No-reference denoising of low-dose ct projections

Elvira Zainulina, Alexey Chernyavskiy, Dmitry V. Dylov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Low-dose computed tomography (LDCT) became a clear trend in radiology with an aspiration to refrain from delivering excessive X-ray radiation to the patients. The reduction of the radiation dose decreases the risks to the patients but raises the noise level, affecting the quality of the images and their ultimate diagnostic value. One mitigation option is to consider pairs of low-dose and high-dose CT projections to train a denoising model using deep learning algorithms; however, such pairs are rarely available in practice. In this paper, we present a new self-supervised method for CT denoising. Unlike existing self-supervised approaches, the proposed method requires only noisy CT projections and exploits the connections between adjacent images. The experiments carried out on an LDCT dataset demonstrate that our method is almost as accurate as the supervised approach, while also outperforming several modern self-supervised denoising methods.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages77-81
Number of pages5
ISBN (Electronic)9781665412469
DOIs
Publication statusPublished - 13 Apr 2021
Externally publishedYes
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: 13 Apr 202116 Apr 2021

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2021-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Country/TerritoryFrance
CityNice
Period13/04/2116/04/21

Keywords

  • Blind denoising
  • Computed tomography (CT)
  • Convolutional long short-term memory (ConvLSTM)
  • Convolutional neural networks (CNN)
  • CT projections
  • Self-supervised learning

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