Integration of activation maps of epicardial veins in computational cardiac electrophysiology

Stella, Simone; Vergara, Christian; Maines, Massimiliano; Catanzariti, Domenico; Africa, Pasquale Claudio; Demattè, Cristina; Centonze, Maurizio; Nobile, Fabio; Greco, Maurizio Del; Quarteroni, Alfio

doi:10.1016/j.compbiomed.2020.104047

Cited by 15 publications

(18 citation statements)

References 55 publications

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“…Moreover, the meshing tools proposed in this paper have also been used in other works regarding the cardiac electrophysiology, 93,94 the cardiac electromechanics, 95 and the cardiac perfusion 96 . Finally, we remark that also electro‐mechanics and electro‐mechano‐fluid models of the whole heart are currently under development.…”

Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 83%

“…The proposed algorithms have been combined into ad‐hoc pipelines to test them in some complex cardiac applications, like the mesh generation of a fully‐detailed ventricular geometry including the papillary muscles and the trabeculae carneae (Section 3.2) or a complete fluid‐dynamics mesh of the left‐heart (Section 3.3). Moreover, as discussed in Section 3.4, they have already been successfully used in other papers to address a broad range of applications 28,91‐96 . These examples highlight the flexibility of these tools and their ability to be easily applicable to a large variety of cardiac mesh generations through the building of ad‐hoc application‐dependent pipelines.…”

Section: Discussionmentioning

confidence: 98%

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Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Fedele

Quarteroni

2021

Numer Methods Biomed Eng

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In order to simulate the cardiac function for a patient‐specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad‐hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh‐size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms—implemented in the open‐source software vmtk—can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed‐up the mesh generation process for a large range of cardiac applications, from single‐chamber single‐physics simulations to multi‐chambers multi‐physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.

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Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 98%

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Fedele

Quarteroni

2021

Numer Methods Biomed Eng

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“…Overall, the parameters of the eikonal model with lumped Purkinje network are just the location, the number and the activation onset of the EASs, as well as the conduction velocities in the thin layers and the myocardium. The problem of fitting these parameters to clinical data has already been considered in the literature for the conduction velocities [3,24,38,23]. However, the optimization of EASs received limited attention so far with only a few works dealing with activation onsets [25] or locations [27].…”

Section: Related Workmentioning

confidence: 99%

GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models

Grandits,

Effland,

Pock

et al. 2021

Preprint

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The personalization of cardiac models is the cornerstone of patient-specific modeling. Ideally, non-invasive or minimally-invasive clinical data, such as the standard ECG or intracardiac contact recordings, could provide an insight on model parameters. Parameter selection of such models is however a challenging and potentially time-consuming task.In this work, we estimate the earliest activation sites governing the cardiac electrical activation. Specifically, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify their locations and times. 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 functional, which in the case of GEASI is the quadratic mismatch to given activation measurements. This versatile approach can be extended for computing topological gradients to estimate the number of sites, 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.

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“…The problem of fitting these parameters to clinical data has already been considered in the literature for the conduction velocities. 17 , 18 , 19 , 20 However, the optimization of EASs received limited attention so far with only a few works dealing with activation onsets 21 or locations. 22 The simultaneous optimization of EASs (especially their number) and conduction velocity has been analyzed only very recently.…”

Section: Introductionmentioning

confidence: 99%

GEASI: Geodesic‐based earliest activation sites identification in cardiac models

Grandits

Effland

Pock

et al. 2021

Numer Methods Biomed Eng

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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.

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Integration of activation maps of epicardial veins in computational cardiac electrophysiology

Cited by 15 publications

References 55 publications

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function

GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models

GEASI: Geodesic‐based earliest activation sites identification in cardiac models

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