TY - GEN
T1 - No-reference image quality assessment of T2-weighted magnetic resonance images in prostate cancer patients
AU - Masoudi, Samira
AU - Harmon, Stephanie
AU - Mehralivand, Sherif
AU - Lay, Nathan
AU - Bagci, Ulas
AU - Wood, Bradford J.
AU - Pinto, Peter A.
AU - Choyke, Peter
AU - Turkbey, Baris
N1 - Funding Information:
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.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/13
Y1 - 2021/4/13
N2 - 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%.
AB - 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%.
KW - Generative adversarial network
KW - Magnetic resonance imaging
KW - No-reference image quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85107235940&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107235940&partnerID=8YFLogxK
U2 - 10.1109/ISBI48211.2021.9434027
DO - 10.1109/ISBI48211.2021.9434027
M3 - Conference contribution
AN - SCOPUS:85107235940
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 1201
EP - 1205
BT - 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021
PB - IEEE Computer Society
T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021
Y2 - 13 April 2021 through 16 April 2021
ER -
