Attention mechanisms for sharing low-rank, image and k-space information during MR image reconstruction

Cardiac CINE MR imaging requires long acquisitions under multiple breath-holds. With the development of deep learning-based reconstruction methods, the acceleration rate and reconstructed image quality have been increased. However, existing methods face several shortcomings, such as limited information-sharing across domains and generalizability which may restrict their clinical adoption. To address these issues, we propose A-LIKNet which incorporates attention mechanisms and maximizes information sharing between low-rank, image, and k-space in an interleaved architecture. Results indicate that the proposed A-LIKNet outperforms other methods for up to 24x accelerated acquisitions within a single breath-hold.