Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-07-01T04:43:12.641Z Has data issue: false hasContentIssue false

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*
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

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Leveraging 3D Imaging and Analysis Methods for New Opportunities in Material Science
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