发表机构
University of British Columbia; Hangzhou Dianzi University; ShanghaiTech University; Griffith University（不列颠哥伦比亚大学; 杭州电子科技大学; 上海科技大学; 格里菲斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学3D超分辨率中现有方法依赖逐对象优化、保真度低的问题，提出端到端前馈框架MedGSSR，采用显式3D高斯场与层级投影，实现任意尺度超分辨率，在MRI和CT基准上显著优于现有方法，且无需逐对象优化，具备跨数据集泛化能力。
AI 中文摘要
高分辨率体积医学成像对于临床诊断至关重要，然而其采集常常受到扫描仪硬件、扫描时间以及对于CT而言的辐射剂量的限制。医学三维超分辨率（Med3DSR）提供了一种计算替代方案，但现有方法通常依赖于逐对象优化、预训练先验或基于坐标的隐式表示，这些方法损害了解剖保真度并限制了效率。为解决这些限制，我们提出了MedGSSR，一个完全端到端的前馈框架，将体积表示为显式的三维高斯场用于Med3DSR。与基于坐标的隐式函数不同，我们的显式三维高斯表示自然地增强了信号连续性和局部高频保真度。具体而言，MedGSSR通过所提出的金字塔解剖编码器和层级高斯投影仪，将重建过程显式解耦为粗粒度结构保留和细粒度纹理细化。为支持任意尺度的超分辨率，我们引入了亚体素高斯分解和可微分高斯体素化器，该体素化器直接查询连续的3D强度场，减少离散化伪影。在MRI和CT基准上的大量实验表明，MedGSSR显著优于最先进的方法。值得注意的是，我们的框架在未见数据集上表现出强大的泛化能力，无需逐对象优化，从而在实际临床环境中实现快速推理和高保真体积超分辨率。我们的项目网页（包含代码）位于此https URL。
英文摘要
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
CommentsECCV 2026
