Work

Accelerating iterative ptychography with an integrated neural network

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Microscopy

DOI: 10.1111/jmi.13407

Authors: Arthur R. C. McCray, Stephanie M. Ribet, Georgios Varnavides, Colin Ophus

Institutions: Lawrence Berkeley National Laboratory, Molecular Foundry, Stanford University, University of California, Berkeley

Language: English

Topic: Advanced Electron Microscopy Techniques and Applications

Subfield: Structural Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze