论文标题
0.71-Å分辨率电子断层扫描通过深度学习辅助信息恢复启用
0.71-Å resolution electron tomography enabled by deep learning aided information recovery
论文作者
论文摘要
电子断层扫描是一种重要的3D成像方法，它提供了一种强大的方法，可以探测从纳米尺度到原子尺度的材料的3D结构。但是，作为赠款挑战，纳米级样品的辐射不耐受以及缺失的wedde诱导的信息丢失和人工制品极大地阻碍了我们获得高忠诚度的3D原子结构。在这里，通过将生成性对抗模型与最先进的网络体系结构相结合，我们证明了电子断层扫描的分辨率可以提高到0.71 Angstrom，这是迄今为止迄今已报告的最高三维成像分辨率。我们还表明，仅通过获取-50至+50度的数据来恢复丢失的信息并删除重建的断层图中的伪像（剂量降低了44％，而44％的剂量比-90至+90度全倾斜系列）。与常规方法相反，深度学习模型显示了宏观物体和原子特征的出色性能，这些特征解决了电子层析成像中长期存在的剂量和缺失的边缘问题。我们的工作为将机器学习方法应用于层析成像的应用提供了重要的指导，并阐明了其在其他3D成像技术中的应用。
Electron tomography, as an important 3D imaging method, offers a powerful method to probe the 3D structure of materials from the nano- to the atomic-scale. However, as a grant challenge, radiation intolerance of the nanoscale samples and the missing-wedge-induced information loss and artifacts greatly hindered us from obtaining 3D atomic structures with high fidelity. Here, for the first time, by combining generative adversarial models with state-of-the-art network architectures, we demonstrate the resolution of electron tomography can be improved to 0.71 angstrom which is the highest three-dimensional imaging resolution that has been reported thus far. We also show it is possible to recover the lost information and remove artifacts in the reconstructed tomograms by only acquiring data from -50 to +50 degrees (44% reduction of dosage compared to -90 to +90 degrees full tilt series). In contrast to conventional methods, the deep learning model shows outstanding performance for both macroscopic objects and atomic features solving the long-standing dosage and missing-wedge problems in electron tomography. Our work provides important guidance for the application of machine learning methods to tomographic imaging and sheds light on its applications in other 3D imaging techniques.
