Static MRI Reconstruction Based on K-space and Image-domain Information

In order to accelerate the imaging process of MRI (Magnetic Resonance Imaging), CS-MRI (Compressed Sensing Magnetic Resonance Imaging) utilizes the prior information of MR images and reconstructs under-sampled MR images by solving an optimization problem through an iterative algorithm, and it is time consuming. With the emergence of deep learning technology, more and more optimization problems can be solved through well-designed networks and large amounts of training data. Therefore, DL-Based (Deep Learning-Based) methods have attracted much attention. However, most of the current work simply treats MRI reconstruction as an image-to-image task, so there is still a lot of room for improvement in how to better integrate MRI information. This paper proposes a new DL-based method, which reconstructs the MR images in both k-space and image domain using several sub-networks and fuses information among different sub-networks. We also used down-sampling and up-sampling pairs to reduce the computational complexity. Our proposed architecture obtained promising experimental results on Calgary-Campinas Brain MRI datasets.