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Related Experiment Video

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Sub-pixel electron detection using a convolutional neural network.

J Paul van Schayck¹, Eric van Genderen², Erik Maddox³

¹Maastricht MultiModal Molecular Imaging Institute (M4I), FHML, Maastricht University, Maastricht, The Netherlands.

Ultramicroscopy

|August 25, 2020

Summary

This summary is machine-generated.

New Timepix3 detectors can be used for high-energy cryo-electron microscopy (cryo-EM), improving image quality and reducing exposure times for macromolecular structure determination.

Keywords:

Cryo-EM Detectors Neural network Structural biology

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Area of Science:

* Structural Biology
* Microscopy and Imaging
* Materials Science

Background:

* Direct electron detectors (DEDs) have advanced cryogenic electron microscopy (cryo-EM) for macromolecular studies.
* Current DEDs require long exposures and optimal performance at 300 keV, limiting broader applications.
* Hybrid Pixel Detectors (HPDs) were previously deemed unsuitable for cryo-EM above 80 keV due to electron scattering.

Purpose of the Study:

* To evaluate the suitability of Timepix3 detectors for cryo-EM at higher electron energies (200-300 keV).
* To develop methods for correcting detector artifacts and improving electron event localization.
* To assess the performance enhancement of Timepix3 detectors in cryo-EM imaging.

Main Methods:

* Timepix3 detectors were tested on 200 keV and 300 keV electron microscopes.
* A per-pixel correction method was developed to address output variations.
* Geant4Medipix simulations were used to model detector response, and a convolutional neural network (CNN) was trained to predict electron positions.

Main Results:

* Simulated detector response closely matched experimental data.
* A CNN accurately predicted electron incident positions, improving localization by 0.50 pixels (200 keV) and 0.68 pixels (300 keV).
* The CNN significantly enhanced the Modulation Transfer Function (MTF), improving it from 0.39 to 0.70 at 200 keV and 0.06 to 0.65 at 300 keV.

Conclusions:

* Timepix3 detectors are viable for cryo-EM at higher energies, overcoming previous limitations.
* The developed CNN-based approach substantially improves image resolution and data quality.
* Timepix3 enables efficient data acquisition, allowing protein dose-lifetime measurements within a 1-second exposure.