Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM)

Singanallur Venkatakrishnan; Lawrence Drummy; Michael Jackson; Marc De Graef; Jeff Simmons; Charles Bouman

doi:10.1109/SSP.2012.6319793

Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM)

Singanallur Venkatakrishnan, Lawrence Drummy, Michael Jackson, Marc De Graef, Jeff Simmons, Charles Bouman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography. Typically, reconstruction is performed using Filtered Back Projection (FBP) or the SIRT algorithm. In this paper, we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation, the noise characteristics of the measurement, and the inherent smoothness in the object. Reconstructions of polystyrene functionalized Titanium dioxide nano particle assemblies show results that are qualitatively superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.

Original language	English
Title of host publication	2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages	680-683
Number of pages	4
DOIs	https://doi.org/10.1109/SSP.2012.6319793
State	Published - 2012
Externally published	Yes
Event	2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States Duration: Aug 5 2012 → Aug 8 2012

Publication series

Name	2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Conference

Conference	2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/Territory	United States
City	Ann Arbor, MI
Period	08/5/12 → 08/8/12

Keywords

Bayesian
Electron tomography
dark-field
methods

Access to Document

10.1109/SSP.2012.6319793

Cite this

Venkatakrishnan, S., Drummy, L., Jackson, M., De Graef, M., Simmons, J., & Bouman, C. (2012). Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM). In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 (pp. 680-683). Article 6319793 (2012 IEEE Statistical Signal Processing Workshop, SSP 2012). https://doi.org/10.1109/SSP.2012.6319793

@inproceedings{229deada5ef040b394fbbc434c117e7d,

title = "Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM)",

abstract = "HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography. Typically, reconstruction is performed using Filtered Back Projection (FBP) or the SIRT algorithm. In this paper, we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation, the noise characteristics of the measurement, and the inherent smoothness in the object. Reconstructions of polystyrene functionalized Titanium dioxide nano particle assemblies show results that are qualitatively superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.",

keywords = "Bayesian, Electron tomography, dark-field, methods",

author = "Singanallur Venkatakrishnan and Lawrence Drummy and Michael Jackson and {De Graef}, Marc and Jeff Simmons and Charles Bouman",

year = "2012",

doi = "10.1109/SSP.2012.6319793",

language = "English",

isbn = "9781467301831",

series = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",

pages = "680--683",

booktitle = "2012 IEEE Statistical Signal Processing Workshop, SSP 2012",

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}

Venkatakrishnan, S, Drummy, L, Jackson, M, De Graef, M, Simmons, J & Bouman, C 2012, Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM). in 2012 IEEE Statistical Signal Processing Workshop, SSP 2012., 6319793, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, pp. 680-683, 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, Ann Arbor, MI, United States, 08/5/12. https://doi.org/10.1109/SSP.2012.6319793

Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM). / Venkatakrishnan, Singanallur; Drummy, Lawrence; Jackson, Michael et al.
2012 IEEE Statistical Signal Processing Workshop, SSP 2012. 2012. p. 680-683 6319793 (2012 IEEE Statistical Signal Processing Workshop, SSP 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM)

AU - Venkatakrishnan, Singanallur

AU - Drummy, Lawrence

AU - Jackson, Michael

AU - De Graef, Marc

AU - Simmons, Jeff

AU - Bouman, Charles

PY - 2012

Y1 - 2012

N2 - HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography. Typically, reconstruction is performed using Filtered Back Projection (FBP) or the SIRT algorithm. In this paper, we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation, the noise characteristics of the measurement, and the inherent smoothness in the object. Reconstructions of polystyrene functionalized Titanium dioxide nano particle assemblies show results that are qualitatively superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.

AB - HAADF-STEM data is increasingly being used in the physical sciences to study materials in 3D because it is free from the diffraction effects seen in Bright Field STEM data and satisfies the projection requirement for tomography. Typically, reconstruction is performed using Filtered Back Projection (FBP) or the SIRT algorithm. In this paper, we develop a Bayesian reconstruction algorithm for HAADF-STEM tomography which models the image formation, the noise characteristics of the measurement, and the inherent smoothness in the object. Reconstructions of polystyrene functionalized Titanium dioxide nano particle assemblies show results that are qualitatively superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.

KW - Bayesian

KW - Electron tomography

KW - dark-field

KW - methods

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DO - 10.1109/SSP.2012.6319793

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AN - SCOPUS:84868229950

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T3 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

SP - 680

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BT - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

T2 - 2012 IEEE Statistical Signal Processing Workshop, SSP 2012

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ER -

Venkatakrishnan S, Drummy L, Jackson M, De Graef M, Simmons J, Bouman C. Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM). In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012. 2012. p. 680-683. 6319793. (2012 IEEE Statistical Signal Processing Workshop, SSP 2012). doi: 10.1109/SSP.2012.6319793

Bayesian tomographic reconstruction for high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM)

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Keywords

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