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Proceedings Paper

Validation of an image registration and segmentation method to measure stent graft motion on ECG-gated CT using a physical dynamic stent graft model
Author(s): Maaike A. Koenrades; Ella M. Struijs; Almar Klein; Henny Kuipers; Robert H. Geelkerken; Cornelis H. Slump
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Paper Abstract

The application of endovascular aortic aneurysm repair has expanded over the last decade. However, the long-term performance of stent grafts, in particular durable fixation and sealing to the aortic wall, remains the main concern of this treatment. The sealing and fixation are challenged at every heartbeat due to downward and radial pulsatile forces. Yet knowledge on cardiac-induced dynamics of implanted stent grafts is sparse, as it is not measured in routine clinical follow-up. Such knowledge is particularly relevant to perform fatigue tests, to predict failure in the individual patient and to improve stent graft designs. Using a physical dynamic stent graft model in an anthropomorphic phantom, we have evaluated the performance of our previously proposed segmentation and registration algorithm to detect periodic motion of stent grafts on ECG-gated (3D+t) CT data. Abdominal aortic motion profiles were simulated in two series of Gaussian based patterns with different amplitudes and frequencies. Experiments were performed on a 64-slice CT scanner with a helical scan protocol and retrospective gating. Motion patterns as estimated by our algorithm were compared to motion patterns obtained from optical camera recordings of the physical stent graft model in motion. Absolute errors of the patterns' amplitude were smaller than 0.28 mm. Even the motion pattern with an amplitude of 0.23 mm was measured, although the amplitude of motion was overestimated by the algorithm with 43%. We conclude that the algorithm performs well for measurement of stent graft motion in the mm and sub-mm range. This ultimately is expected to aid in patient-specific risk assessment and improving stent graft designs.

Paper Details

Date Published: 3 March 2017
PDF: 11 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013418 (3 March 2017); doi: 10.1117/12.2254262
Show Author Affiliations
Maaike A. Koenrades, MIRA Institute for Biomedical Engineering and Technical Medicine, Univ. of Twente (Netherlands)
Medisch Spectrum Twente (Netherlands)
Ella M. Struijs, MIRA Institute for Biomedical Engineering and Technical Medicine, Univ. of Twente (Netherlands)
Almar Klein, MIRA Institute for Biomedical Engineering and Technical Medicine, Univ. of Twente (Netherlands)
Henny Kuipers, MIRA Institute for Biomedical Engineering and Technical Medicine, Univ. of Twente (Netherlands)
Robert H. Geelkerken, MIRA Institute for Biomedical Engineering and Technical Medicine, Univ. of Twente (Netherlands)
Medisch Spectrum Twente (Netherlands)
Cornelis H. Slump, MIRA Institute for Biomedical Engineering and Technical Medicine, Univ. of Twente (Netherlands)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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