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X-Ray CT Reconstruction of Additively Manufactured Parts using 2.5D Deep Learning MBIR

Published online by Cambridge University Press:  05 August 2019

Amirkoushyar Ziabari ,
Michael Kirka ,
Vincent Paquit ,
Philip Bingham  and
Singanallur Venkatakrishnan
Amirkoushyar Ziabari*
Affiliation:
Imaging, Signals and Machine Learning Group, Oak Ridge National Lab
Michael Kirka
Affiliation:
Deposition Science and Technology, Oak Ridge National Lab
Vincent Paquit
Affiliation:
Imaging, Signals and Machine Learning Group, Oak Ridge National Lab
Philip Bingham
Affiliation:
Imaging, Signals and Machine Learning Group, Oak Ridge National Lab
Singanallur Venkatakrishnan
Affiliation:
Imaging, Signals and Machine Learning Group, Oak Ridge National Lab
*
*Corresponding author: aziabari@ornl.gov

Abstract

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Type
Leveraging 3D Imaging and Analysis Methods for New Opportunities in Material Science
Information
Microscopy and Microanalysis , Volume 25 , Supplement S2 , August 2019 , pp. 376 - 377
Copyright
Copyright © Microscopy Society of America 2019 

References

[1]Venkatakrishnan, S.V., et al. , “Model-Based Iterative Reconstruction for Bright-Field Electron Tomography,” IEEE Trans. on Computational Imaging Vol. 1, Issue 1, pp. 1-15, 2015Google Scholar
[2]Mohan, K. A., et al. , “TIMBIR: A Method for Space-Time Reconstruction from Interlaced Views”, IEEE Trans. on Computational Imaging, Vol. 1, No.2, 2015Google Scholar
[3]Venkatakrishnan, S.V., et al. , “Model-based iterative reconstruction for neutron laminography”, IEEE Asilomar Conference on Signals, Systems and Computer, 2017.Google Scholar
[4]Ziabari, A., et al. , “2.5 D Deep Learning for CT Image Reconstruction using a Multi-GPU implementation,” IEEE Asilomar Conference on Signal, Systems and Computers, 2018.Google Scholar