Super-resolution (SR) plays a crucial role in improving the image quality of magnetic resonance imaging (MRI). MRI produces multi-contrast images and can provide a clear display of soft tissues. However, current super-resolution methods only employ a single contrast, or use a simple multi-contrast fusion mechanism, ignoring the rich relations among different contrasts, which are valuable for improving SR. In this work, we propose a multi-stage integration network (i.e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR. In particular, our MINet first learns a hierarchical feature representation from multiple convolutional stages for each of different-contrast image. Subsequently, we introduce a multi-stage integration module to mine the comprehensive relations between the representations of the multi-contrast images. Specifically, the module matches each representation with all other features, which are integrated in terms of their similarities to obtain an enriched representation. Extensive experiments on fastMRI and real-world clinical datasets demonstrate that 1) our MINet outperforms state-of-the-art multi-contrast SR methods in terms of various metrics and 2) our multi-stage integration module is able to excavate complex interactions among multi-contrast features at different stages, leading to improved target-image quality.
翻译:超分辨率(SR)在提高磁共振成像(MRI)的图像质量方面发挥着关键作用。磁共振生成多调图像,可以清晰显示软组织。然而,目前的超级分辨率方法只使用单一对比,或使用简单的多调融合机制,忽视不同对比之间的丰富关系,而这些关系对于改进磁共振很有价值。在这项工作中,我们提议为多调的磁共振成像(MRI)建立一个多阶段集成网络(即MINet),明确模拟不同阶段多调合图像之间的依赖性,以指导图像SR。特别是,我们的Miniet首先从多个相联阶段学习一个等级特征代表,或者使用一个简单的多调合融合机制,忽略不同对比关系之间的丰富关系,对于改进磁共振成图像的表达方式,模块与所有其他特征相匹配,这些特征在获得更丰富的代表性方面相互融合。在快速和真实世界的图像集成图象特征中,我们Miniet首先从多个相级级级级级级级级的级级特征,然后是多级的多级集制。