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Ves-GAN: Unsupervised Vessel-Targeted Low-Dose Coronary Computed Tomography Angiography Denoising Framework.

Xinyuan Xiang1, Jiayue Li2, Yan Yi3

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This study introduces Ves-GAN, an unsupervised denoising framework for low-dose coronary CT angiography (LDCTA). Ves-GAN effectively reduces noise while preserving critical vascular structures, enhancing diagnostic accuracy in cardiovascular imaging.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Diagnostics

Background:

  • Low-dose coronary CT angiography (LDCTA) reduces radiation exposure but introduces noise and blurring, impacting diagnostic accuracy.
  • Existing denoising methods, particularly supervised deep learning, require paired data and make assumptions about noise characteristics.
  • Unsupervised denoising models struggle to preserve fine vascular structures, limiting their clinical utility.

Purpose of the Study:

  • To develop an unsupervised denoising framework for LDCTA that effectively reduces noise while preserving vascular integrity.
  • To address the limitations of existing denoising methods in handling noise and maintaining structural details in LDCTA.
  • To improve the reliability and quality of cardiovascular diagnosis through enhanced image quality.

Main Methods:

  • Development of Ves-GAN, a novel unsupervised denoising framework utilizing a high-frequency-aware data augmentation strategy.
  • Incorporation of a high-frequency squeeze-and-excitation module in the generator to enhance sensitivity to fine vascular features.
  • Introduction of a vessel-consistency loss function to ensure structural integrity preservation during the denoising process.

Main Results:

  • Ves-GAN demonstrated an average improvement of 7.5% in peak signal-to-noise ratio and 10.2% in structural similarity index compared to existing unsupervised models.
  • Clinical validation with 50 CT scans showed substantial enhancements in vascular clarity and lesion visibility, as noted by 3 radiologists.
  • The framework achieved superior noise reduction and preservation of vascular details.

Conclusions:

  • Ves-GAN significantly outperforms current unsupervised denoising models in preserving vascular details and reducing noise in LDCTA.
  • The proposed framework enhances the reliability of clinical evaluations and improves the overall quality of cardiovascular diagnosis.
  • Ves-GAN offers a promising solution for improving LDCTA image quality for better clinical decision-making.