Contents

📒 Lopezperez 2015

Three-dimensional cardiac computational modelling: methods, features and applications1

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Introduction

  • Nowadays, 3D cardiac models are becoming increasingly complex and are currently used in other areas such as cardiac image segmentation, statistical modelling of cardiac anatomy, patient risk stratification or surgical planning

https://user-images.githubusercontent.com/40054455/86703723-bf6c2200-c046-11ea-9c08-7b7a846db893.png

Evolution of 3D models of cardiac anatomy

Generic models

  • Geometric models
  • anatomical models: the rabbit model from University of California San Diego and the canine model from University of Auckland
  • computer-aided design (CAD) tools

Medical image-based models

  • CT / MRI images => patient-specific models https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig1_HTML.jpg
  • Cardiac atlases
  • highly-detailed bi-ventricular models built from very high resolution ex-vivo MRI datasets (~25 μm per slice) from small mammalian hearts https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig2_HTML.jpg

Elements of a 3D cardiac computational model

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig3_HTML.jpg

Geometry

  • completeness and the anatomical realism and accuracy required by a particular 3D cardiac model will strongly depend on its final application
  • structurally simplified models (without endocardial details or vessels) are well suited for a large range of 3D cardiac modelling applications aimed at EP simulation
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig4_HTML.jpg
    3D cardiac geometry generation stage
  • Medical image-based models can include patient-specific details obtained from clinical imaging data and/or population-based properties collected from ex-vivo datasets
  • Ex-vivo cardiac images can provide much higher spatial resolution than in-vivo datasets
  • In-vivo images can provide both anatomical and temporal patient-specific information, thus enabling the characterisation of cardiac motion

Meshing

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig5_HTML.jpg

  • finite-element method (FEM) enabled the resolution of complex biophysical problems
  • tetrahedral, hexahedral, cubic Hermite elements
  • Spatial (ds) and temporal discretisation (dt) constraints are imposed when biophysical models are used, which are in the order of ds = 0.1-0.5 mm and dt = 0.05-0.005 ms
  • For the case of phenomenological models, such as Eikonal ones, spatial and temporal discretisation is less demanding (order of ds = 0.5 mm, dt = 1 ms), resulting in faster computation times.

Myocardial structure: fibre orientation

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig6_HTML.jpg

  • rule-based algorithms
  • measurements (histology, imaging)
  • Diffusion tensor-MRI (DT-MRI), also called diffusion tensor imaging (DTI): diffusion of water molecules within the biological tissues hhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig7_HTML.jpg

Cardiac conduction system

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig8_HTML.jpg

Electrophysiology

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig9_HTML.jpg

  • cellular-level equations and the tissue-level equations
  • Hodgkin and Huxley (HH) formalism vs Markov-type models (more mechanistic detailed)
  • EP models are now highly specific and include human atrial, ventricular and Purkinje cells in normal or diseased conditions
  • The number of state variables in the ionic AP models (and thus the number of ODEs) can be as high as 48

Electrophysiological heterogeneities

  • The ventricular wall is not homogeneous, as cardiac myocytes in different portions of the ventricles exhibit different ionic currents and APs.
  • epicardial-endocardial, apico-basal and left-right differences

Tissue level models

  • bidomain model: extracellular and intracellular domains: two partial differential equations (PDEs) + a set of ODE systems
  • equal anisotropy ratios are assumed in diffusion tensors: monodomain approach: one reaction-diffusion PDE plus a set of ODE systems
  • Highly computationally demanding esp. using FEM
  • Eikonal approximation, which replaces the reaction-diffusion equation with an eikonal equation that is simpler and based on a Huygens approach

Electromechanical coupling

  • Ca2+ cycling => EC coupling => myocyte shortening
  • Acute changes in ventricular mechanics can affect cardiac EP: stretch-activated ion channels, mechanical modulation of cell calcium handling

Pathology

  • functional remodelling: electrical / mechanical
  • Chronic or healed ischaemic injuries resulting from myocardial infarctions (infarct scars)
  • diffuse myocardial fibrosis

Example of a 3D cardiac computational model

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig10_HTML.jpg

Personalisation of 3D cardiac computational models

Applications of 3D cardiac computational models

Cardiac image segmentation

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4424572/bin/12938_2015_33_Fig11_HTML.jpg

  • model-based segmentation: in-vivo cardiac image segmentation and analysis
  • The Cardiac Atlas Project: a wide database of cardiac images available online

Simulation of acute ischaemia

Ablation of chronic myocardial infarction

Cardiac resynchronisation therapy

Reference


  1. Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng Online. 2015;14:35. Published 2015 Apr 17. doi:10.1186/s12938-015-0033-5. PMC4424572 ↩︎