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Proj2Proj: self-supervised low-dose CT reconstruction.

Mehmet Ozan Unal1, Metin Ertas2, Isa Yildirim1

  • 1Department of Electronics and Communication Engineering, Istanbul Technical University, Istanbul, Turkey.

Peerj. Computer Science
|March 4, 2024
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Summary

This study introduces a novel self-supervised deep learning method for low-dose Computed Tomography (CT) imaging. The approach reconstructs high-quality CT images without requiring labeled data, outperforming existing methods.

Keywords:
Deep learningImage reconstructionLow-dose CTSelf-supervised learning

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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence

Background:

  • Ionizing radiation in Computed Tomography (CT) poses significant risks.
  • Reducing radiation dose while maintaining image quality is a critical challenge.
  • Deep learning offers promising solutions for low-dose CT reconstruction.

Purpose of the Study:

  • To develop a self-supervised deep learning method for low-dose CT reconstruction.
  • To eliminate the need for labeled datasets in training CT reconstruction models.
  • To improve image quality in low-dose CT imaging.

Main Methods:

  • A self-supervision principle was applied in the projection domain.
  • Low-dose projections were used as training targets for a denoiser neural network.
  • The neural network parameters were optimized via self-supervised training.

Main Results:

  • The proposed method demonstrated superior performance compared to traditional and compressed sensing methods.
  • It outperformed other deep learning-based unsupervised methods in low-dose CT reconstruction.
  • Reconstruction quality was comparable to established supervised deep learning methods.

Conclusions:

  • Self-supervised learning in the projection domain is effective for low-dose CT reconstruction.
  • This approach significantly reduces reliance on labeled data for CT imaging.
  • The method offers a viable alternative for high-quality, low-radiation CT scans.