Ssiqa: Multi-task learning for non-reference ct image quality assessment with self-supervised noise level prediction

Abdullah Al Zubaer Imran, Debashish Pal, Bhavik Patel, Adam Wang

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

8 Scopus citations

Abstract

Reduction of CT radiation dose is important due to the potential effects on patients. But lowering dose incurs degradation in the reconstructed image quality, furthering compromise in the diagnostic and image-based analyses performance. Considering the patient health risks, high quality reference images cannot be easily obtained, making the assessment challenging. Therefore, automatic no-reference image quality assessment is desirable. Leveraging an innovative self-supervised regularization in a convolutional neural network, we propose a novel, fully automated, no-reference CT image quantification method namely self-supervised image quality assessment (SSIQA). Extensive experimentation via in-domain (abdomen CT) and cross-domain (chest CT) evaluations demonstrates SSIQA is accurate in quantifying CT image quality, generalized across the scan types, and consistent with the established metrics and different relative dose levels.

Original languageEnglish
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
Pages1962-1965
Number of pages4
ISBN (Electronic)9781665412469
DOIs
StatePublished - Apr 13 2021
Event18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France
Duration: Apr 13 2021Apr 16 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
Period4/13/214/16/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • CT
  • G-SSIM
  • Image quality assessment
  • Radiation dose
  • Self-supervised learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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