Self-supervised learning for CT image denoising and reconstruction: a review

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Abstract

This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.

Original languageEnglish
Pages (from-to)1207-1220
Number of pages14
JournalBiomedical Engineering Letters
Volume14
Issue number6
DOIs
StatePublished - Nov 2024

Keywords

  • Computed tomography (CT)
  • Dose reduction
  • Image denoising
  • Image reconstruction
  • Self-supervised learning

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