USRMamba: Adaptive Routing-Guided State Space Model for Ultrasound Super-Resolution

In ultrasound (US) imaging, resolution degradation caused by the acoustic diffraction limit and transducer array density can significantly reduce image quality, which have negative impacts on clinical diagnosis. Super-resolution (SR) reconstruction is a more flexible and cost-effective measure compared to system upgrades. However, the complexity and diversity of tissue acoustic properties make it difficult to establish a unified model for US image SR reconstruction. In this context, this paper pioneers a revolutionary Mamba-based single US image SR method, referred to as USRMamba. Firstly, a simple and efficient Enhanced Transform Combine Module (ETCM) is designed for shallow feature extraction, which achieves multi-scale decoupling through Laplacian sharpening and wavelet transform to solve the interference of high-frequency information loss and speckle noise in US images; More importantly, an Adaptive Top-k Prompt Module (ATPM) is proposed, whose core is to generate semantic prompts through an adaptive routing-guided strategy to suppress the interference of fuzzy region labels caused by attenuation on detail reconstruction. In addition, a Frequency Channel Attention Module (FCAM) is developed, forming a modeling strategy of "frequency-spatial domain reconstruction" in parallel with ATPM, further optimizing the fidelity for US images SR reconstruction. Qualitative and quantitative experiments demonstrate that USRMamba exhibits superior performance on several US datasets. Especially with scale factor × 2, the proposed method has an average PSNR 1.31 dB higher than state-of-the-art (SOTA) methods.