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.

翻译：磁共振成像（MRI）是一种广泛使用的成像技术，但其扫描时间较长。尽管以往的基于模型和基于学习的磁共振成像重建方法已展现出良好的性能，但大多数方法未能充分利用磁共振图像中的边缘先验信息，仍有较大的改进空间。本文构建了一个联合边缘优化模型，该模型不仅分别考虑了针对磁共振图像和边缘的独立正则化项，还引入了一个协同正则化项以有效增强两者之间的关联性。具体而言，边缘信息通过非边缘概率图定义，在优化过程中指导图像重建。同时，与图像和边缘相关的正则化项被嵌入到深度展开网络中，以自动学习它们各自固有的先验知识。数值实验采用了不同采样方案下多种采样因子的多线圈和单线圈磁共振数据，结果表明所提方法优于其他对比方法。