Model order reduction for left ventricular mechanics via congruency training.

Area of Science:

• Computational biology
• Biomedical engineering
• Cardiovascular modeling

Background:

• Computational models of heart function are crucial for medical practice and clinical trials.
• Current finite element models require extensive parameter optimization, limiting clinical utility.
• Integrating patient-specific anatomical data is essential for accurate cardiovascular simulations.

Observation:

• A novel multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics is presented.
• Simple linear transformations between sarcomere strain and ventricular volume effectively reproduce global pressure-volume outputs.
• A surrogate low-order model can be trained from multi-scale finite elements for parameter optimization.

Findings:

• The proposed method significantly reduces computational complexity in cardiac modeling.
• A single myocyte unit in a reduced model can sufficiently represent global cardiac function.
• The approach facilitates parameter optimization using medical imaging data.

Implications:

• This work streamlines the adaptation of computational heart models for clinical use.
• The method enables efficient processing of large medical image datasets and echocardiographic reports.
• It paves the way for broader application of heart mechanics models in clinical practice.