NDLI: Self-assessing image-based respiratory motion compensation for fluoroscopic coronary roadmapping

Content Provider	IEEE Xplore Digital Library
Author	Manhart, M. Ying Zhu Vitanovski, D.
Copyright Year	2011
Description	Author affiliation: Siemens Corporate Research, 755 College Road East, Princeton, NJ, USA (Manhart, M.; Ying Zhu) \|\| Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany (Vitanovski, D.)
Abstract	We present a self-assessing image-based motion compensation method for coronary roadmapping in fluoroscopic images. Extending our previous work on respiratory motion compensation, we introduce kernel-based nonparametric data analysis in this work to better characterize the objective function involved in motion estimation, which leads to two new improvements in motion compensation. First, through mode analysis we are able to capture the dominant component of the respiratory image motion and increase the chance of finding the global optimum. Second, an information theoretic measure is proposed to assess the uncertainty of the motion estimation and automatically detect unreliable motion estimates. The benefits of the proposed method are shown through evaluations performed on real clinical data from different procedures of percutaneous coronary interventions.
Starting Page	1065
Ending Page	1069
File Size	521797
Page Count	5
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Motion estimation Approximation methods Optimization Motion compensation Pixel Spline Coronary Roadmapping Image Motion Compensation Uncertainty Analysis Fluoroscopic Angiography Image Guidance
Content Type	Text
Resource Type	Article

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