Computationally Efficient Noninvasive Cardiac Activation Time Imaging

Towards personalized clinical in-silico modeling of atrial anatomy and electrophysiology

Computational atrial models aid the understanding of pathological mechanisms and therapeutic measures in basic research. The use of biophysical models in a clinical environment requires methods to personalize the anatomy and electrophysiology (EP). Strategies for the automation of model generation and for evaluation are needed. In this manuscript, the current efforts of clinical atrial modeling in the euHeart project are summarized within the context of recent publications in this field. Model-based segmentation methods allow for the automatic generation of ready-to-simulate patient-specific anatomical models. EP models can be adapted to patient groups based on a-priori knowledge and to the individual without significant further data acquisition. ECG and intracardiac data build the basis for excitation personalization. Information from late enhancement (LE) MRI can be used to evaluate the success of radio-frequency ablation (RFA) procedures and interactive virtual atria pave the way for RFA planning. Atrial modeling is currently in a transition from the sole use in basic research to future clinical applications. The proposed methods build the framework for model-based diagnosis and therapy evaluation and planning. Complex models allow to understand biophysical mechanisms and enable the development of simplified models for clinical applications.

Single-beat noninvasive imaging of ventricular endocardial and epicardial activation in patients undergoing CRT

Background

Little is known about the effect of cardiac resynchronization therapy (CRT) on endo- and epicardial ventricular activation. Noninvasive imaging of cardiac electrophysiology (NICE) is a novel imaging tool for visualization of both epi- and endocardial ventricular electrical activation.

Methodology/Principal Findings

NICE was performed in ten patients with congestive heart failure (CHF) undergoing CRT and in ten patients without structural heart disease (control group). NICE is a fusion of data from high-resolution ECG mapping with a model of the patient's individual cardiothoracic anatomy created from magnetic resonance imaging. Beat-to-beat endocardial and epicardial ventricular activation sequences were computed during native rhythm as well as during ventricular pacing using a bidomain theory-based heart model to solve the related inverse problem. During right ventricular (RV) pacing control patients showed a deterioration of the ventricular activation sequence similar to the intrinsic activation pattern of CHF patients. Left ventricular propagation velocities were significantly decreased in CHF patients as compared to the control group (1.6±0.4 versus 2.1±0.5 m/sec; p<0.05). CHF patients showed right-to-left septal activation with the latest activation epicardially in the lateral wall of the left ventricle. Biventricular pacing resulted in a resynchronization of the ventricular activation sequence and in a marked decrease of total LV activation duration as compared to intrinsic conduction and RV pacing (129±16 versus 157±28 and 173±25 ms; both p<0.05).

Conclusions/Significance

Endocardial and epicardial ventricular activation can be visualized noninvasively by NICE. Identification of individual ventricular activation properties may help identify responders to CRT and to further improve response to CRT by facilitating a patient-specific lead placement and device programming.

Single-beat noninvasive imaging of cardiac electrophysiology of ventricular pre-excitation

OBJECTIVES

The aim of this study was to determine whether noninvasive imaging of cardiac electrophysiology (NICE) is feasible in patients with Wolff-Parkinson-White (WPW) syndrome in the clinical setting of a catheter laboratory and to test the accuracy of the noninvasively obtained ventricular activation sequences as compared with that of standard invasive electroanatomic mapping.

BACKGROUND

NICE of ventricular activation could serve as a useful tool in the treatment of cardiac arrhythmias and might help improve our understanding of arrhythmia mechanisms.

METHODS

NICE works by fusing the data from high-resolution electrocardiographic mapping and a model of the patient's cardiac anatomy obtained by magnetic resonance imaging. The ventricular activation sequence was computed with a bidomain theory-based heart model to solve this inverse problem. Noninvasive imaging of cardiac electrophysiology was performed in 7 patients with WPW syndrome undergoing catheter ablation of the accessory pathway. The position error of NICE was defined as the distance between the site of earliest activation computed by NICE and the successful ablation site identified by electroanatomic mapping (CARTO; Biosense Webster, Diamond Bar, California) for normal atrioventricular (AV) conduction as well as for adenosine-induced AV block.

RESULTS

The error introduced by geometric coupling of the CARTO data and the NICE model was 5 +/- 3 mm (model discretization 10 mm). All ventricular accessory pathway insertion sites were identified with an accuracy of 18.7 +/- 5.8 mm (baseline) and 18.7 +/- 6.4 mm (adenosine).

CONCLUSIONS

The individual cardiac anatomy model obtained for each patient enables accurate noninvasive electrocardiographic imaging of ventricular pre-excitation in patients with WPW syndrome. Noninvasive imaging of cardiac electrophysiology might be used as a complementary noninvasive approach to localize the origin and help identify and understand the underlying mechanisms of cardiac arrhythmias.

Application of the method of fundamental solutions to potential-based inverse electrocardiography

Potential-based inverse electrocardiography is a method for the noninvasive computation of epicardial potentials from measured body surface electrocardiographic data. From the computed epicardial potentials, epicardial electrograms and isochrones (activation sequences), as well as repolarization patterns can be constructed. We term this noninvasive procedure Electrocardiographic Imaging (ECGI). The method of choice for computing epicardial potentials has been the Boundary Element Method (BEM) which requires meshing the heart and torso surfaces and optimizing the mesh, a very time-consuming operation that requires manual editing. Moreover, it can introduce mesh-related artifacts in the reconstructed epicardial images. Here we introduce the application of a meshless method, the Method of Fundamental Solutions (MFS) to ECGI. This new approach that does not require meshing is evaluated on data from animal experiments and human studies, and compared to BEM. Results demonstrate similar accuracy, with the following advantages: 1. Elimination of meshing and manual mesh optimization processes, thereby enhancing automation and speeding the ECGI procedure. 2. Elimination of mesh-induced artifacts. 3. Elimination of complex singular integrals that must be carefully computed in BEM. 4. Simpler implementation. These properties of MFS enhance the practical application of ECGI as a clinical diagnostic tool.