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.

翻译：基于深度学习的方法在磁共振成像（MRI）重建中取得了卓越性能，实现了众多临床应用的快速成像。现有方法采用卷积网络学习图像先验作为正则化项。在定量MRI中，核磁共振弛豫测量的物理模型已知，可为图像重建提供额外的先验知识。然而，传统重建网络局限于学习空间域先验知识，忽略了弛豫测量先验。为此，我们提出一种弛豫测量引导的定量MRI重建框架，用于从数据中学习空间先验，并从MRI物理中学习弛豫测量先验。此外，我们还评估了两种主流重建骨干网络——循环变分网络（RVN）和基于U-Net的变分网络（VN）的性能。实验结果表明，所提方法在定量MRI重建中取得了极具前景的结果。