TY - GEN
T1 - Bridge to real data
AU - Lu, Yanye
AU - Berger, Martin
AU - Manhart, Michael
AU - Choi, Jang Hwan
AU - Hoheisel, Martin
AU - Kowarschik, Markus
AU - Fahrig, Rebecca
AU - Ren, Qiushi
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.
PY - 2016/6/15
Y1 - 2016/6/15
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.
UR - http://www.scopus.com/inward/record.url?scp=84978376823&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2016.7493306
DO - 10.1109/ISBI.2016.7493306
M3 - Conference contribution
AN - SCOPUS:84978376823
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 457
EP - 460
BT - 2016 IEEE International Symposium on Biomedical Imaging
PB - IEEE Computer Society
Y2 - 13 April 2016 through 16 April 2016
ER -