Super-resolution method for MR images based on multi-resolution CNN

Abstract

High-Resolution (HR) Magnetic Resonance Images (MRI) can help physician diagnosis lesion more effectively. However, in practice, it is difficult to obtain HR-MRI due to equipment limitations, scanning time or patient comfort. Fortunately, with the development of information technology, HR-MRI could be obtained by some image post-processing approaches. This paper presents a novel HR-MRI generation approach based on convolution neural network (CNN) and multi-resolution analysis, which intends to improve the resolution of the low-resolution (LR) T2-weighted (T2w) images with the prior information provided by HR T1-weighted (T1w) reference images based on the inherent correlation between the two. Specifically, a novel 3D multi-resolution analysis multi-modality SR reconstruction network for MRI is built, which takes full advantage of structural similarity between the modalities. Different from other networks, the proposed reconstruction network fully combines the idea of multi-resolution analysis. Firstly, we built an associative memory network between HR-T1w and HR-T2w, which learns high-frequency feature from HR-T1w for HR-T2w at different scales. Subsequently, a progressive reconstruction approach is presented to combine the multi-scale high-frequency feature extracted by the first step from HR-T1w with the down-sampling 4x LR-T2w feature to generate 2× and 4× HR-T2w images, respectively. The experimental results on two real MRI dataset show the effectiveness of the proposed 3D SR reconstruction network, which achieves improved reconstruction performance compared with other single- and multi-modal networks. Even with 4x magnification, our method can effectively restore the edge details of the image.