CoChat
Sign Up
Cited by 151Cardiac electrophysiology and arrhythmiasCardiac Arrhythmias and TreatmentsAnatomy and Medical Technology

The DOI matches its Crossref record; 191 of the 192 references it cites were found in a registry or an index.

Three-dimensional cardiac computational modelling: methods, features and applications.

Open access
Lopez-Perez, Alejandro, Sebastian, Rafael, Ferrero, Jose M·2015·BioMedical Engineering OnLine
DOI 10.1186/s12938-015-0033-5PMC PMC4424572
Open this paper in CoChat — free accountOpen PDF

Abstract

The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12938-015-0033-5

Cites

191 of 191 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Patient-specific bi-ventricular model. (a) In-vivo cardiac MRI slices showing manually segmented epicardial contour. (b) 3D cardiac model overlaid on the MRI stack. (c) Finite-element mesh with tri-cubic Hermite elements showing the main direction of fibre orientation at epicardium (yellow), midwall (green) and endocardium (purple). Reproduced with permission from [39]
Figure 1 Patient-specific bi-ventricular model. (a) In-vivo cardiac MRI slices showing manually segmented epicardial contour. (b) 3D cardiac model overlaid on the MRI stack. (c) Finite-element mesh with tri-cubic Hermite elements showing the main direction of fibre orientation at epicardium (yellow), midwall (green) and endocardium (purple). Reproduced with permission from [39]CC BY · PubMed Central
Figure 2 Highly-detailed rabbit bi-ventricular model. (a) Very high resolution ex-vivo MRI. (b) 3D rendering of the model showing a high level of anatomical detail. (c) Detail of tetrahedral finite-element mesh showing the papillary muscles (green) and chordae tendineae (blue). Adapted with permission from [46].
Figure 2 Highly-detailed rabbit bi-ventricular model. (a) Very high resolution ex-vivo MRI. (b) 3D rendering of the model showing a high level of anatomical detail. (c) Detail of tetrahedral finite-element mesh showing the papillary muscles (green) and chordae tendineae (blue). Adapted with permission from [46].CC BY · PubMed Central
Figure 3 Full pipeline to build a 3D cardiac computational model aimed at biophysical simulation. Summarised flowchart showing the main stages of the construction of a 3D cardiac model aimed at biophysical simulation: 3D cardiac geometry generation, meshing, CCS generation, myocardial structure generation, biophysical modelling (cardiac EP and biomechanics) and cardiac pathology modelling. Lines and arrows depict the relationships between the different stages by means of partial results (grey boxes) and coupling steps (yellow boxes). For pathology modelling, the diagram shows the different types (orange boxes) and subtypes (brown boxes) of cardiac pathology that can be included in a cardiac computational model and the stage in which each type of pathology must be taken into account.
Figure 3 Full pipeline to build a 3D cardiac computational model aimed at biophysical simulation. Summarised flowchart showing the main stages of the construction of a 3D cardiac model aimed at biophysical simulation: 3D cardiac geometry generation, meshing, CCS generation, myocardial structure generation, biophysical modelling (cardiac EP and biomechanics) and cardiac pathology modelling. Lines and arrows depict the relationships between the different stages by means of partial results (grey boxes) and coupling steps (yellow boxes). For pathology modelling, the diagram shows the different types (orange boxes) and subtypes (brown boxes) of cardiac pathology that can be included in a cardiac computational model and the stage in which each type of pathology must be taken into account.CC BY · PubMed Central
Figure 4 3D cardiac geometry generation stage of the development process of a 3D cardiac computational model. Diagram depicting the main alternatives to generate the 3D surface mesh that represents the cardiac geometry, showing the sources of anatomical information (blue boxes) and the methods (green boxes) with their possible options (brown boxes) used for this task, as well as the kind of model (orange boxes) obtained by each method.
Figure 4 3D cardiac geometry generation stage of the development process of a 3D cardiac computational model. Diagram depicting the main alternatives to generate the 3D surface mesh that represents the cardiac geometry, showing the sources of anatomical information (blue boxes) and the methods (green boxes) with their possible options (brown boxes) used for this task, as well as the kind of model (orange boxes) obtained by each method.CC BY · PubMed Central
Figure 5 Meshing stage of the development process of a 3D cardiac computational model. Diagram describing the most common methods (green boxes) and options (brown boxes) to build the 3D volumetric mesh of a cardiac model using the 3D surface mesh or the 3D segmented image resulting from the cardiac geometry generation as a starting point for the meshing process.
Figure 5 Meshing stage of the development process of a 3D cardiac computational model. Diagram describing the most common methods (green boxes) and options (brown boxes) to build the 3D volumetric mesh of a cardiac model using the 3D surface mesh or the 3D segmented image resulting from the cardiac geometry generation as a starting point for the meshing process.CC BY · PubMed Central
Figure 6 Myocardial structure generation stage of the development process of a 3D cardiac computational model. Diagram showing the main sources of structural information at tissue level (blue boxes) and the methods (green boxes) used to obtain the fibre orientation to be included in a 3D cardiac model.
Figure 6 Myocardial structure generation stage of the development process of a 3D cardiac computational model. Diagram showing the main sources of structural information at tissue level (blue boxes) and the methods (green boxes) used to obtain the fibre orientation to be included in a 3D cardiac model.CC BY · PubMed Central
Figure 7 Different methods to include the fibre orientation in 3D bi-ventricular models. Comparison between (a) rule-based method (Laplace-Dirichlet) and (b) DTI-based estimation of the myocardial fibre orientation for a 3D model of canine ventricles. Adapted with permission from [81].
Figure 7 Different methods to include the fibre orientation in 3D bi-ventricular models. Comparison between (a) rule-based method (Laplace-Dirichlet) and (b) DTI-based estimation of the myocardial fibre orientation for a 3D model of canine ventricles. Adapted with permission from [81].CC BY · PubMed Central
Figure 8 CCS generation stage of the development process of a 3D cardiac computational model. Diagram outlining several ways to generate a model of the CCS to be coupled to a 3D cardiac model. It shows the information sources (blues boxes), the partial results obtained (grey boxes) and the methods (green boxes) used to generate different kinds of model of CCS (orange boxes).
Figure 8 CCS generation stage of the development process of a 3D cardiac computational model. Diagram outlining several ways to generate a model of the CCS to be coupled to a 3D cardiac model. It shows the information sources (blues boxes), the partial results obtained (grey boxes) and the methods (green boxes) used to generate different kinds of model of CCS (orange boxes).CC BY · PubMed Central
Figure 9 Cardiac electrophysiology modelling stage of the development process of a 3D cardiac computational model. Diagram summarizing very briefly the main methods (green boxes) and options (brown boxes) to model the cardiac EP by means of EP models from EP data (blue box) provided by electrical activation maps at tissue level and patch-clamp experiments at cellular level.
Figure 9 Cardiac electrophysiology modelling stage of the development process of a 3D cardiac computational model. Diagram summarizing very briefly the main methods (green boxes) and options (brown boxes) to model the cardiac EP by means of EP models from EP data (blue box) provided by electrical activation maps at tissue level and patch-clamp experiments at cellular level.CC BY · PubMed Central
Figure 10 Elements and final application of a 3D bi-ventricular patient-specific computational model. (a) Geometry of human ventricles with highly detailed endocardium showing details such as the septal papillary muscle (red arrow) and the moderator band (black arrow) in the RV. (b) Fibre orientation from a rule-based algorithm. (c) Anatomical model of the CCS. (d) Infarct scar showing the core (red) and the BZ (blue). (e) FEM volumetric mesh with regular hexahedral elements. (f) Biophysical computational simulation of cardiac EP.
Figure 10 Elements and final application of a 3D bi-ventricular patient-specific computational model. (a) Geometry of human ventricles with highly detailed endocardium showing details such as the septal papillary muscle (red arrow) and the moderator band (black arrow) in the RV. (b) Fibre orientation from a rule-based algorithm. (c) Anatomical model of the CCS. (d) Infarct scar showing the core (red) and the BZ (blue). (e) FEM volumetric mesh with regular hexahedral elements. (f) Biophysical computational simulation of cardiac EP.CC BY · PubMed Central
Figure 11 Whole-heart cardiac atlas constructed from in-vivo high-resolution MS-CT of 138 human subjects. (a) Mean shape of the whole-heart atlas. (b) First two modes of variation at end diastole of the spatio-temporal whole-heart statistical atlas (μ represents the mean shape and √λ is the standard deviation). Adapted with permission from [52].
Figure 11 Whole-heart cardiac atlas constructed from in-vivo high-resolution MS-CT of 138 human subjects. (a) Mean shape of the whole-heart atlas. (b) First two modes of variation at end diastole of the spatio-temporal whole-heart statistical atlas (μ represents the mean shape and √λ is the standard deviation). Adapted with permission from [52].CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 192 references

