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Proceedings Paper

Use of 3D printed intracranial aneurysm phantoms to test the effect of flow diverters geometry on hemodynamics
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Paper Abstract

Purpose: Intracranial aneurysm (IA) treatment using flow diverters (FDs) has become a widely used endovascular therapy with occlusion rates between 70 to 90 percent resulting in reduced mortality and morbidity. This significant variation in occlusion rates could be due to variations in patient anatomy, which causes different flow regimes in the IA dome. We propose to perform detailed in-vitro studies to observe the relation between the FD geometrical properties and IA hemodynamics changes. Materials and Methods: Idealized and patient-specific phantoms were 3D-printed, treated with FDs, and connected into a flow loop where intracranial hemodynamics were simulated using a programmable pump. Pressure measurements were acquired before and after treatment in the main arteries and IA domes for optimal and sub-optimal diameter sizing of the FD when compared with the main artery. The 3D-printed phantoms were scanned using a micro-CT to measure the ostium coverage, calculate the theoretical FD hydraulic resistance, and study its effect on flow. Results: The pressure differences between arteries and the IA dome for optimal FDs’ diameter with a hydraulic resistance of 3.4 were ~7 mmHg. When the FD was undersized, the hydraulic resistance was 4.2 and pressure difference increased to ~11 mmHg. Conclusion: 3D-printing allows development of very precise benchtop experiments where pressure sensors can be embedded in vascular phantoms to study hemodynamic changes due to various therapies such as IA treatment with FDs. In addition, precise imaging, such as micro-CT can be used in order to evaluate complex deployment geometries and study their correlation with flow.

Paper Details

Date Published: 2 March 2020
PDF: 12 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131803 (2 March 2020); doi: 10.1117/12.2549575
Show Author Affiliations
Ariana B. Allman, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Mohammad Mahdi Shiraz Bhurwani, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Jillian L. Senko, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Ryan A. Rava, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Alexander R. Podgorsak, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Stephan Rudin, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)
Ciprian N. Ionita, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

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