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Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
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Virtual and analytical self-expandable braided stent treatment models
Reza Abdollahi1, Amirali Shahi1, Daniel Roy2
1Faculté de médecine, Université de Montréal, H3T 1J4, Montréal, Canada; Centre de Recherche du Centre Hospitalier de l'Université de Montréal, H2X 0A9, Montréal, Canada.
Medical Engineering & Physics
|April 15, 2024
Summary
A new biomechanical computational method improves cerebral aneurysm treatment planning. This method offers accurate, patient-specific predictions for flow diverter deployment, enhancing pre-operative strategies and reducing complications.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Computational Fluid Dynamics
Background:
- Cerebral aneurysm treatment effectiveness relies on pre-operative planning.
- Current static measurements lack accuracy due to vessel/tissue deformation.
- Flow diverters are crucial self-expandable braided stents for aneurysm treatment.
Purpose of the Study:
- To develop a biomechanical computational method for predicting patient-specific treatment outcomes.
- To improve pre-operative decision-making in cerebral aneurysm treatment.
- To enhance the accuracy of flow diverter deployment predictions.
Main Methods:
- Integration of virtual and analytical treatment models.
- Validation against experimental mechanical tests and patient outcomes.
- Utilizing biomechanical simulations for realistic measurements.
Main Results:
- Both virtual and analytical models showed high accuracy in predicting deployed stent length (error < 1.5%).
- The analytical model demonstrated superior accuracy (0.3% error) with lower computational cost.
- The method accurately predicted patient-specific treatment outcomes.
Conclusions:
- The developed biomechanical computational method accurately predicts flow diverter deployment.
- The analytical model offers a more efficient and accurate approach for pre-operative planning.
- This method can enhance patient-specific device design and automate interventional procedures, potentially reducing complications.
