用3D高斯点云实现医疗影像超分辨率重建，速度快且保持解剖细节。
MedGSSR: Generalizable Medical Image Super-Resolution 3D Reconstruction via Hierarchical Feed-forward Gaussian Splatting
- 采用显式3D高斯场表示，分层解耦结构与纹理重建过程。
- 在多模态数据上超越现有方法，峰值信噪比提升2.1~3.6dB。
- 无需逐例优化，跨数据集泛化强，适合临床实时应用。
高分辨率三维医学成像对临床诊断至关重要，但受限于扫描仪硬件、扫描时间和CT的辐射剂量。医学3D超分辨率（Med3DSR）提供了计算替代方案，但现有方法常依赖个体优化、预训练先验或基于坐标的隐式表示，影响解剖保真度并降低效率。为此，我们提出MedGSSR，一种全端到端前馈框架，将体积表示为显式3D高斯场以实现医图像超分辨率。不同于基于坐标的隐式函数，该显式3D高斯表示天然增强信号连续性与局部高频保真度。具体而言，通过提出的金字塔解剖编码器和分层高斯投影器，显式分离结构保留与纹理细化。为支持任意尺度超分辨率，引入子体素高斯分解和可微高斯体素化器，直接查询连续3D强度场，减少离散化伪影。在MRI与CT基准测试中，MedGSSR显著优于当前最优方法。值得注意的是，该框架在未见数据集上表现出鲁棒泛化能力，无需逐例优化，实现快速推理与高保真体积超分辨率，适用于实际临床场景。项目主页含代码：https://william2ai.github.io/medgssr
原文摘要 · Abstract (English)
High-resolution volumetric medical imaging is critical for clinical diagnosis, yet acquisition is often limited by scanner hardware, scan time, and for CT, radiation dose. Medical 3D Super-Resolution (Med3DSR) offers a computational alternative, but existing methods commonly rely on per-subject optimization, pretrained priors, or coordinate-based implicit representations, which compromise anatomical fidelity and limit efficiency. To address these limitations, we present MedGSSR, a fully end-to-end feed-forward framework that represents volumes as an explicit 3D Gaussian field for Med3DSR. Unlike coordinate-based implicit functions, our explicit 3D Gaussian representation naturally enhances signal continuity and local high-frequency fidelity. Specifically, MedGSSR explicitly decouples the reconstruction process into coarse-grained structural preservation and fine-grained textural refinement through the proposed Pyramid Anatomical Encoder and a Hierarchical Gaussian Projector. To support arbitrary-scale super-resolution, we introduce sub-voxel Gaussian decomposition and a Differentiable Gaussian Voxelizer that directly queries the continuous 3D intensity field, reducing discretization artifacts. Extensive experiments on MRI and CT benchmarks demonstrate that MedGSSR significantly outperforms state-of-the-art methods. Notably, our framework exhibits robust generalizability across unseen datasets without requiring per-subject optimization, enabling fast inference and high-fidelity volumetric super-resolution in practical clinical settings. Our project webpage, including code, is at https://william2ai.github.io/medgssr
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