Year: 2025
Type: article
Abstract: 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.
Author: Jin, Yan
Institution: Tiangong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $431
OpenAlex ID: W7117575844
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