DIIK-Net: A full-resolution cross-domain deep interaction convolutional neural network for MR image reconstruction

Abstract

Acquiring incomplete k-space matrices is an effective way to accelerate Magnetic Resonance Imaging (MRI). It is an important and challenging task to accurately reconstruct images from such under-sampled k-space matrices. On the one hand, neither image-domain oriented nor frequency-domain oriented deep Convolutional Neural Networks can simultaneously employ both frequency features and spatial features for cooperatively improving reconstruction accuracy. On the other hand, existing dual-domain reconstruction methods adopt heavy encoder-decoder frameworks, resulting in low efficiency and information loss in the process of pooling. To deal with these problems, in this paper, we propose a full-resolution dual-domain reconstruction network, called DIIK-Net. The DIIK-Net consists of a full-resolution frequency-domain branch, a full-resolution image-domain branch, and cross-domain interaction modules between the two branches. The first novelty of the proposed method is that the features of each block of frequency-domain branch are extracted by 1×1 filters, which reduces computational cost and captures rich contextual information. Due to the fact that an element in frequency domain conveys information of the whole image, 1×1 convolutional blocks are able to extract large contextual information with the interaction of image domain. The second novelty is that the image-domain branch consists of a very small number of 3×3 convolutional blocks and each block has very large field of perception due to integration of frequency domain. The third novelty lies in the simple and effective cross-domain interaction module. Experimental results on the challenging fastMRI dataset demonstrate that the proposed method is capable of achieving higher reconstruction accuracy with a few number of parameters.