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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Self-supervised learning for CT image denoising and reconstruction: a review.

Kihwan Choi1

  • 1Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul, 01811 Republic of Korea.

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This review explores self-supervised learning for computed tomography (CT) image denoising and reconstruction. These methods enable learning from data without requiring paired clean and noisy images, advancing medical imaging.

Keywords:
Computed tomography (CT)Dose reductionImage denoisingImage reconstructionSelf-supervised learning

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Deep learning is a key technology in medical imaging and computer vision.
  • Self-supervised learning (SSL) is gaining prominence for its ability to learn from unlabeled data.
  • CT imaging is crucial for diagnosis, but image quality can be compromised by noise and reconstruction artifacts.

Purpose of the Study:

  • To review self-supervised learning methods applied to CT image denoising and reconstruction.
  • To examine the advancements and evolution of deep learning in CT image processing.
  • To provide insights into the theoretical and methodological progress of SSL in this domain.

Main Methods:

  • Review of existing literature on self-supervised learning for CT.
  • Analysis of deep learning techniques for image denoising and reconstruction.
  • Focus on the theoretical underpinnings and methodological developments of SSL.

Main Results:

  • Self-supervised learning offers a promising avenue for CT image denoising and reconstruction without paired data.
  • Deep learning has significantly impacted CT image processing capabilities.
  • SSL methods are evolving, showing potential for improved image quality and reduced radiation dose.

Conclusions:

  • Self-supervised learning represents a significant advancement in CT image denoising and reconstruction.
  • Continued research in SSL is expected to further enhance medical imaging quality and applications.
  • SSL techniques are crucial for overcoming limitations in traditional CT imaging approaches.