Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies

Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University, MD, USA.
Journal of electrocardiology (Impact Factor: 1.36). 02/2009; 42(2):157.e1-10. DOI: 10.1016/j.jelectrocard.2008.12.003
Source: PubMed


The objective of this article is to present a set of methods for constructing realistic computational models of cardiac structure from high-resolution structural and diffusion tensor magnetic resonance images and to demonstrate the applicability of the models in simulation studies. The structural image is segmented to identify various regions such as normal myocardium, ventricles, and infarct. A finite element mesh is generated from the processed structural data, and fiber orientations are assigned to the elements. The Purkinje system, when visible, is modeled using linear elements that interconnect a set of manually identified points. The methods were applied to construct 2 different models; and 2 simulation studies, which demonstrate the applicability of the models in the analysis of arrhythmia and defibrillation, were performed. The models represent cardiac structure with unprecedented detail for simulation studies.

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    • "Figure 1 presents the generation of the model geometry from ex vivo MRI scans, the pipeline for which we have described previously (Vadakkumpadan et al., 2010b). Dilatation and ventricular wall thinning, as well as the changes in the fiber/ sheet architecture, that are associated with HF were incorporated naturally into the electromechanical model of the HF canine ventricles, via the image-based geometry (Vadakkumpadan et al., 2009; Gurev et al., 2011). Briefly, the electromechanical model is composed of two parts: an electrical component and a mechanical component. "
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    ABSTRACT: A methodology for the simulation of heart function that combines an MRI-based model of cardiac electromechanics (CE) with a Navier-Stokes-based hemodynamics model is presented. The CE model consists of two coupled components that simulate the electrical and the mechanical functions of the heart. Accurate representations of ventricular geometry and fiber orientations are constructed from the structural magnetic resonance and the diffusion tensor MR images, respectively. The deformation of the ventricle obtained from the electromechanical model serves as input to the hemodynamics model in this one-way coupled approach via imposed kinematic wall velocity boundary conditions and at the same time, governs the blood flow into and out of the ventricular volume. The time-dependent endocardial surfaces are registered using a diffeomorphic mapping algorithm, while the intraventricular blood flow patterns are simulated using a sharp-interface immersed boundary method-based flow solver. The utility of the combined heart-function model is demonstrated by comparing the hemodynamic characteristics of a normal canine heart beating in sinus rhythm against that of the dyssynchronously beating failing heart. We also discuss the potential of coupled CE and hemodynamics models for various clinical applications.
    Frontiers in Bioengineering and Biotechnology 09/2015; 3. DOI:10.3389/fbioe.2015.00140
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    • "led to the construction of population-based computational models of the distal section of the CCS in the ventricles (Behradfar et al., 2014; Deo et al., 2010; Sebastian et al., 2013; Walton et al., 2014). Among the techniques that have been used to model the geometry of the CCS there are fractal models (Abboud et al., 1991), manually delineated models (Romero et al., 2010), models guided by ex-vivo electrical recordings (Pollard and Barr, 1990), models guided by histological information (Sebastian et al., 2013), and models guided by high-resolution images in animals (Vadakkumpadan et al., 2009). The reader is referred to Sebastian et al. (2013) for a review on different techniques commonly used to build computer models of the CCS. "
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    ABSTRACT: The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia (VT). Nowadays, patient-specific activation times on ventricular chambers can be estimated from electro-anatomical maps, providing crucial information to clinicians for guiding cardiac radio-frequency ablation treatment. However, some relevant electrical pathways such as those of the Purkinje system are very difficult to interpret from these maps due to sparsity of data and the limited spatial resolution of the system. We present here a novel method to estimate these fast electrical pathways from the local activations maps (LATs) obtained from electro-anatomical maps. The location of Purkinje-myocardial junctions (PMJs) is estimated considering them as critical points of a distance map defined by the electro-anatomical activation maps, and then minimal cost paths are computed on the ventricular surface between the detected junctions. Experiments to validate the proposed method have been carried out in simplified and realistic simulated data, showing good performance on recovering the main characteristics of simulated Purkinje networks (e.g. PMJs). A feasibility study with real cases of fascicular VT was also performed, showing promising results.
    Medical Image Analysis 05/2015; 4(1):52-62. DOI:10.1016/ · 3.65 Impact Factor
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    • "Direct characterisation of tissue structure in whole hearts is also possible to some extent by high resolution ex vivo T2* enhanced MRI (Gilbert et al., 2012). Models of cardiac electrophysiology based on tissue structure distribution obtained from ex vivo DT-MRI acquired from a single subject (Vadakkumpadan et al., 2010), or statistical atlases based on ex vivo DT-MRI of multiple datasets (Relan et al., 2011), have been used to predict the evolution of arrhythmia in the infarcted rabbit (Vadakkumpadan et al., 2009) and human heart (Relan et al., 2011). Given that high-resolution DT-MRI studies are only possible ex vivo, solutions have been proposed to build patient-specific fibre models by warping fibre atlases onto patient-specific gross cardiac geometries (McDowell et al., 2013). "
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    ABSTRACT: Computational models have become a fundamental tool in cardiac research. Models are evolving to cover multiple scales and physical mechanisms. They are moving towards mechanistic descriptions of personalised structure and function, including effects of natural variability. These developments are underpinned to a large extent by advances in imaging technologies. This article reviews how novel imaging technologies, or the innovative use and extension of established ones, integrate with computational models and drive novel insights into cardiac biophysics. In terms of structural characterization, we discuss how imaging is allowing a wide range of scales to be considered, from cellular levels to whole organs. We analyse how the evolution from structural to functional imaging is opening new avenues for computational models, and in this respect we review methods for measurement of electrical activity, mechanics and flow. Finally, we consider ways in which combined imaging and modelling research is likely to continue advancing cardiac research, and identify some of the main challenges that remain to be solved.
    Progress in Biophysics and Molecular Biology 08/2014; 115(2-3). DOI:10.1016/j.pbiomolbio.2014.08.005 · 2.27 Impact Factor
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