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Updated: Nov 1, 2025
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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
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GEASI: Geodesic-based earliest activation sites identification in cardiac models
Thomas Grandits1,2, Alexander Effland1,3,4, Thomas Pock1,2
1Institute of Computer Graphics and Vision, TU Graz, Graz, Austria.
Summary
Identifying earliest activation sites (EASs) is crucial for personalized cardiac models. GEASI, a novel geodesic-based method, accurately pinpoints multiple EASs from complex cardiac data, improving model personalization.
Area of Science:
- Computational biology
- Biomedical engineering
- Cardiovascular research
Background:
- Accurate ventricular activation sequence identification is vital for patient-specific cardiac models.
- Pathological conditions can lead to sparse and localized earliest activation sites (EASs).
- Inferring EASs from remote recordings like ECG is challenging due to model complexity.
Purpose of the Study:
- Introduce GEASI (Geodesic-based Earliest Activation Sites Identification), a novel approach to simultaneously identify all EASs.
- Develop a versatile method applicable to various cardiac conditions and data types.
- Enhance the personalization of patient-specific cardiac models.
Main Methods:
- Utilize the anisotropic eikonal equation for cardiac electrical activation modeling.
- Employ Hamilton-Jacobi formulation to minimize objective functions, such as quadratic mismatch to activation measurements.
- Incorporate topological gradient for estimating the number of activation sites.
Main Results:
- GEASI successfully identifies multiple EASs simultaneously.
- Demonstrated applicability in 2D and 3D in-silico models.
- Validated with in-vivo intracardiac recordings from a patient undergoing cardiac resynchronization therapy.
Conclusions:
- GEASI offers a robust and versatile solution for identifying earliest activation sites.
- The method shows significant clinical applicability for personalized cardiac modeling.
- Potential to improve patient-specific models and guide clinical interventions.
