AI RESEARCH
Efficient Vision Mamba for MRI Super-Resolution via Hybrid Selective Scanning
arXiv CS.CV
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ArXi:2512.19676v3 Announce Type: replace 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.