# 0.71-AA resolution electron tomography enabled by deep learning aided information recovery

arXiv: https://arxiv.org/abs/2003.12259 · HTML: https://mlsanity.com/paper/2003.12259/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials](https://mlsanity.com/paper/2201.07342/) (2022)
- [Advancing atomic electron tomography with neural networks](https://mlsanity.com/paper/2506.16104/) (2025)

## Abstract (arXiv)

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.