Learning-Based Regularization for Cardiac Strain Analysis via Domain Adaptation.

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Machine Learning

Background:

• 3D+ time echocardiography (4DE) is crucial for myocardial injury assessment.
• Low signal-to-noise ratio (SNR) in 4DE images hinders reliable motion and strain analysis.
• Intelligent regularization is essential for accurate motion estimation in 4DE.

Purpose of the Study:

• To develop a robust regularization framework for 4DE motion estimation using domain adaptation.
• To improve the localization and characterization of myocardial injury.
• To enhance early detection and targeted interventions for cardiac conditions.

Main Methods:

• Incorporated domain adaptation into a supervised neural network regularization framework.
• Proposed a semi-supervised Multi-Layered Perceptron (MLP) network with biomechanical constraints.
• Validated the method on in vivo data using sonomicrometers and compared strain maps to postmortem infarct regions.

Main Results:

• The MLP network learned a latent representation yielding physiologically plausible displacements.
• Biomechanical constraints enhanced the network's domain adaptation capabilities on synthetic data.
• The semi-supervised approach accurately identified infarct regions, showing good agreement with manual tracing.

Conclusions:

• The developed semi-supervised learning regularization method significantly improves motion and strain analysis in 4DE.
• This technique offers a reliable tool for identifying myocardial infarct regions.
• The approach holds promise for enhanced early diagnosis and treatment of cardiac injuries.