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dc.contributor.authorSIYEOP YOON-
dc.contributor.authorStephen Baek-
dc.contributor.authorLee, Deukhee-
dc.date.accessioned2024-01-12T05:43:02Z-
dc.date.available2024-01-12T05:43:02Z-
dc.date.created2021-09-29-
dc.date.issued2018-09-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/79072-
dc.description.abstractIn this paper, we present a novel method for the real-time cardiac motion compensation. Our method generates interpolated cardiac motion using segmented mesh models from preoperative 3D+T computed tomography angiography (CTA). We propose a pair-wise mesh registration technique for building correspondence and interpolating the control points over a cardiac cycle. The key contribution of this work is a rapid creation of a deformation field through a concise mathematical formulation while maintaining desired properties. These are C 2 continuity, invertibility, incompressibility of cardiac structure and capability to handling large deformation. And we evaluated the proposed method using different conditions, such as deformation resolution, temporal sampling rates, and template model selection.-
dc.languageEnglish-
dc.publisherMICCAI-
dc.title4D Cardiac Motion Modeling Using Pair-Wise Mesh Registration-
dc.typeConference-
dc.identifier.doi10.1007/978-3-030-12029-0_18-
dc.description.journalClass1-
dc.identifier.bibliographicCitation9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, pp.161 - 170-
dc.citation.title9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018-
dc.citation.startPage161-
dc.citation.endPage170-
dc.citation.conferencePlaceGE-
dc.citation.conferencePlaceGranada, Spain-
dc.citation.conferenceDate2018-09-16-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.identifier.scopusid2-s2.0-85064048786-
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KIST Conference Paper > 2018
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