High-resolution Magnetic Resonance Imaging (MRI) is vital for clinical diagnosis but limited by long acquisition times and motion artifacts. Super-resolution (SR) reconstructs low-resolution scans into high-resolution images, yet existing methods are mutually constrained: paired-data methods achieve efficiency only by relying on costly aligned datasets, while implicit neural representation approaches avoid such data needs at the expense of heavy computation. We propose a zero-shot MRI SR framework using explicit Gaussian representation to balance data requirements and efficiency. MRI-tailored Gaussian parameters embed tissue physical properties, reducing learnable parameters while preserving MR signal fidelity. A physics-grounded volume rendering strategy models MRI signal formation via normalized Gaussian aggregation. Additionally, a brick-based order-independent rasterization scheme enables highly parallel 3D computation, lowering training and inference costs. Experiments on two public MRI datasets show superior reconstruction quality and efficiency, demonstrating the method's potential for clinical MRI SR.

翻译：高分辨率磁共振成像（MRI）对临床诊断至关重要，但受限于长采集时间和运动伪影。超分辨率（SR）技术可将低分辨率扫描重建为高分辨率图像，然而现有方法存在相互制约的局限：配对数据方法虽能实现高效重建，但依赖成本高昂的配准数据集；而隐式神经表征方法虽无需此类数据，却以巨额计算量为代价。本文提出一种采用显式高斯表征的零样本MRI超分辨率框架，以平衡数据需求与计算效率。通过定制化的高斯参数嵌入组织物理特性，在保持MR信号保真度的同时减少可学习参数量。基于物理原理的体积渲染策略通过归一化高斯聚合模拟MRI信号形成过程。此外，基于体素块的无序无关栅格化方案实现了高度并行的三维计算，显著降低了训练与推理成本。在两个公开MRI数据集上的实验表明，该方法在重建质量与效率方面均表现优异，展现了其在临床MRI超分辨率应用中的潜力。