Abstract:

Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and noninvasively reconstructed in a digitized model of the patient’s three-dimensional heart, which has led to clinical interest in ECGI’s ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI’s ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose ‘best’ practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique towards a useful role in clinical practice.

Abstract:

Activation times (AT) describe the sequence of car- diac depolarization and represent one of the most impor- tant parameters for analysis of cardiac electrical activ- ity. However, estimation of ATs can be challenging due to multiple sources of noise such as fractionation or base- line wander. If ATs are estimated from signals recon- structed using electrocardiographic imaging (ECGI), ad- ditional problems can arise from over-smoothing or due to ambiguities in the inverse problem. Often, resulting AT maps show falsely homogeneous regions or artificial lines of block. As ATs are not only important clinically, but are also commonly used for evaluation of ECGI methods, it is important to understand where these errors come from. We present results from a community effort to compare methods for AT estimation on a common dataset of simu- lated ventricular pacings. ECGI reconstructions were per- formed using three different surface source models: trans- membrane voltages, epi-endo potentials and pericardial potentials, all using 2nd-order Tikhonov and 6 different regularization parameters. ATs were then estimated by the community participants and compared to the ground truth. While the pacing site had the largest effect on AT cor- relation coefficients (CC larger for lateral than for septal pacings), there were also differences between methods and source models that were poorly reflected in CCs. Results indicate that artificial lines of block are most severe for purely temporal methods. Compared to the other source models, ATs estimated from transmembrane voltages are more precise and less prone to artifacts.

Abstract:

Determination of activation times (ATs) using noninvasive electrocardiographic imaging is a promising technique for future diagnosis in cardiology. However, recent studies showed artificial lines of block (ALBs) in AT maps, estimated from reconstructed source signals. Although a variety of different source models and estimation methods are used, few attempts have been made to compare these. For this reason, a systematic comparison was performed using three different source models (surface transmembrane voltages (TMVs), extracellular potentials (EPs) on an epi-endocardial surface, and EPs on a pericardial surface). Four different estimation methods were compared (deflection-based temporal, deflection-based spatiotemporal, correlation-based temporal, and correlation-based spatiotemporal). Four physiological cases with different pacings and 10 pathological cases with elongated scars and patches were taken into account. Monodomain simulations were performed and resulting body surface potentials were calculated and corrupted with an additive white Gaussian noise to obtain a signal to noise ratio of 20 dB. Reconstructions were performed using second-order Tikhonov regularization with 5 different degrees of smoothing and the L-curve-method to analyze the influence of the regularization. Subsequent AT estimation showed that TMVs performed better than EPs and showed fewer ALBs. Spatiotemporal approaches showed fewer ALBs than purely temporal ones. Deflection-based methods could depict scars, but showed many ALBs. Correlation-based methods performed better than deflection-based methods and did not show ALBs for TMVs, but overblurred scars for pathological cases. A modification of the correlation-based temporal method could be developed which resulted in the reproduction of scars without showing ALBs. In addition, it was shown that spatial oversmoothing in the reconstruction leads to characteristic ALBs and that this special kind of ALBs can be recreated by spatially smoothing true source signals. However, this does not explain all occurences of ALBs.