TY - GEN
T1 - SS-JIRCS
T2 - 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
AU - Gan, Weijie
AU - Hu, Yuyang
AU - Eldeniz, Cihat
AU - Liu, Jiaming
AU - Chen, Yasheng
AU - An, Hongyu
AU - Kamilov, Ulugbek S.
N1 - Funding Information:
Research reported in this publication was supported by the NSF CAREER award under CCF-2043134 and the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH).
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Parallel magnetic resonance imaging (MRI) is a widely-used technique that accelerates data collection by making use of the spatial encoding provided by multiple receiver coils. A key issue in parallel MRI is the estimation of coil sensitivity maps (CSMs) that are used for reconstructing a single high-quality image. This paper addresses this issue by developing SS-JIRCS, a new self-supervised model-based deep-learning (DL) method for image reconstruction that is equipped with automated CSM calibration. Our deep network consists of three types of modules: data-consistency, regularization, and CSM calibration. Unlike traditional supervised DL methods, these modules are directly trained on undersampled and noisy k-space data rather than on fully sampled high-quality ground truth. We present empirical results on simulated data that show the potential of the proposed method for achieving better performance than several baseline methods.
AB - Parallel magnetic resonance imaging (MRI) is a widely-used technique that accelerates data collection by making use of the spatial encoding provided by multiple receiver coils. A key issue in parallel MRI is the estimation of coil sensitivity maps (CSMs) that are used for reconstructing a single high-quality image. This paper addresses this issue by developing SS-JIRCS, a new self-supervised model-based deep-learning (DL) method for image reconstruction that is equipped with automated CSM calibration. Our deep network consists of three types of modules: data-consistency, regularization, and CSM calibration. Unlike traditional supervised DL methods, these modules are directly trained on undersampled and noisy k-space data rather than on fully sampled high-quality ground truth. We present empirical results on simulated data that show the potential of the proposed method for achieving better performance than several baseline methods.
UR - http://www.scopus.com/inward/record.url?scp=85123052087&partnerID=8YFLogxK
U2 - 10.1109/ICCVW54120.2021.00450
DO - 10.1109/ICCVW54120.2021.00450
M3 - Conference contribution
AN - SCOPUS:85123052087
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 4031
EP - 4039
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 October 2021 through 17 October 2021
ER -