MAPANet: A Multi-Scale Attention-Guided Progressive Aggregation Network for Multi-Contrast MRI Super-Resolution

Abstract

Multi-contrast magnetic resonance imaging (MRI) super-resolution (SR), which utilizes complementary information from different contrast images to reconstruct the target images, can provide rich information for quantitative image analysis and accurate medical diagnosis. However, the current mainstream methods are failed in exploiting multi-scale features or global information for data representation, leading to poor outcomes. To address these limitations, we propose a multi-scale attention-guided progressive aggregation network (MAPANet) to progressively restore the target contrast MR images from the corresponding low resolution (LR) observations with the assistance of auxiliary contrast images. Specifically, the proposed MAPANet is composed of several stacked dual-branch aggregation (DBA) blocks, each of which consists of two parallel modules: the multi-scale attention module (MSAM) and the reference feature extraction module (RFEM). The former aims to utilize multi-scale and appropriate non-local information to facilitate the SR reconstruction, while the latter is designed to extract the complementary information from auxiliary contrast images to assist in restoring edge structures and details for target contrast images. Extensive experiments on the public datasets demonstrate that the proposed MAPANet outperforms several state-of-the-art multi-contrast SR methods.