Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however
it has the limitation of long scanning time. Though previous model-based and
learning-based MRI reconstruction methods have shown promising performance,
most of them have not fully utilized the edge prior of MR images, and there is
still much room for improvement. In this paper, we build a joint edge
optimization model that not only incorporates individual regularizers specific
to both the MR image and the edges, but also enforces a co-regularizer to
effectively establish a stronger correlation between them. Specifically, the
edge information is defined through a non-edge probability map to guide the
image reconstruction during the optimization process. Meanwhile, the
regularizers pertaining to images and edges are incorporated into a deep
unfolding network to automatically learn their respective inherent a-priori
information.Numerical experiments, consisting of multi-coil and single-coil MRI
data with different sampling schemes at a variety of sampling factors,
demonstrate that the proposed method outperforms other compared methods.

通过建立联合边缘优化模型，结合 MRI 图像和边缘的特定正则化器以及协同正则化器，利用非边缘概率图引导图像重建，借助深度展开网络自动学习图像和边缘的先验信息，在多种采样因子的多线圈和单线圈 MRI 数据的数值实验中，表明所提出的方法优于其他方法。