{"ID":2826383,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19676","arxiv_id":"2512.19676","title":"Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning","abstract":"Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep learning methods face fidelity-efficiency trade-offs. Purpose: To develop a computationally efficient and accurate deep learning framework for MRI SR that preserves anatomical detail for clinical integration. Materials and Methods: We propose a novel SR framework combining multi-head selective state-space models (MHSSM) with a lightweight channel MLP. The model uses 2D patch extraction with hybrid scanning to capture long-range dependencies. Each MambaFormer block integrates MHSSM, depthwise convolutions, and gated channel mixing. Evaluation used 7T brain T1 MP2RAGE maps (n=142) and 1.5T prostate T2w MRI (n=334). Comparisons included Bicubic interpolation, GANs (CycleGAN, Pix2pix, SPSR), transformers (SwinIR), Mamba (MambaIR), and diffusion models (I2SB, Res-SRDiff). Results: Our model achieved superior performance with exceptional efficiency. For 7T brain data: SSIM=0.951+-0.021, PSNR=26.90+-1.41 dB, LPIPS=0.076+-0.022, GMSD=0.083+-0.017, significantly outperforming all baselines (p\u003c0.001). For prostate data: SSIM=0.770+-0.049, PSNR=27.15+-2.19 dB, LPIPS=0.190+-0.095, GMSD=0.087+-0.013. The framework used only 0.9M parameters and 57 GFLOPs, reducing parameters by 99.8% and computation by 97.5% versus Res-SRDiff, while outperforming SwinIR and MambaIR in accuracy and efficiency. Conclusion: The proposed framework provides an efficient, accurate MRI SR solution, delivering enhanced anatomical detail across datasets. Its low computational demand and state-of-the-art performance show strong potential for clinical translation.","short_abstract":"Background: High-resolution MRI is critical for diagnosis, but long acquisition times limit clinical use. Super-resolution (SR) can enhance resolution post-scan, yet existing deep learning methods face fidelity-efficiency trade-offs. Purpose: To develop a computationally efficient and accurate deep learning framework f...","url_abs":"https://arxiv.org/abs/2512.19676","url_pdf":"https://arxiv.org/pdf/2512.19676v3","authors":"[\"Mojtaba Safari\",\"Shansong Wang\",\"Vanessa L Wildman\",\"Mingzhe Hu\",\"Zach Eidex\",\"Chih-Wei Chang\",\"Erik H Middlebrooks\",\"Richard L. J Qiu\",\"Pretesh Patel\",\"Ashesh B. Jani\",\"Hui Mao\",\"Zhen Tian\",\"Xiaofeng Yang\"]","published":"2025-12-22T18:53:13Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"physics.med-ph\"]","methods":"[\"Diffusion Model\",\"Transformer\",\"Generative Adversarial Network\"]","has_code":false}