Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies☆
Introduction
The primary cumulative goal of all cardiac research is a comprehensive understanding of the structure and function of the heart in health and disease. A key to achieving this goal is the integration of information obtained at various levels, from ion channels and calcium cycling to activation maps and regional strain distributions. Computational modeling provides a powerful tool to address this challenge and is thus becoming essential for the complete understanding of the heart. Computational approaches require finite element representations (or models) of the geometry and fiber architecture of the cardiac tissue.1 The cardiac architecture must be accurately acquired to construct realistic models. Recent advances in magnetic resonance (MR) imaging technologies have facilitated the acquisition of geometry and tissue architecture of the heart at very high spatial resolution. Modern anatomical MR scanners can image the cardiac histoanatomy of small experimental animals, such as rabbit, with an isotropic resolution in the order of 10−5 m.2 Advanced diffusion tensor (DT) MR equipments can measure the diffusivity of water in the tissue with a resolution in the order of 10−4 m.3 The primary eigenvectors of the DTs have been shown to be aligned with the prevailing cardiomyocyte orientations, commonly referred to as “fiber orientations.” Evidence also suggests that the secondary and tertiary eigenvectors are oriented normally to the main cell axes, in the myocardial laminar plane and perpendicular to it, respectively.
The objective of this article is twofold. First, we present a set of generic methods that we have developed for constructing detailed computational models of the heart from high-resolution structural and DTMR images. Second, we demonstrate the application of these models in the study of arrhythmia and defibrillation. The models that we present contain unprecedented structural detail, opening enhanced opportunities for modeling cardiac function. In the following, Section 2 presents the methods for generating whole-heart models, Section 3 presents the reconstructed whole-heart models, Section 4 describes the reconstruction of the Purkinje network, Section 5 reports results of electrophysiologic simulation studies, demonstrating the utility of image-based modeling in arrhythmia and defibrillation research, and Section 6 concludes the article.
Section snippets
Segmentation of the structural image
To generate image-based models of the heart, it is necessary to classify (or segment) the voxels in the structural MR image into different groups, such as normal tissue, diseased tissue (or infarct), background, and so on. Segmentation of high-resolution cardiac images is a very challenging task because of the complex geometry and topology of the myocardium, measurement noise, blurred object boundaries in the images, and the overlap of image intensities between different voxel groups. After
Data sets and whole-heart models
Models were generated from MR data sets of a normal rabbit heart and a rabbit heart with cardiac infarction. For the infarcted heart, both structural MR and DTMR images were acquired, at resolutions of 61 × 61 × 60 and 122 × 122 × 500 μm3, respectively. The infarct was 4 weeks old and induced by occlusion of the lateral division of the coronary artery.12 For the normal heart, only the structural MR image was acquired, at a very high resolution of 26.5 × 26.5 × 24.5 μm3. These data sets have
Purkinje system modeling
We have developed a methodology for reconstructing the Purkinje System (PS) from a high-resolution structural MR image and a finite element mesh representing the normal rabbit ventricles. Explicit reconstruction of the PS incorporates additional structural detail into the whole-heart model and allows the user to assign distinct electrophysiologic properties to the PS. For each slice in the image, branching points within the free-running PS (Purkinje-Purkinje junctions, PPJs) and points where
Simulation examples of defibrillation and arrhythmia in the generated models
In this section, we present 2 different electrophysiologic simulation examples using each of the 2 models presented in Section 3. The first study uses the infarcted rabbit model to investigate the influence of the infarct core on the formation of virtual electrodes at defibrillation shock-end. In the second study, the RV free wall extracted from the high-resolution normal rabbit model is subject to pacing stimuli to examine reentrant waves. These studies use cardiac models with unprecedented
Concluding remarks
This report presents a set of generic methods for reconstructing high-resolution 3D heart models from structural MR and DTMR images and demonstrates the utility of these models in the study of arrhythmia and defibrillation. The models presented in this article offer hitherto unavailable structural detail, heralding enhanced opportunities for modeling cardiac electrophysiology. In particular, our models contain fine details of the cardiac geometry such as endocardial trabeculations and the PS,
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This work was supported by NIH grants R01-HL063195, R01-HL082729, and R01-HL067322, and the NSF grant CBET-0601935 to NT, by the Mathematics of Information Systems and Complex Systems network and the Natural Sciences and Engineering Research Council of Canada to EV, and by a Marie Curie Fellowship MC-OIF 040190 and the Austrian Science Fund grant SFB F3210-N18 to GP. Furthermore, the authors thank the teams of Drs Kohl, Gavaghan, and Schneider at the University of Oxford for access to data from their 3D Heart Project, BBSRC grant E003443.