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 language | English (US) |
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Title of host publication | 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021 |
Publisher | IEEE Computer Society |
Pages | 1201-1205 |
Number of pages | 5 |
ISBN (Electronic) | 9781665412469 |
DOIs | |
State | Published - Apr 13 2021 |
Event | 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France Duration: Apr 13 2021 → Apr 16 2021 |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2021-April |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Conference
Conference | 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 |
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Country/Territory | France |
City | Nice |
Period | 4/13/21 → 4/16/21 |
Funding
This work is supported in part by the Intramural Research Program of the National Cancer Institute, National Institutes of Health. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
Keywords
- Generative adversarial network
- Magnetic resonance imaging
- No-reference image quality assessment
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging