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<0.001). 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.
Abstract (translated)
背景：高分辨率MRI在诊断中至关重要，但长时间的获取时间限制了其临床应用。超分辨率(SR)可以提高扫描后的图像质量，然而现有的深度学习方法面临着精度与效率之间的权衡。 目的：开发一种计算高效且准确的深度学习框架用于MRI超分辨率处理，以保持解剖细节并便于临床上的应用整合。 材料和方法：我们提出了一种结合多头选择性状态空间模型(MHSSM)与轻量级通道MLP（多层感知器）的新颖SR框架。该模型采用2D补丁提取与混合扫描来捕捉长距离依赖关系。每个MambaFormer块集成了MHSSM、深度卷积和门控通道混合。评估使用了7T脑部的T1 MP2RAGE映射（n=142）和1.5T前列腺T2w MRI图像（n=334）。对比方法包括双三次插值，生成对抗网络(GANs)如CycleGAN、Pix2pix、SPSR，变换器模型如SwinIR，Mamba模型(MambaIR)，以及扩散模型如I2SB和Res-SRDiff。 结果：我们的模型在效率上表现出色且性能优越。对于7T脑部数据集：SSIM（结构相似性指数）为0.951±0.021、PSNR（峰值信噪比）为26.90±1.41 dB、LPIPS（感知线性判别编码器距离）为0.076±0.022、GMSD（梯度平均标准差）为0.083±0.017，显著优于所有基准方法（p<0.001）。对于前列腺数据集：SSIM为0.770±0.049、PSNR为27.15±2.19 dB、LPIPS为0.190±0.095、GMSD为0.087±0.013。我们的框架仅使用了0.9M参数和57 GFLOPs，相较于Res-SRDiff模型减少了99.8%的参数以及97.5%的计算量，并在准确性和效率上超越了SwinIR和MambaIR。 结论：所提出的框架提供了一种高效且精确的MRI超分辨率解决方案，在不同数据集中增强了解剖细节。其较低的计算需求及顶尖性能显示出了巨大的临床转化潜力。
URL
https://arxiv.org/abs/2512.19676
