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Manufacturing Abdominal Aorta Hydrogel Tissue-Mimicking Phantoms for Ultrasound Elastography Validation
Published on: September 19, 2018
Patient based abdominal aortic aneurysm rupture risk prediction combining clinical visualizing modalities with fluid
Michalis Xenos1, Suraj Rambhia, Yared Alemu
1Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794-8181, USA.
Summary
Fluid structure interaction simulations accurately predicted abdominal aortic aneurysm rupture sites by correlating high wall stress. This method estimates rupture potential and disease progression in patient-specific models.
Area of Science:
- Biomedical Engineering
- Computational Fluid Dynamics
- Medical Imaging
Background:
- Abdominal Aortic Aneurysms (AAAs) pose significant rupture risks.
- Accurate prediction of AAA rupture remains a clinical challenge.
- Patient-specific modeling offers a promising approach for risk stratification.
Purpose of the Study:
- To validate fluid structure interaction (FSI) methodology for predicting AAA rupture location.
- To develop a ruptured potential index (RPI) for assessing AAA pathological state.
- To compare healthy and pathological aortas for disease progression insights.
Main Methods:
- Patient-specific AAA geometries (non-ruptured and ruptured) were used for FSI simulations.
- High wall stress regions were correlated with known rupture locations.
- Ruptured Potential Index (RPI) was calculated for pathological assessment.
- Simulations compared healthy and diseased aorta models.
Main Results:
- FSI simulations successfully identified high wall stress regions correlating with rupture sites.
- The developed RPI provided an estimation of the AAA's pathological condition.
- Comparative analysis revealed distinct hemodynamic differences between healthy and pathological aortas.
Conclusions:
- FSI simulation is a viable tool for predicting AAA rupture location.
- RPI can aid in assessing AAA severity and guiding clinical decisions.
- Understanding hemodynamic changes is crucial for predicting AAA disease progression.
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