Robust electron counting for direct electron detectors with the Back-Propagation Counting method

TL;DR

The paper introduces Back-Propagation Counting (BPC), a machine learning-based method that accurately counts multiple electrons in high-fluence electron microscopy data, improving image quality and diffraction analysis.

Contribution

BPC is a novel electron counting technique that does not require large training datasets and is effective at high electron flux conditions.

Findings

BPC accurately counts multiple electrons per pixel in synthetic data.

BPC improves contrast and reconstructs diffraction peaks in experimental data.

BPC outperforms standard counting methods at high electron flux.

Abstract

Electron microscopy (EM) is a foundational tool for directly assessing the structure of materials. Recent advances in direct electron detectors have improved signal-to noise ratios via single-electron counting. However, accurately counting electrons at high fluence remains challenging. We developed a new method of electron counting for direct electron detectors, Back-Propagation Counting (BPC). BPC uses machine learning techniques designed for mathematical operations on large tensors but does not require large training datasets. In synthetic data, we show BPC is able to count multiple electron strikes per pixel and is robust to increasing occupancy. In experimental data, frames counted with BPC are shown to reconstruct diffraction peaks corresponding to individual nanoparticles with relatively higher intensity and produce images with improved contrast when compared to a standard counting method. Together, these results show that BPC excels in experiments where pixels see a high flux of electron irradiation such as in situ TEM movies and diffraction.