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Electrical Engineering and Systems Science > Image and Video Processing
Title: Relaxometry Guided Quantitative Cardiac Magnetic Resonance Image Reconstruction
(Submitted on 1 Mar 2024 (v1), last revised 1 May 2024 (this version, v3))
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.
Submission history
From: Yidong Zhao [view email][v1] Fri, 1 Mar 2024 14:18:00 GMT (1407kb,D)
[v2] Tue, 30 Apr 2024 06:01:22 GMT (1408kb,D)
[v3] Wed, 1 May 2024 06:50:59 GMT (1408kb,D)
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