HFTN-SR: High-Frequency Feature Transfer Network for MR Image Super-Resolution

Magnetic resonance imaging plays a crucial role in clinical diagnosis due to its ability to provide information on soft tissue structure. At present, multi-contrast super-resolution (SR) methods for magnetic resonance (MR) images have been widely studied and have achieved good results. However, most studies overlook the impact of modal differences between reference and low-resolution (LR) image features on feature reconstruction, which may result in inaccurate reconstruction of detail features due to improper alignment of structural features. To address this issue, we propose a high-frequency feature transfer network for MR image SR task (HFTN-SR), which consists of two feature extraction branches and one high-frequency feature transfer branch. Considering the modal differences between the target and reference images, a high-low frequency decomposition method is used to decompose the reference image into high-frequency and low-frequency components, where the low-frequency components are used in the subsequent network to match LR target image features. A feature extraction block (FEB) is constructed to extract and integrate high-frequency and low-frequency features of the reference image, as well as features of the LR target image. In response to the modal differences between two image features, a feature transfer block (FTB) is designed to establish the correlation between the low-frequency features of the reference image and the target image features, and use the correlation matrix to transfer the high-frequency features of the reference image to the target image features. To further reduce the loss of shallow features caused by the increase in network depth, a standardized combined residual feature module (SCRFM) is constructed to supplement the shallow features of the target image into the final reconstructed features. Experiments on the public dataset FastMRI and the self-built dataset AXA show that the performance of HFTN-SR is superior to some state-of-the-art (SOTA) methods. Notably, HFTN-SR achieves the highest PSNR and SSIM scores across all tested datasets, with significant improvements in visual quality and detail reconstruction.