|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Computational stent placement in transcatheter aortic valve implantation|
Sündermann, Simon H.
|Conference details:||6th International Symposium ISBMS 2014, Strasbourg, France, 16-17 October 2014|
|Series:||Lecture Notes in Computer Science|
|Publisher / Ed. Institution:||Springer|
|Publisher / Ed. Institution:||Cham|
|Subject (DDC):||617: Surgery|
|Abstract:||Transcatheter aortic valve implantation (TAVI) is a minimally invasive procedure to treat severe aortic stenosis in patients with a high risk for conventional surgery. In-silico experiments of stent deployment within patient-specific models of the aortic root have created an opportunity to predict stent behavior during the intervention. Current limitations in procedure planning are a primary motivator for these simulations. The virtual stent placement preceding the deployment phase of such experiments has major influence on the outcome of the simulation, but only received little attention in literature up to now. This work presents a methodical approach to patient-specific planning of placement of self-expanding stent models by analyzing experimental outcomes of different sets of boundary conditions constraining the stent. As a results, different paradigms for automated or expert guided stent placement are evaluated, which demonstrate the benefits of virtual stent deployment for intervention planning. To build a predictive planning pipeline for TAVI we use an automatic segmentation of the aorta, aortic root and left ventricle, which is converted to a finite element mesh. The virtual stent is then placed along a guide wire model and deployed at multiple locations around the aortic root. The simulation has been evaluated using pre- and post-interventional CT scans with an average relative circumferential error of 4.0% (±2.5%), which is less than half of the average difference in circumference between individual stent sizes (8.6%). Our methods are therefore enabling patient-specific planning and provide better guidance during the intervention.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Computational Life Sciences (ICLS)|
|Appears in collections:||Publikationen Life Sciences und Facility Management|
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