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Preparation and Observation of Thick Biological Samples by Scanning Transmission Electron Tomography

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A model based iterative reconstruction algorithm for high angle annular dark field-scanning transmission electron

S V Venkatakrishnan, Lawrence F Drummy, Michael A Jackson

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 20, 2013

Summary

This study introduces a new iterative algorithm for 3D material reconstruction using high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) tomography. The method significantly reduces artifacts and improves contrast in reconstructions compared to traditional techniques.

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Area of Science:

Materials Science
Electron Microscopy
Image Reconstruction

Background:

High-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) is crucial for 3D materials research.
Traditional reconstruction methods like filtered back projection (FBP) and simultaneous iterative reconstruction technique (SIRT) are limited by artifacts in HAADF-STEM tomography.
Limited angle tomography and low signal-to-noise ratio in HAADF-STEM data exacerbate reconstruction challenges.

Purpose of the Study:

To develop an advanced model-based iterative reconstruction algorithm for HAADF-STEM tomography.
To improve the accuracy and quality of 3D material reconstructions from HAADF-STEM data.
To address artifacts and enhance contrast in limited-angle tomography.

Main Methods:

Developed a model-based iterative reconstruction algorithm incorporating HAADF-STEM image formation.
Formulated the reconstruction as a maximum a posteriori probability (MAP) estimation problem.
Adapted the iterative coordinate descent algorithm for efficient MAP cost function minimization, accounting for missing data.

Main Results:

The proposed algorithm significantly suppresses artifacts in reconstructed volumes.
Enhanced contrast was observed in reconstructions compared to FBP and SIRT methods.
Superior reconstruction results were achieved for both simulated and experimental HAADF-STEM datasets.

Conclusions:

The developed model-based iterative algorithm offers superior performance for HAADF-STEM tomography.
This method provides a significant advancement in 3D materials characterization using electron microscopy.
The approach effectively mitigates artifacts inherent in limited-angle tomography with low signal-to-noise data.