Bridging the gap between measurements and modelling: a cardiovascular functional avatar

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Computational Modeling

Background:

• Lumped parameter models aid cardiovascular research but personalization often requires invasive methods or lacks sufficient clinical data.
• Subject-specific models are crucial for accurate understanding of individual cardiovascular function.
• Existing personalization techniques face limitations due to data requirements and invasiveness.

Purpose of the Study:

• To develop and validate a non-invasive approach for personalizing lumped parameter cardiovascular models.
• To create a subject-specific cardiovascular model, termed the 'cardiovascular avatar', using only non-invasive measurements.
• To assess the accuracy of the cardiovascular avatar in reproducing in vivo hemodynamic data.

Main Methods:

• Personalization of a lumped parameter model of the heart and systemic circulation.
• Utilized four-dimensional magnetic resonance imaging (4D-MRI) for flow data acquisition.
• Employed non-invasive cuff pressure measurements from the brachial artery.

Main Results:

• The cardiovascular avatar accurately reproduced subject-specific pressures and flows.
• Quantitative and qualitative agreement was observed between model predictions and in vivo measurements.
• The proof-of-concept study included eight healthy subjects.

Conclusions:

• The proposed non-invasive approach successfully personalizes cardiovascular models.
• The cardiovascular avatar synthesizes medical data into clinically relevant indicators.
• This method enables estimation of unmeasured hemodynamic variables non-invasively.