The core problem of Magnetic Resonance Imaging (MRI) is the trade off between acceleration and image quality. Image reconstruction and super-resolution are two crucial techniques in Magnetic Resonance Imaging (MRI). Current methods are designed to perform these tasks separately, ignoring the correlations between them. In this work, we propose an end-to-end task transformer network (T$^2$Net) for joint MRI reconstruction and super-resolution, which allows representations and feature transmission to be shared between multiple task to achieve higher-quality, super-resolved and motion-artifacts-free images from highly undersampled and degenerated MRI data. Our framework combines both reconstruction and super-resolution, divided into two sub-branches, whose features are expressed as queries and keys. Specifically, we encourage joint feature learning between the two tasks, thereby transferring accurate task information. We first use two separate CNN branches to extract task-specific features. Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks. Experimental results show that our multi-task model significantly outperforms advanced sequential methods, both quantitatively and qualitatively.

翻译：磁共振成像(MRI)的核心问题是加速度和图像质量之间的交换。图像重建与超分辨率是磁共振成像(MRI)中的两项关键技术。目前设计的方法是为了分别执行这些任务,而忽略它们之间的相互关系。在这项工作中,我们建议建立一个端到端任务变压器网络(T$$2$Net),用于联合磁共振成像和超级分辨率的重建与超分辨率,使多个任务之间能够分享演示和特征传输,从而实现质量更高、超级溶解和无运动艺术成像来自高压低和退化的磁共振成像的数据。我们的框架将重建与超分辨率结合起来,将其分为两个子分机,其特征以查询和钥匙的形式表达。具体地说,我们鼓励在两个任务之间联合学习特征,从而转移准确的任务信息。我们首先使用两个CNN分支来提取任务的具体特征。然后设计一个任务变异模块来嵌入和综合两个任务之间的关联性。实验结果显示,我们的多式模型在质量和高级序列方法上都明显超越了我们的多式模型。