Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction

被引:0
|
作者
Zhao, Yidong [1 ]
Zhang, Yi [1 ]
Tao, Qian [1 ]
机构
[1] Delft Univ Technol, Dept Imaging Phys, Delft, Netherlands
来源
STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. REGULAR AND CMRXRECON CHALLENGE PAPERS, STACOM 2023 | 2024年 / 14507卷
关键词
Caridac MRI; Quantitative mapping; Relaxometry; Image reconstruction; NETWORK; MRI;
D O I
10.1007/978-3-031-52448-6_33
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页码:349 / 358
页数:10
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