Abstract
Magnetic resonance imaging (MRI) plays a crucial role in clinical diagnosis, yet traditional MR image acquisition often requires a prolonged duration, potentially causing patient discomfort and image artifacts. Faster and more accurate image reconstruction may alleviate patient discomfort during MRI examinations and enhance diagnostic accuracy and efficiency. In recent years, significant advancements in deep learning technology offer promise for improving MR image quality and accelerating acquisition. Addressing the demand for cardiac cine MRI reconstruction, we propose KGMgT, a novel MRI reconstruction network based on knowledge-guided approaches. The KGMgT model leverages adaptive spatiotemporal attention mechanisms to infer motion trajectories of adjacent cardiac frames, thereby better extracting complementary information. Additionally, we employ Transformer-driven dynamic feature aggregation to establish long-range dependencies, facilitating global information integration. Research findings demonstrate that the KGMgT model achieves state-of-the-art performance on multiple benchmark datasets, offering an efficient solution for cardiac cine MRI reconstruction. This collaborative approach, combining artificial intelligence technology to assist medical professionals in clinical decision-making, holds promise for significantly improving diagnostic efficiency, optimizing treatment plans, and enhancing the patient treatment experience. The code and trained models are available at https://github.com/MICV-Lab/KGMgT.