Hostname: page-component-5c6d5d7d68-wpx84 Total loading time: 0 Render date: 2024-08-16T14:53:14.791Z Has data issue: false hasContentIssue false

High Resolution X-Ray CT Reconstruction of Additively Manufactured Metal Parts using Generative Adversarial Network-based Domain Adaptation in AI-CT

Published online by Cambridge University Press:  30 July 2021

Amir Ziabari
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Abhishek Dubey
Affiliation:
Center of Cancer Research, National Cancer Institute, United States
Singanallur Venkatakrishnan
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Curtis Frederick
Affiliation:
Carl Zeiss Industrial Metrology, United States
Philip Bingham
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Ryan Dehoff
Affiliation:
Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
Vincent Paquit
Affiliation:
Oak Ridge National Laboratory, United States

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Advanced Characterization of Components Fabricated by Additive Manufacturing
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Jin, P., Bouman, C. A., and Sauer, K. D., “A Model-Based Image Reconstruction Algorithm With Simultaneous Beam Hardening Correction for X-Ray CT,” IEEE Trans. Comput. Imaging, vol. 1, no. 3, pp. 200216, 2015.CrossRefGoogle Scholar
Ruth, V., Kolditz, D., Steiding, C., and Kalender, W. A., “Metal artifact reduction in X-ray computed tomography using computer-aided design data of implants as prior information,” Invest. Radiol., vol. 52, no. 6, pp. 349359, 2017.CrossRefGoogle ScholarPubMed
Van de Casteele, E., Van Dyck, D., Sijbers, J., and Raman, E., “An energy-based beam hardening model in tomography,” Phys. Med. Biol., vol. 47, no. 23, pp. 41814190, 2002.Google ScholarPubMed
Xu, S. and Dang, H., “Deep residual learning enabled metal artifact reduction in CT,” in SPIE Medical Imaging, 2018, no. 10573, p. 132.Google Scholar
Ziabari, A. et al. , “Beam hardening artifact reduction in x-ray ct reconstruction of 3d printed metal parts leveraging deep learning and cad models,”in Proceedings of the ASME 2020 International Mechanical Engineering Congress and Exposition(IMECE), 2020, p. V02BT02A043.Google Scholar
Zhu, J. Y., Park, T., Isola, P., and Efros, A. A., “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2017 Octob, pp. 22422251, 2017.Google Scholar
Wilson, G. and Cook, D. J., “A Survey of Unsupervised Deep Domain Adaptation,” ACM Trans. Intell. Syst. Technol., vol. 11, no. 5, pp. 146, 2020.CrossRefGoogle ScholarPubMed