用高斯函数直接建模原子位置，提升电镜三维成像的准确性。
A Gaussian Parameterization for Direct Atomic Structure Identification in Electron Tomography
- 将原子结构表示为可学习的高斯分布，直接求解3D原子位置
- 在模拟和真实数据上均表现出对成像伪影更强的鲁棒性
- 适合材料表征中高精度原子结构分析的科研人员
原子电子断层扫描（AET）通过获取颗粒的二维断层投影序列，计算重建其三维原子结构。传统方法先恢复中间体素表示，再后处理得到原子结构。本文将断层逆问题重新建模，直接求解单个原子的位置与属性。将原子结构参数化为一组可学习的高斯函数，该表示引入强物理先验，显著提升对实际成像伪影的鲁棒性。模拟实验及基于真实采集数据的验证结果表明，该方法在透射电子显微镜（TEM）材料表征中具有实际应用潜力。代码已开源：https://github.com/nalinimsingh/gaussian-atoms。
原文摘要 · Abstract (English)
Atomic electron tomography (AET) enables the determination of 3D atomic structures by acquiring a sequence of 2D tomographic projection measurements of a particle and then computationally solving for its underlying 3D representation. Classical tomography algorithms solve for an intermediate volumetric representation that is post-processed into the atomic structure of interest. In this paper, we reformulate the tomographic inverse problem to solve directly for the locations and properties of individual atoms. We parameterize an atomic structure as a collection of Gaussians, whose positions and properties are learnable. This representation imparts a strong physical prior on the learned structure, which we show yields improved robustness to real-world imaging artifacts. Simulated experiments and a proof-of-concept result on experimentally-acquired data confirm our method's potential for practical applications in materials characterization and analysis with Transmission Electron Microscopy (TEM). Our code is available at https://github.com/nalinimsingh/gaussian-atoms.
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