Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction
This work addresses the need for faster MRI reconstruction for medical imaging applications, representing an incremental improvement by better utilizing edge information.
The paper tackles the problem of long MRI scan times by proposing a joint edge optimization model that incorporates edge priors into a deep unfolding network for accelerated MRI reconstruction, demonstrating superior performance over other methods across various sampling schemes and factors.
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.