Bridge to real data: Empirical multiple material calibration for learning-based material decomposition

Yanye Lu; Martin Berger; Michael Manhart; Jang Hwan Choi; Martin Hoheisel; Markus Kowarschik; Rebecca Fahrig; Qiushi Ren; Joachim Hornegger; Andreas Maier

doi:10.1109/ISBI.2016.7493306

Bridge to real data: Empirical multiple material calibration for learning-based material decomposition

Yanye Lu, Martin Berger, Michael Manhart, Jang Hwan Choi, Martin Hoheisel, Markus Kowarschik, Rebecca Fahrig, Qiushi Ren, Joachim Hornegger, Andreas Maier

Division of Mechanical and Biomedical Engineering

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

4 Scopus citations

Abstract

In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.

Original language	English
Title of host publication	2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publication	From Nano to Macro, ISBI 2016 - Proceedings
Publisher	IEEE Computer Society
Pages	457-460
Number of pages	4
ISBN (Electronic)	9781479923502
DOIs	https://doi.org/10.1109/ISBI.2016.7493306
State	Published - 15 Jun 2016
Event	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic Duration: 13 Apr 2016 → 16 Apr 2016

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2016-June
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/Territory	Czech Republic
City	Prague
Period	13/04/16 → 16/04/16

Bibliographical note

Funding Information:
The authors gratefully acknowledge funding support from the NIH Shared Instrument Grant S10 RR026714 supporting the zeego@StanfordLab, and Siemens AT. The authors also gratefully acknowledge funding of the Research Training Group 1773 Heterogeneous Image Systems and the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Research Foundation (DFG)

Publisher Copyright:
© 2016 IEEE.

Access to Document

10.1109/ISBI.2016.7493306

Cite this

Lu, Y., Berger, M., Manhart, M., Choi, J. H., Hoheisel, M., Kowarschik, M., Fahrig, R., Ren, Q., Hornegger, J., & Maier, A. (2016). Bridge to real data: Empirical multiple material calibration for learning-based material decomposition. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings (pp. 457-460). Article 7493306 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June). IEEE Computer Society. https://doi.org/10.1109/ISBI.2016.7493306

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abstract = "In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.",

author = "Yanye Lu and Martin Berger and Michael Manhart and Choi, {Jang Hwan} and Martin Hoheisel and Markus Kowarschik and Rebecca Fahrig and Qiushi Ren and Joachim Hornegger and Andreas Maier",

note = "Funding Information: The authors gratefully acknowledge funding support from the NIH Shared Instrument Grant S10 RR026714 supporting the zeego@StanfordLab, and Siemens AT. The authors also gratefully acknowledge funding of the Research Training Group 1773 Heterogeneous Image Systems and the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Research Foundation (DFG) Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 ; Conference date: 13-04-2016 Through 16-04-2016",

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Lu, Y, Berger, M, Manhart, M, Choi, JH, Hoheisel, M, Kowarschik, M, Fahrig, R, Ren, Q, Hornegger, J & Maier, A 2016, Bridge to real data: Empirical multiple material calibration for learning-based material decomposition. in 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings., 7493306, Proceedings - International Symposium on Biomedical Imaging, vol. 2016-June, IEEE Computer Society, pp. 457-460, 2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016, Prague, Czech Republic, 13/04/16. https://doi.org/10.1109/ISBI.2016.7493306

Bridge to real data: Empirical multiple material calibration for learning-based material decomposition. / Lu, Yanye; Berger, Martin; Manhart, Michael et al.
2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society, 2016. p. 457-460 7493306 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2016-June).

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

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AU - Hornegger, Joachim

AU - Maier, Andreas

N1 - Funding Information: The authors gratefully acknowledge funding support from the NIH Shared Instrument Grant S10 RR026714 supporting the zeego@StanfordLab, and Siemens AT. The authors also gratefully acknowledge funding of the Research Training Group 1773 Heterogeneous Image Systems and the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Research Foundation (DFG) Publisher Copyright: © 2016 IEEE.

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N2 - In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.

AB - In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.

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Lu Y, Berger M, Manhart M, Choi JH, Hoheisel M, Kowarschik M et al. Bridge to real data: Empirical multiple material calibration for learning-based material decomposition. In 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Proceedings. IEEE Computer Society. 2016. p. 457-460. 7493306. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2016.7493306

Bridge to real data: Empirical multiple material calibration for learning-based material decomposition

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