用共享高斯几何实现脑MRI多模态超分辨率重建
AtlasGS: Brain MRI Spatial Resolution Harmonization With Shared Gaussian Geometry
- 构建共享高斯几何框架，先学解剖结构再适配不同模态图像
- 在3个数据集上实现跨模态、跨平面超分辨率，最高提升7倍
- 支持任意视角生成，适合临床影像重建与标准化研究
基于点阵（Splatting）的共享几何框架采用两阶段训练：首先从各向同性结构扫描中学习显式的、个体特异的高斯骨架以编码解剖几何，随后将其复用于稀疏切片采集的目标模态（如T2加权、FLAIR、DWI、ASL）的外观拟合。在英国生物银行、胶质母细胞瘤（GBM）和ABCD数据集上的实验表明，该方法在多种模态、降质因子（×3、×5、×7）及病理异常（如胶质母细胞瘤）条件下均达到当前最优重建保真度。共享高斯几何可实现目标模态的任意视角生成，具有强结构一致性，并展现出自监督内平面超分辨率潜力。本工作确立了显式几何引导表示作为回顾性多对比度MRI统一化与可靠临床参考构建的新路径。源代码已开源：https://github.com/yfgao76/AtlasGS
原文摘要 · Abstract (English)
Splatting (GS)-based shared geometry framework adopts a two-stage training strategy, in which an explicit, subject-specific Gaussian scaffold encoding anatomical geometry is first learned from the isotropic structural scan and then reused to fit appearance for target modalities acquired with sparse slices. Experiments on the UK Biobank, GBM, and ABCD datasets for through-plane super-resolution across multiple modalities (T2-weighted, FLAIR, DWI, ASL), degradation factors ($\times 3$, $\times 5$, $\times 7$), and pathological abnormalities (glioblastoma) demonstrate state-of-the-art reconstruction fidelity. The shared Gaussian geometry enables arbitrary-view generation for target modalities with strong structural consistency and further shows potential for self-supervised in-plane super-resolution. This work establishes explicit geometry-guided representations as a novel, flexible, and interpretable pathway toward retrospective multi-contrast MRI harmonization and reliable clinical reference construction. Source code is available at: https://github.com/yfgao76/AtlasGS
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