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.
|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|
|Number of pages||4|
|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
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016|
|Period||13/04/16 → 16/04/16|
Bibliographical noteFunding 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)
© 2016 IEEE.