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Updated: May 27, 2025

Imaging Replicative Domains in Ultrastructurally Preserved Chromatin by Electron Tomography

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Published on: May 20, 2022

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography.

Singanallur Venkatakrishnan¹, Obaidullah Rahman², Lynda Amichi³

¹Multi-modal Sensor Analytics Group, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. venkatakrisv@ornl.gov.

Scientific Reports

|February 18, 2025

Summary

This summary is machine-generated.

This study introduces a novel two-stage algorithm to improve 3D reconstructions from electron tomography. The method effectively reduces artifacts from metal particles, enhancing material characterization for applications like fuel cell catalysts.

Keywords:

Artifact reduction Diffraction Electron tomography Model-based reconstruction

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Cryo-Electron Tomography Remote Data Collection and Subtomogram Averaging

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Published on: July 12, 2022

Area of Science:

Materials Science
Imaging Science
Nanotechnology

Background:

High-quality 3D reconstructions are vital for analyzing crystalline particles in materials, especially for fuel cell catalysts.
Electron tomography faces challenges like limited tilt range, low signal-to-noise, and artifacts from metal particles.
Conventional algorithms struggle with artifacts caused by Bragg diffraction and material property differences, impacting accurate characterization.

Purpose of the Study:

To develop an advanced algorithm for high-fidelity 3D electron tomography reconstructions.
To overcome artifacts caused by metal particles in lighter support materials.
To improve the accuracy of downstream characterization of material systems.

Main Methods:

A two-stage algorithm incorporating metal artifact reduction techniques.
Utilizing model-based iterative reconstruction with adaptive regularization parameter adjustment.
Validation using simulated and experimental bright-field electron tomography data.

Main Results:

The proposed algorithm significantly reduces streaking and shading artifacts.
Achieves superior 3D reconstruction quality compared to traditional methods.
Accurately reconstructs both metal particles and surrounding support materials.

Conclusions:

The developed algorithm provides high-quality 3D reconstructions essential for accurate material analysis.
It effectively addresses limitations of conventional methods in electron tomography of metal-containing systems.
Enables more reliable characterization of material properties for advanced applications.