Work

High-Frequency Modulated Transformer for Multi-Contrast MRI Super-Resolution

Year: 2025

Type: article

Abstract: Accelerating the MRI acquisition process is always a key issue in modern medical practice, and great efforts have been devoted to fast MR imaging. Among them, multi-contrast MR imaging is a promising and effective solution that utilizes and combines information from different contrasts. However, existing methods may ignore the importance of the high-frequency priors among different contrasts. Moreover, they may lack an efficient method to fully utilize the information from the reference contrast. In this paper, we propose a lightweight and accurate High-frequency Modulated Transformer (HFMT) for multi-contrast MRI super-resolution. The key ideas of HFMT are high-frequency prior enhancement and its fusion with global features. Specifically, we employ an enhancement module to enhance and amplify the high-frequency priors in the reference and target modalities. In addition, we utilize the Rectangle Window Transformer Block (RWTB) to capture global information in the target contrast. Meanwhile, we propose a novel cross-attention mechanism to fuse the high-frequency enhanced features with the global features sequentially, which assists the network in recovering clear texture details from the low-resolution inputs. Extensive experiments show that our proposed method can reconstruct high-quality images with fewer parameters and faster inference time.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/tmi.2025.3558164

Authors: Juncheng Li, Hanhui Yang, Qiaosi Yi, Minhua Lu, Jun Shi, Tieyong Zeng

Institutions: Shanghai University, Chinese University of Hong Kong, Hong Kong Polytechnic University, Shenzhen University

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,645