An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular...

Area of Science:

• Cardiovascular fluid dynamics
• Computational fluid dynamics (CFD)
• Medical imaging analysis

Background:

• Accurate computational fluid dynamics (CFD) simulations of the cardiovascular system depend on appropriate boundary conditions.
• Current methods for setting boundary conditions are often manual, time-consuming, and lack patient-specific data assimilation.
• Patient-specific hemodynamic indicators like wall shear stress (WSS) are clinically relevant but sensitive to boundary condition accuracy.

Purpose of the Study:

• To develop an automated technique for estimating outlet boundary conditions in cardiovascular CFD simulations.
• To improve the accuracy and repeatability of simulations by assimilating patient-specific data.
• To compare the proposed method against existing boundary condition techniques.

Main Methods:

• Utilizing an optimal control framework to estimate resistive boundary conditions.
• Setting boundary condition values as control variables optimized to match patient-specific data.
• Validating the method on four patient-specific aortic arch models using 4D-Flow MRI data.

Main Results:

• The proposed optimal control method demonstrated superior assimilation of 4D-Flow MRI data compared to traditional methods.
• The technique accurately estimated patient-specific outlet boundary conditions.
• Simulations using the automated method showed improved accuracy in predicting hemodynamic indicators.

Conclusions:

• Automated estimation of outlet boundary conditions using optimal control offers a more accurate and efficient approach for cardiovascular simulations.
• This technique enhances the assimilation of patient-specific data, leading to more reliable hemodynamic assessments.
• The proposed method provides a foundation for repeatable and clinically relevant CFD simulations in cardiovascular research.