Cardiac 3D Mechanical and Electrical Signal Reconstruction via Defocused Speckle Imaging

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Medical Imaging

Background:

• Defocused speckle imaging (DSI) non-invasively measures mechanocardiography (MCG) signals like seismocardiography (SCG) and gyrocardiography (GCG).
• Cardiac function relies on electromechanical coupling, suggesting MCG signals could infer electrocardiogram (ECG) activity.

Purpose of the Study:

• To develop CardioNet, a physics-driven framework for simultaneous non-contact reconstruction of cardiac mechanical and electrical activities.
• To leverage DSI-measured MCG signals and physical priors to capture the heart's electromechanical relationship.

Main Methods:

• Developed a physics-driven framework (CardioNet) integrating MCG representations with synchronized ECG.
• Utilized speckle motion signals and physical model-based priors for signal reconstruction.
• Validated the framework in cross-subject studies with laboratory subjects and clinical patients.

Main Results:

• Achieved high Pearson correlation coefficients for SCG (0.718-0.725) and ECG (0.850-0.911) in both settings.
• Demonstrated high accuracy in detecting key cardiac events, with mean timing errors as low as 1.36 ms for SCG and 1.39 ms for ECG.
• Successfully captured fine-grained hemodynamic biomarkers.

Conclusions:

• CardioNet enables comprehensive, non-contact cardiac monitoring by simultaneously reconstructing mechanical and electrical activities.
• The framework shows promise for cardiac rhythm analysis but faces challenges in capturing subtle morphologies and spatial dynamics.
• Future work will focus on 12-lead spatial reconstruction and expanding validation to diverse pathological cohorts.