Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy

Abstract

The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.

Fig. 1. Multiscale approach to image-based modeling of cardiac electrophysiology

Passive electrical coupling of cardiac cells mediates the tissue-scale propagation of bioelectric impulses that originate at the membrane level (action potentials). 3-D geometrical models are reconstructed from images. (Modified with permission from Trayanova et al. [43].)

Fig. 2. Constructing image-based models of the ventricles and the atria

A. Construction of a patient-specific ventricular model of arrhythmia from a clinical MR scan. Images are shown of an infarcted patient heart before ablation (treatment) and the corresponding segmentation: healthy (red), remodeled gray zone, GZ (green), or scar (yellow). An image of the 3-D geometric model of the patient heart rendered with the epicardium and the infarct border zone semi-transparent is shown in the third panel. (Modified with permission from Winslow et al. [2].) B. A model of the fibrotic human atria (right) generated from a patient LGE-MRI scan (left) following segmentation (middle) into normal and fibrotic tissue (fibrosis in green). (Modified with permission from Zahid et al. [52].) C–D. Fiber orientation in the human ventricles and atria, acquired by high-resolution ex-vivo diffusion tensor MRI. (Modified by permission from Pashakhanloo et al. [55,56].)

Fig. 3. Illustrative examples of VARP results for 7 of the 41 personalized heart models

Induced arrhythmia in two hearts are shown (top), for which geometrical models are presented together with transmembrane voltage and electrical activation isochronal maps, obtained following pacing from the site indicated. White arrows represent direction of propagation of the reentrant arrhythmias. The geometrical models of the five hearts, in which no arrhythmia was induced from any pacing site, are shown at the bottom. (Modified with permission from Arevalo et al. [45].)

Fig. 4. Locating reentrant drivers in the fibrotic substrate in human AF

A. Transmembrane potential maps during AF in a human atrial model. The locations of the reentrant drivers phase singularities and direction of propagation are indicated with purple circles and black arrows, respectively. B. The aggregate locations of reentrant drivers phase singularities for all AF episodes in this patient. Red circles correspond to the AF episode in A. (Modified with permission from Zahid et al. [52].)