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Publisher DOI: 10.1038/s41598-018-29282-0
Title: Analysis of the influence of imaging-related uncertainties on cerebral aneurysm deformation quantification using a no-deformation physical flow phantom
Language: English
Authors: Schetelig, Daniel 
Sedlacik, Jan 
Fiehler, Jens 
Frölich, Andreas M. J. 
Knopp, Tobias 
Sothmann, Thilo 
Waschkewitz, Jonathan 
Werner, René 
Issue Date: 20-Jul-2018
Publisher: Nature Publishing Group UK
Source: Scientific reports 1 (8): 11004- (2018-07-20)
Journal or Series Name: Scientific reports 
Abstract (english): 
Cardiac-cycle related pulsatile aneurysm motion and deformation is assumed to provide valuable information for assessing cerebral aneurysm rupture risk. Accordingly, numerous studies addressed quantification of cerebral aneurysm wall motion and deformation. Most of them utilized in vivo imaging data, but image-based aneurysm deformation quantification is subject to pronounced uncertainties: unknown ground-truth deformation; image resolution in the order of the expected deformation; direct interplay between contrast agent inflow and image intensity. To analyze the impact of the uncertainties on deformation quantification, a multi-imaging modality ground-truth phantom study is performed. A physical flow phantom was designed that allowed simulating pulsatile flow through a variety of modeled cerebral vascular structures. The phantom was imaged using different modalities [MRI, CT, 3D-RA] and mimicking physiologically realistic flow conditions. Resulting image data was analyzed by an established registration-based approach for automated wall motion quantification. The data reveals severe dependency between contrast media inflow-related image intensity changes and the extent of estimated wall deformation. The study illustrates that imaging-related uncertainties affect the accuracy of cerebral aneurysm deformation quantification, suggesting that in vivo imaging studies have to be accompanied by ground-truth phantom experiments to foster data interpretation and to prove plausibility of the applied image analysis algorithms.
DOI: 10.15480/882.1763
ISSN: 2045-2322
Institute: Biomedizinische Bildgebung E-5 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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