Enhancing Brain MRI Super-Resolution Through Multi-Slice Aware Matching and Fusion

Abstract

In clinical diagnosis, magnetic resonance imaging (MRI) allows different contrast images to be obtained. High-resolution (HR) MRI presents fine anatomical structures, which is important for improving the efficiency of expert diagnosis and realising smart healthcare. However, due to the cost of scanning equipment and the time required for scanning, obtaining an HR brain MRI is quite challenging. Therefore, to improve the quality of images, reference-based super-resolution technology has come into existence. Nevertheless, the existing methods still have some drawbacks: (1) The advantages of different contrast images are not fully utilised. (2) The slice-by-slice scanning nature of magnetic resonance imaging is not considered. (3) The ability to capture contextual information and to match and fuse multi-scale, multi-contrast features is lacking. In this paper, we propose the multi-slice aware matching and fusion (MSAMF) network, which makes full use of multi-slice reference images information by introducing a multi-slice aware module and multi-scale matching strategy to capture corresponding contextual information in reference features at other scales. To further integrate matching features, a multi-scale fusion mechanism is also designed to progressively fuse multi-scale matching features, thereby generating more detailed super-resolution images. The experimental results support the benefits of our network in enhancing the quality of brain MRI reconstruction.