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。

联合边缘优化深度展开网络用于加速磁共振成像重建

Joint Edge Optimization Deep Unfolding Network for Accelerated MRI  Reconstruction

Most existing MRI reconstruction methods perform tar-geted reconstruction of
the entire MR image without tak-ing specific tissue regions into consideration.
This may fail to emphasize the reconstruction accuracy on im-portant tissues
for diagnosis. In this study, leveraging a combination of the properties of
k-space data and the diffusion process, our novel scheme focuses on mining the
multi-frequency prior with different strategies to pre-serve fine texture
details in the reconstructed image. In addition, a diffusion process can
converge more quickly if its target distribution closely resembles the noise
distri-bution in the process. This can be accomplished through various
high-frequency prior extractors. The finding further solidifies the
effectiveness of the score-based gen-erative model. On top of all the
advantages, our method improves the accuracy of MRI reconstruction and
accel-erates sampling process. Experimental results verify that the proposed
method successfully obtains more accurate reconstruction and outperforms
state-of-the-art methods.

通过结合 k - 空间数据和扩散过程的特性，本研究提出了一种新的方案，利用不同策略挖掘多频先验，并保留重建图像中细节纹理，同时通过高频先验提取器更快地收敛扩散过程，从而提高了 MRI 重建的准确性和采样过程的加速性。实验结果验证了该方法成功地获得了更准确的重建并超越了现有方法。