  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.
