No-reference image quality assessment of T2-weighted magnetic resonance images in prostate cancer patients

Samira Masoudi, Stephanie Harmon, Sherif Mehralivand, Nathan Lay, Ulas Bagci, Bradford J. Wood, Peter A. Pinto, Peter Choyke, Baris Turkbey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

No-reference image quality assessment in magnetic resonance (MR) imaging is a challenging task due to the variable nature of these images and lack of standard quantification methods, which makes the interpretation to be almost always subjective. In this study, we propose an architecture where we: (i) extended the no-reference image quality assessment problem of MRI into a full-reference image quality assessment using unpaired generative adversarial network (GAN) and (ii) employed a weaklysupervised trained deep classifier to determine the quality of MR images by comparing each image with its synthetic higher quality reference image. Using this approach, we achieved 11.28% improvement in the accuracy of our MR image quality assessment algorithm on an independent data test with FPR in detecting low quality images, reduced from 13% to 9.6%.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PublisherIEEE Computer Society
Pages1201-1205
Number of pages5
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

Keywords

  • Generative adversarial network
  • Magnetic resonance imaging
  • No-reference image quality assessment

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'No-reference image quality assessment of T2-weighted magnetic resonance images in prostate cancer patients'. Together they form a unique fingerprint.

Cite this