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

doi:10.1109/ISBI.2016.7493306

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	Bridge to real data: Empirical multiple material calibration for learning-based material decomposition
	Lu, Yanye ; Berger, Martin ; Manhart, Michael ; Choi, Jang-Hwan ; Hoheisel, Martin ; Kowarschik, Markus ; Fahrig, Rebecca ; Ren, Qiushi ; Hornegger, Joachim ; Maier, Andreas
	2016
DOI	10.1109/ISBI.2016.7493306
英文摘要	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. ? 2016 IEEE.; EI; 457-460; 2016-June
会议录	2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
语种	英语
内容类型	会议论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/449452]
专题	工学院
推荐引用方式 GB/T 7714	Lu, Yanye,Berger, Martin,Manhart, Michael,et al. Bridge to real data: Empirical multiple material calibration for learning-based material decomposition[C]. 见:.