Cited by

Sorted by citations

See all 151

Related

Metadata

Authors
Lopez-Perez, A., Sebastian, R., & Ferrero, J. M.
Affiliations
Universitat Politècnica de València (ES)
Corresponding author
Lopez-Perez, Alejandro
ORCID
Lopez-Perez, Alejandro
Venue
BioMedical Engineering OnLine
Publisher
Springer Science and Business Media LLC
Published
2015-04-17
Volume
14
Issue
1
Pages
35-35
ISSN
1475-925X (ISSN-L)
DOI
10.1186/s12938-015-0033-5
PMC
PMC4424572
OpenAlex
W2047658479
Cited by
151
References
192
Citations by year
2026: 7 · 2025: 5 · 2024: 9 · 2023: 15 · 2022: 20 · 2021: 19 · 2020: 21 · 2019: 20 · 2018: 14 · 2017: 9 · 2016: 10 · 2015: 2
FWCI
2.01
Type
journal article
Language
en
Open access
Licence
Locations
  • BioMedical Engineering OnLine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
Funders
  • European Commission
  • Ministerio de Economía y Competitividad (BES-2013, TIN2011-28067, TIN2012-37546-C03-01)
  • Generalitat Valenciana
  • European Regional Development Fund
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac Arrhythmias and Treatments
  • Physical Sciences › Engineering › Biomedical Engineering › Anatomy and Medical Technology
Keywords
patient-specific cardiac models, cardiac electrophysiology simulation, cardiac arrhythmias, image segmentation, mesh generation, model-based image analysis, biophysical simulation
MeSH terms
Animals, Biomechanical Phenomena, Cardiology, Computer Simulation, Decision Making, Computer-Assisted, Dogs, Electrophysiology, Heart Conduction System, Heart Diseases, Humans, Image Processing, Computer-Assisted, Models, Cardiovascular, Myocardium, Rabbits, Myocytes, Cardiac, Biophysical Phenomena, Precision Medicine, Cardiac Resynchronization Therapy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review