Comparison of CT noise reduction performances with deep learning-based, conventional, and combined denoising algorithms

Zsolt Adam Balogh, Benedek Janos Kis

Research output: Contribution to journalArticlepeer-review

Abstract

Conventional noise reduction algorithms have been used in image processing for a very long time, but recently, deep learning-based algorithms have been shown to significantly reduce the noise in CT images. In this paper, a comparison of CT noise reduction of a deep learning-based, a conventional, and their combined denoising algorithms is presented. A conventional adaptive 3D bilateral filter and a 2D deep learning-based noise reduction algorithm and a combination of these are compared. For comparison, we used the noise power spectrum and the task transfer function which were measured on original CT images and the effective dose saving factors were also calculated. The noise reduction effect, the noise power spectrum and the task-transfer function are studied using Catphan 600 phantom and 26 clinical cases with more than 100,000 images. We also show that the effect of noise reduction of a 2D deep learning-based algorithm can be further enhanced by using conventional 3D spatial noise reduction algorithms.

Original languageEnglish
Article number103897
JournalMedical Engineering and Physics
Volume109
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Computed tomography
  • Deep learning
  • Noise reduction

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering

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