D, RED-CNN, WGAN-VGG, and CycleGAN.
  • Achieved the highest peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measurement (SSIM) values.
  • Effectively removed noise while retaining essential structural features in MVCT images.

Conclusions:

  • The DeCoGAN method offers remarkable MVCT denoising capabilities.
  • This deep learning approach shows significant promise for improving image quality in radiation therapy.
  • DeCoGAN can enhance the precision and reliability of image-guided radiotherapy.