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Updated: Jan 15, 2026
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
Published on: January 8, 2013
Reconstructing ventricular activation sequences from epicardial data: Insights from Geodesic Back-Propagation
Lindsay C R Tanner1, Anna Busatto1, Thomas Grandits2
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States of America; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, United States of America; Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America.
Cardiac digital twins (CDTs) can be personalized by inferring early activation sites (EASs) from epicardial data. This method shows potential but requires further refinement for robust, realistic cardiac modeling.
Area of Science:
- Computational Biology
- Biomedical Engineering
- Cardiovascular Physiology
Background:
- Cardiac digital twins (CDTs) are crucial for personalized medicine, enabling subject-specific cardiac function simulation.
- Accurate personalization of the His-Purkinje system (HPS) is a key challenge for CDTs.
- The HPS dictates ventricular activation patterns.
Purpose of the Study:
- To develop and evaluate a novel method for inferring early activation sites (EASs) from epicardial activation times.
- To assess the feasibility of using EASs as surrogates for Purkinje-myocardial junctions in CDTs.
- To determine the accuracy and variability of the proposed method using experimental and synthetic data.
Main Methods:
- A modified Geodesic-BP method was employed to infer EASs from epicardial activation times.
- Experimental porcine and synthetic datasets were used, with electrode sock measurements.
- Inference was repeated 10 times for varying numbers of EASs (5, 50, 100, 200) to assess variability.
Main Results:
- The algorithm successfully recovered global ventricular activation patterns from epicardial data alone.
- Mean absolute differences ranged from 0.13-3.86 ms on the epicardial surface and 2.42-14.07 ms within the myocardium.
- Limitations included discrepancies between epicardial and intramural data, EAS overfitting, and run-to-run variability.
Conclusions:
- Inferring EASs from epicardial data is feasible for improving CDT personalization.
- The method demonstrates potential for deriving activation patterns from limited data.
- Future work should incorporate physiological priors and anatomical constraints to enhance CDT robustness and realism.
