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Low-dose CT denoising with a high-level feature refinement and dynamic convolution network.

Sihan Yang1,2, Qiang Pu3, Chunting Lei1,2

  • 1College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, China.

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|December 21, 2022
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Summary
This summary is machine-generated.

This study introduces a deep learning (DL) denoising network that enhances low-dose CT (LDCT) images by refining high-level features and using dynamic convolutions. The novel approach significantly improves image quality for better medical diagnosis.

Keywords:
deep learning (DL)dynamic convolutionimage denoisinglow-dose CT (LDCT)

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Concerns exist regarding health risks associated with radiation from computed tomography (CT) scans.
  • Low-dose CT (LDCT) images often suffer from noise and artifacts, potentially impacting diagnostic accuracy.

Purpose of the Study:

  • To address limitations of existing deep learning (DL) denoising methods in fully exploiting hierarchical features.
  • To propose an LDCT denoising network that utilizes high-level feature refinement and multiscale dynamic convolution for improved performance.

Main Methods:

  • A dual network structure comprising a Feature Refinement Network (FRN) and a Dynamic Perception Network (DPN).
  • FRN extracts multi-level features using residual dense connections; DPN fuses features with local channel attention (LCA).
  • Dynamic dilated convolution (DDC) with multibranch, multiscale receptive fields enhances network expressivity.

Main Results:

  • The proposed method achieved optimal and statistically significant denoising performance on the Mayo dataset (PSNR: 46.35 dB, SSIM: 0.9844).
  • Significant improvements in PSNR and SSIM were observed compared to standard LDCT images.
  • Effective performance was validated on external datasets, including ultra-low-dose chest CT (10% dose) and head CT images.

Conclusions:

  • A novel DL-based LDCT denoising algorithm is presented, leveraging high-level features and multiscale dynamic convolution.
  • The method demonstrates efficient noise suppression and detail preservation capabilities.
  • This technique offers a valuable tool for improving the diagnostic utility of LDCT scans.