Hemodynamic Modeling of Biological Aortic Valve Replacement Using Preoperative Data Only

Area of Science:

• Cardiovascular Imaging
• Biomedical Engineering
• Computational Fluid Dynamics

Background:

• Optimizing treatment planning for aortic valve replacement (AVR) requires predicting postoperative hemodynamics.
• Biological AVR is a common procedure, but patient-specific outcomes can vary.

Purpose of the Study:

• To demonstrate a computational fluid dynamics (CFD) approach for predicting postoperative hemodynamics after biological AVR.
• To assess the accuracy of predicted maximum velocity, pressure gradient, secondary flow degree (SFD), and normalized flow displacement (NFD) against postoperative 4D flow MRI data.

Main Methods:

• Virtual AVR was performed on 10 patients using preoperative anatomical and 4D flow MRI data.
• CFD simulations were conducted based on the virtual AVR models.
• Hemodynamic parameters from CFD were compared with postoperative 4D flow MRI measurements.

Main Results:

• Strong correlations were found between CFD and 4D flow MRI for maximum velocity (R²=0.75) and pressure gradient (R²=0.81).
• Moderate and weak correlations were observed for SFD (R²=0.44) and NFD (R²=0.20), respectively.
• Flow visualization showed good qualitative agreement between CFD and MRI.

Conclusions:

• The demonstrated CFD approach effectively estimates postoperative velocity and pressure gradients after biological AVR using only preoperative MRI data.
• This method can help assess prosthesis performance and identify patients at risk of patient-prosthesis mismatch preoperatively.
• Novel parameters like SFD and NFD require further validation and workflow optimization.