TY - GEN
T1 - Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
AU - Zhao, Yidong
AU - Zhang, Yi
AU - Tao, Qian
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2024
Y1 - 2024
N2 - Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as the regularization term. In quantitative MRI, the physical model of nuclear magnetic resonance relaxometry is known, providing additional prior knowledge for image reconstruction. However, traditional reconstruction networks are limited to learning the spatial domain prior knowledge, ignoring the relaxometry prior. Therefore, we propose a relaxometry-guided quantitative MRI reconstruction framework to learn the spatial prior from data and the relaxometry prior from MRI physics. Additionally, we also evaluated the performance of two popular reconstruction backbones, namely, recurrent variational networks (RVN) and variational networks (VN) with U-Net. Experiments demonstrate that the proposed method achieves highly promising results in quantitative MRI reconstruction.
AB - Deep learning-based methods have achieved prestigious performance for magnetic resonance imaging (MRI) reconstruction, enabling fast imaging for many clinical applications. Previous methods employ convolutional networks to learn the image prior as the regularization term. In quantitative MRI, the physical model of nuclear magnetic resonance relaxometry is known, providing additional prior knowledge for image reconstruction. However, traditional reconstruction networks are limited to learning the spatial domain prior knowledge, ignoring the relaxometry prior. Therefore, we propose a relaxometry-guided quantitative MRI reconstruction framework to learn the spatial prior from data and the relaxometry prior from MRI physics. Additionally, we also evaluated the performance of two popular reconstruction backbones, namely, recurrent variational networks (RVN) and variational networks (VN) with U-Net. Experiments demonstrate that the proposed method achieves highly promising results in quantitative MRI reconstruction.
KW - Caridac MRI
KW - Image reconstruction
KW - Quantitative mapping
KW - Relaxometry
UR - http://www.scopus.com/inward/record.url?scp=85186697320&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-52448-6_33
DO - 10.1007/978-3-031-52448-6_33
M3 - Conference contribution
AN - SCOPUS:85186697320
SN - 978-3-031-52447-9
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 349
EP - 358
BT - Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers - 14th International Workshop, STACOM 2023, Held in Conjunction with MICCAI 2023, Revised Selected Papers
A2 - Camara, Oscar
A2 - Puyol-Antón, Esther
A2 - Suinesiaputra, Avan
A2 - Young, Alistair
A2 - Sermesant, Maxime
A2 - Tao, Qian
A2 - Wang, Chengyan
PB - Springer
T2 - 14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023 held in Conjunction with MICCAI 2023
Y2 - 12 October 2023 through 12 October 2023
ER -