Title
Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
Author
Zhao, Y. (TU Delft ImPhys/Tao group)
Zhang, Y. (TU Delft Pavement Engineering)
Tao, Q. (TU Delft ImPhys/Tao group)
Contributor
Camara, Oscar (editor)
Puyol-Antón, Esther (editor)
Suinesiaputra, Avan (editor)
Young, Alistair (editor)
Sermesant, Maxime (editor)
Tao, Qian (editor)
Wang, Chengyan (editor)
Date
2024
Abstract
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.
Subject
Caridac MRI
Image reconstruction
Quantitative mapping
Relaxometry
To reference this document use:
http://resolver.tudelft.nl/uuid:fa00f697-cf80-4fd9-945e-77080f85ea51
DOI
10.1007/978-3-031-52448-633
Publisher
Springer
Embargo date
2024-08-02
ISBN
978-3-031-52447-9
Source
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
Event
14th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2023 held in Conjunction with MICCAI 2023, 2023-10-12, Vancouver, Canada
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 14507 LNCS
Bibliographical note
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.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2024 Y. Zhao, Y. Zhang, Q. Tao