Electron ptychography is a powerful and versatile tool for high-resolution and dose-efficient imaging. Iterative reconstruction algorithms are powerful but also computationally expensive due to their relative complexity and the many hyperparameters that must be optimised. Gradient descent-based iterative ptychography is a popular method, but it may converge slowly when reconstructing low spatial frequencies. In this work, we present a method for accelerating a gradient descent-based iterative reconstruction algorithm by training a neural network (NN) that is applied in the reconstruction loop. The NN works in Fourier space and selectively boosts low spatial frequencies, thus enabling faster convergence in a manner similar to accelerated gradient descent algorithms. We discuss the difficulties that arise when incorporating a NN into an iterative reconstruction algorithm and show how they can be overcome with iterative training. We apply our method to simulated and experimental data of gold nanoparticles on amorphous carbon and show that we can significantly speed up ptychographic reconstruction of the nanoparticles.

Keywords: 4DSTEM; gradient descent; machine learning; ptychography.

© 2025 Royal Microscopical Society.

电子ptychography是一种强大的、多功能的高分辨率和低剂量成像工具。迭代重建算法虽然功能强大，但由于其相对复杂性和必须优化的许多超参数，计算成本较高。基于梯度下降的迭代ptychography是一种流行的方法，但在重构低空间频率时可能会收敛缓慢。在本工作中，我们提出了一种方法，通过训练一个神经网络（NN）来加速基于梯度下降的迭代重建算法，在重建过程中应用该网络。该神经网络工作在傅里叶空间中，并有选择地增强低空间频率，从而以类似于加速梯度下降算法的方式实现更快的收敛。我们讨论了将神经网络集成到迭代重建算法时出现的困难，并展示了如何通过迭代训练克服这些困难。我们将该方法应用于金纳米颗粒在无定形碳上的模拟和实验数据，并表明可以显著加快纳米颗粒的ptychographic重构速度。

关键词：4DSTEM；梯度下降；机器学习；ptychography。

© 2025 英国显微学会