Cardiac Cine Magnetic Resonance Imaging (MRI) provides an accurate assessment of heart morphology and function in clinical practice. However, MRI requires long acquisition times, with recent deep learning-based methods showing great promise to accelerate imaging and enhance reconstruction quality. Existing networks exhibit some common limitations that constrain further acceleration possibilities, including single-domain learning, reliance on a single regularization term, and equal feature contribution. To address these limitations, we propose to embed information from multiple domains, including low-rank, image, and k-space, in a novel deep learning network for MRI reconstruction, which we denote as A-LIKNet. A-LIKNet adopts a parallel-branch structure, enabling independent learning in the k-space and image domain. Coupled information sharing layers realize the information exchange between domains. Furthermore, we introduce attention mechanisms into the network to assign greater weights to more critical coils or important temporal frames. Training and testing were conducted on an in-house dataset, including 91 cardiovascular patients and 38 healthy subjects scanned with 2D cardiac Cine using retrospective undersampling. Additionally, we evaluated A-LIKNet on the real-time 8x prospectively undersampled data from the OCMR dataset. The results demonstrate that our proposed A-LIKNet outperforms existing methods and provides high-quality reconstructions. The network can effectively reconstruct highly retrospectively undersampled dynamic MR images up to 24x accelerations, indicating its potential for single breath-hold imaging.

提出了一种新颖的深度学习网络A-LIKNet，它采用平行分支结构，在多个领域（包括低秩、图像和k空间）中嵌入信息，通过耦合的信息共享层实现领域之间的信息交换，并引入注意机制来赋予更重要的线圈或时间帧更大的权重，实现了高速重建高质量的动态磁共振图像。

Attention Incorporated Network for Sharing Low-rank, Image and K-space
  Information during MR Image Reconstruction to Achieve Single Breath-hold
  Cardiac Cine Imaging

应用注意力机制整合的网络，在MR 图像重建过程中共享低秩、图像和k-空间信息，实现单次呼吸保持心脏电影成像