Publication year
2012Author(s)
Number of pages
13 p.
Source
Medical Image Analysis, 16, 1, (2012), pp. 127-139ISSN
Annotation
01 januari 2012
Publication type
Article / Letter to editor
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Organization
Surgery
Radiology
Journal title
Medical Image Analysis
Volume
vol. 16
Issue
iss. 1
Languages used
English (eng)
Page start
p. 127
Page end
p. 139
Subject
NCEBP 14: Cardiovascular diseases; NCEBP 14: Cardiovascular diseases NCMLS 3: Tissue engineering and pathology; ONCOL 5: Aetiology, screening and detectionAbstract
Endovascular aortic replacement (EVAR) is an established technique, which uses stent grafts to treat aortic aneurysms in patients at risk of aneurysm rupture. Late stent graft failure is a serious complication in endovascular repair of aortic aneurysms. Better understanding of the motion characteristics of stent grafts will be beneficial for designing future devices. In addition, analysis of stent graft movement in individual patients in vivo can be valuable for predicting stent graft failure in these patients. To be able to gather information on stent graft motion in a quick and robust fashion, we propose an automatic method to segment stent grafts from CT data, consisting of three steps: the detection of seed points, finding the connections between these points to produce a graph, and graph processing to obtain the final geometric model in the form of an undirected graph. Using annotated reference data, the method was optimized and its accuracy was evaluated. The experiments were performed using data containing the AneuRx and Zenith stent grafts. The algorithm is robust for noise and small variations in the used parameter values, does not require much memory according to modern standards, and is fast enough to be used in a clinical setting (65 and 30s for the two stent types, respectively). Further, it is shown that the resulting graphs have a 95% (AneuRx) and 92% (Zenith) correspondence with the annotated data. The geometric model produced by the algorithm allows incorporation of high level information and material properties. This enables us to study the in vivo motions and forces that act on the frame of the stent. We believe that such studies will provide new insights into the behavior of the stent graft in vivo, enables the detection and prediction of stent failure in individual patients, and can help in designing better stent grafts in the future.
This item appears in the following Collection(s)
- Academic publications [238430]
- Faculty of Medical Sciences [90359]
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