TY - JOUR
T1 - GEASI
T2 - Geodesic-based earliest activation sites identification in cardiac models
AU - Grandits, Thomas
AU - Effland, Alexander
AU - Pock, Thomas
AU - Krause, Rolf
AU - Plank, Gernot
AU - Pezzuto, Simone
N1 - Publisher Copyright:
© 2021 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.
PY - 2021/8
Y1 - 2021/8
N2 - The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton–Jacobi formulation to minimize a given objective function, for example, the quadratic mismatch to given activation measurements. This versatile approach can be extended to estimate the number of activation sites by means of the topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.
AB - The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton–Jacobi formulation to minimize a given objective function, for example, the quadratic mismatch to given activation measurements. This versatile approach can be extended to estimate the number of activation sites by means of the topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.
KW - cardiac model personalization
KW - earliest activation sites
KW - eikonal equation
KW - Hamilton–Jacobi formulation
KW - inverse ECG problem
KW - topological gradient
UR - http://www.scopus.com/inward/record.url?scp=85110711718&partnerID=8YFLogxK
U2 - 10.1002/cnm.3505
DO - 10.1002/cnm.3505
M3 - Article
C2 - 34170082
AN - SCOPUS:85110711718
SN - 2040-7939
VL - 37
JO - International Journal for Numerical Methods in Biomedical Engineering
JF - International Journal for Numerical Methods in Biomedical Engineering
IS - 8
M1 - e3505
ER -