Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
Authors: 
Yidong Zhao, Yi Zhang, Qian TaoAuthors Info & Claims
Yidong Zhao, Yi Zhang, Qian TaoAuthors Info & ClaimsPages 349 - 358
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.
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Information
Published In
Oct 2023
506 pages
ISBN:978-3-031-52447-9
DOI:10.1007/978-3-031-52448-6
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 02 February 2024
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