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dc.contributor.authorGlaßer, Sylviaen_US
dc.contributor.authorHoffmann, Thomasen_US
dc.contributor.authorBoese, Axelen_US
dc.contributor.authorVoß, Samuelen_US
dc.contributor.authorKalinski, Thomasen_US
dc.contributor.authorSkalej, Martinen_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.editorKatja Bühler and Lars Linsen and Nigel W. Johnen_US
dc.date.accessioned2015-09-14T04:49:03Z
dc.date.available2015-09-14T04:49:03Z
dc.date.issued2015en_US
dc.identifier.isbn978-3-905674-82-8en_US
dc.identifier.issn2070-5786en_US
dc.identifier.urihttp://dx.doi.org/10.2312/vcbm.20151218en_US
dc.description.abstractWith an increased rate of cerebrovascular diseases, the need for an advanced vessel wall analysis increases as well. In this work, we provide new information of cerebral artery walls extracted with optical coherence tomography (OCT) ex vivo. We present first results of cerebral vessel wall characteristics combined with histological image data. As a prerequisite for this combination, a new image processing method called virtual inflation was developed. This method accounts for the missing blood pressure causing collapsing of the vessels as well as geometrical shape deformations due to catheter probing and histological imaging. We sample the vessel wall thickness locally based on the (deflated) vessel-lumen border instead of the vessel's centerline. The virtual inflation allows for co-aligning of the different image modalities. It is embedded in a multiple coordinated view framework where correspondences between the data can be highlighted via brushing and linking. In combination with histologic image data, we provide OCT signal characteristics of the human cerebral artery wall.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectComputer Graphics [I.3.3]en_US
dc.subjectDisplay Algorithmsen_US
dc.titleHistology-Based Evaluation of Optical Coherence Tomographic Characteristics of the Cerebral Artery Wall via Virtual Inflatingen_US
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicineen_US
dc.description.sectionheadersVisual Computing for Vessel Structuresen_US
dc.identifier.doi10.2312/vcbm.20151218en_US
dc.identifier.pages149-158en_US


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