GEASI: Geodesic‐based earliest activation sites identification in cardiac models. (13th July 2021)

• Record Type: Journal Article
• Authors: Grandits, Thomas; Effland, Alexander; Pock, Thomas; Krause, Rolf; Plank, Gernot; Pezzuto, Simone
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 37:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-13
• Subjects: cardiac model personalization -- earliest activation sites -- eikonal equation -- Hamilton–Jacobi formulation -- inverse ECG problem -- topological gradient; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3505
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: 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 ( G eodesic‐based E arliest A ctivation S ites I dentification) 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. Abstract : The identification of the earliest activation sites (EASs) of cardiac activation from the surface electrocardiogram (ECG) constitutes the grand challenge in patient‐specific modeling. To this aim, this work introduces GEASI (Geodesic‐based EASs Identification), an innovative method to identify the number, location, and onset timing of EASs from non‐local activation measurements or even the ECG. The effectiveness and versatility of GEASI is demonstrated through a variety of experiments, strongly supporting its clinical feasibility.