FS-RWKV: Leveraging Frequency Spatial-Aware RWKV for 3T-to-7T MRI Translation

TL;DR

The paper tackles the challenge of limited access to ultra-high-field 7T MRI by enabling 3T-to-7T translation through FS-RWKV, a frequency-aware RWKV framework. It introduces two core modules, FSO-Shift for global context via wavelet-based shifts and SFEB for multi-domain structural fusion, integrated into a U-Net backbone. The method achieves state-of-the-art results on UNC and BNU datasets for both 3T T1w/T2w inputs, outperforming CNN-, Transformer-, GAN-, and RWKV-based baselines and preserving anatomical fidelity with improved perceptual quality. This approach provides a scalable, hardware-light path to high-fidelity 7T-like MRI, with potential to broaden clinical research and diagnostic capabilities without requiring 7T infrastructure.

Abstract

Ultra-high-field 7T MRI offers enhanced spatial resolution and tissue contrast that enables the detection of subtle pathological changes in neurological disorders. However, the limited availability of 7T scanners restricts widespread clinical adoption due to substantial infrastructure costs and technical demands. Computational approaches for synthesizing 7T-quality images from accessible 3T acquisitions present a viable solution to this accessibility challenge. Existing CNN approaches suffer from limited spatial coverage, while Transformer models demand excessive computational overhead. RWKV architectures offer an efficient alternative for global feature modeling in medical image synthesis, combining linear computational complexity with strong long-range dependency capture. Building on this foundation, we propose Frequency Spatial-RWKV (FS-RWKV), an RWKV-based framework for 3T-to-7T MRI translation. To better address the challenges of anatomical detail preservation and global tissue contrast recovery, FS-RWKV incorporates two key modules: (1) Frequency-Spatial Omnidirectional Shift (FSO-Shift), which performs discrete wavelet decomposition followed by omnidirectional spatial shifting on the low-frequency branch to enhance global contextual representation while preserving high-frequency anatomical details; and (2) Structural Fidelity Enhancement Block (SFEB), a module that adaptively reinforces anatomical structure through frequency-aware feature fusion. Comprehensive experiments on UNC and BNU datasets demonstrate that FS-RWKV consistently outperforms existing CNN-, Transformer-, GAN-, and RWKV-based baselines across both T1w and T2w modalities, achieving superior anatomical fidelity and perceptual quality.

Figures (6)

• Figure 1: Comparison of T1-weighted and T2-weighted MRI images acquired at 3T and 7T. Images at 7T provide higher spatial resolution, improved tissue contrast, and clearer visualization of anatomical structures compared to 3T.
• Figure 2: Quantitative comparison of representative methods for 3T-to-7T MRI translation on T1w (a) and T2w (b) images. Each point corresponds to a different method. Our proposed model outperforms existing methods on both modalities.
• Figure 3: Overview of the FS-RWKV architecture. (a) The overall framework adopts a U-Net structure, where FS-RWKV blocks are used in both encoder and decoder, and SFEB modules enable feature fusion across levels. (b) The detailed design of the FS-RWKV block, which incorporates FSO-Shift modules in both spatial and channel mixing.
• Figure 4: Illustration of (a) Uni-Shift and (b) the proposed FSO-Shift.
• Figure 5: Overview of SFEB