Characterizing regional myofiber damage post acute myocardial infarction using global optimization.

Area of Science:

• Cardiovascular biomechanics
• Medical imaging analysis
• Computational modeling

Background:

• Cardiac biomechanical models from medical imaging provide valuable diagnostic and prognostic information for cardiovascular diseases.
• Predicting myofiber contraction stresses during ischemic events is a key capability of these models.
• Current methods for heterogeneous contraction models necessitate tissue motion tracking, which is not universally available or clinically implemented.

Purpose of the Study:

• To present a proof-of-concept for a novel technique to predict left ventricle contraction stresses.
• To develop a method independent of tissue tracking capabilities for simulating acute myocardial infarction events.
• To introduce a new variable for finer classification of local myofiber damage.

Main Methods:

• A shape optimization technique was employed for in-silico left ventricle models.
• The method focuses on predicting contraction stresses without requiring tissue motion tracking.
• Three variables were defined within the left ventricle muscle: stresses in healthy and infarct regions, and a novel periinfarct variable.

Main Results:

• The proposed technique successfully predicted contraction stresses in left ventricle models.
• Contraction stress reconstruction errors were less than 12%.
• The novel periinfarct variable allows for a more detailed classification of local myofiber damage.

Conclusions:

• The developed tissue tracking-independent technique offers a viable approach for predicting cardiac contraction stresses.
• This method has the potential to enhance the diagnosis and prognosis of cardiovascular diseases, particularly myocardial infarction.
• The inclusion of a periinfarct variable improves the characterization of myocardial damage.