Model based non-invasive estimation of PV loop from echocardiography

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Computational Modeling

Background:

• Accurate assessment of left ventricular function is crucial for diagnosing and managing cardiovascular diseases.
• Non-invasive methods for estimating patient-specific pressure-volume (PV) loops are highly desirable in clinical practice.
• Existing methods often lack automation or comprehensive validation.

Purpose of the Study:

• To introduce a fully automated, model-based approach for the non-invasive estimation of patient-specific left ventricular (LV) PV loops.
• To personalize a lumped parameter circulation model using a two-step parameter estimation framework.
• To validate the proposed methodology in healthy volunteers and a patient with aortic valve regurgitation.

Main Methods:

• Utilized a lumped parameter circulation model encompassing pulmonary venous circulation, left atrium, left ventricle, and systemic circulation.
• Developed a two-step automated parameter estimation framework: direct computation of initial parameters followed by optimization-based calibration.
• Evaluated the methodology using data from three healthy volunteers and one patient with mild aortic valve regurgitation.

Main Results:

• Achieved perfect agreement between computed and clinically measured quantities in healthy volunteers.
• Demonstrated close agreement between non-invasively estimated and invasively determined time-varying LV pressures, LV volumes, and PV loops in a patient with aortic valve regurgitation.
• Validated the accuracy and feasibility of the automated PV loop estimation method.

Conclusions:

• The proposed model-based approach enables accurate and non-invasive estimation of patient-specific left ventricular PV loops.
• The automated parameter estimation framework facilitates efficient and personalized cardiovascular modeling.
• This methodology holds potential for improved non-invasive assessment of cardiac function in various clinical scenarios.