Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations

Piersanti, Roberto; Africa, Pasquale Claudio; Fedele, Marco; Vergara, Christian; Dedè, Luca; Corno, Antonio F.; Quarteroni, Alfio

doi:10.1016/j.cma.2020.113468

Cited by 84 publications

(127 citation statements)

References 98 publications

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“…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%

“…The algorithms proposed in this paper have already provided the baseline of several works concerning the numerical simulation of the cardiac function. In Figure 17 we show some examples of these studies focused on different aspects of the heart modeling: Piersanti et al 91 proposed new methods for the cardiac fibers generation. In this work electrophysiology simulations on a four‐chambers cardiac geometry were performed, as shown in Figure 17A.…”

Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 99%

“…Some examples of numerical studies of the cardiac function performed on meshes generated with the tools proposed in this paper: (A) the electrical activation time of an electrophysiology simulation of the whole four‐chambers heart, see Piersanti et al 91 ; (B) the resulting displacement field at the end of systole of a left‐ventricular electro‐mechanical simulation, see Regazzoni et al 92 ; (C) the velocity field at the end of systole of a patient‐specific hemodynamics simulation of the systolic anterior motion of the mitral valve, see Fumagalli et al 28 …”

Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 98%

Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 99%

Section: Examples Of Mesh Generation Pipelinesmentioning

confidence: 99%

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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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“…Numerical solution would then be approximated on each vertex of this computational mesh using discretization techniques. Definition of model input parameters using clinical data : this task could be performed in a basic way, encoding simple mathematical relationships between clinical data and model parameters—for example, by adjusting electrical conductivities to reproduce conduction velocities or activation patterns, as shown in ref. 37, and adopting rule‐based fiber distributions 38,39 . This approach is not always successful, especially when considering detailed models with highly nonlinear input‐output relationships and limited experimental observations.…”

Section: Generation Of a Patient‐specific Modelmentioning

confidence: 99%

Data integration for the numerical simulation of cardiac electrophysiology

Pagani

Dedè

Manzoni

et al. 2021

Pacing Clinical Electrophis

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The increasing availability of extensive and accurate clinical data is rapidly shaping cardiovascular care by improving the understanding of physiological and pathological mechanisms of the cardiovascular system and opening new frontiers in designing therapies and interventions. In this direction, mathematical and numerical models provide a complementary relevant tool, able not only to reproduce patient‐specific clinical indicators but also to predict and explore unseen scenarios. With this goal, clinical data are processed and provided as inputs to the mathematical model, which quantitatively describes the physical processes that occur in the cardiac tissue. In this paper, the process of integration of clinical data and mathematical models is discussed. Some challenges and contributions in the field of cardiac electrophysiology are reported.

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“…Recently considerable advancements have been made in simulations of thermodynamic [4], biomechanic [5], and hemodynamic [6] states in living organisms. Mature simulation methods are now available to predict time-dependent changes in microscopic processes at the cellular level, as well as changes in macroscopic scales within one organ, their interactions with mechanical devices or the whole body.…”

Section: Opening Statementmentioning

confidence: 99%

Computer simulations can replace in-vivo experiments for implantable medical devices

2021

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Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations

Cited by 84 publications

References 98 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

Data integration for the numerical simulation of cardiac electrophysiology

Computer simulations can replace in-vivo experiments for implantable medical devices

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