用物理驱动的高斯渲染实现无需配对数据的MRI超分辨率重建
Physics-Driven 3D Gaussian Rendering for Zero-Shot MRI Super-Resolution
- 用定制高斯参数建模组织物理特性，减少参数量
- 在两个公开数据集上实现更优画质与更低计算开销
- 适合追求高效无配对数据的医学影像研究者
高分辨率磁共振成像（MRI）对临床诊断至关重要，但受限于扫描时间长和运动伪影。超分辨率（SR）可将低分辨率图像重建为高分辨率，但现有方法存在矛盾：有配对数据的方法依赖昂贵的对齐数据集以实现高效，而隐式神经表示方法虽无需数据却计算量巨大。本文提出一种零样本MRI超分辨率框架，采用显式高斯表示，在降低数据依赖的同时提升效率。针对MRI设计的高斯参数嵌入组织物理属性，减少可学习参数并保持磁共振信号保真度。基于物理的体素渲染策略通过归一化高斯聚合模拟MRI信号形成过程。此外，基于砖块的顺序无关光栅化方案实现高度并行3D计算，显著降低训练与推理成本。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优于现有方法，展现出在临床MRI超分辨率中的应用潜力。
原文摘要 · Abstract (English)
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.
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