HFF-Net: A High-Frequency Fidelity Model for Accelerated Parallel MRI Reconstruction

Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing and treating various diseases. However, the long acquisition time of MRI scans often leads to patient discomfort and motion artifacts. Consequently, accelerating MRI speed is essential. Researchers have combined Deep Learning with Compressed Sensing and Parallel Imaging to advance MRI. However, many existing methods fail to effectively recover the fine details and structures in Magnetic Resonance images. To address these challenges, we propose a novel model for accelerated parallel MRI reconstruction. Our model incorporates a high-frequency fidelity method into the reconstruction process, explicitly emphasizing the recovery of high-frequency information. Additionally, we consider the joint priori distribution between the reconstructed images from each coil. Using the variable splitting approach, the proposed model is unrolled as an end-to-end network termed HFF-Net. Experimental results demonstrate that our method outperforms state-of-the-art techniques, yielding high-quality MR images with enhanced detail and fine structure recovery.