Ilan Lashevsky

Correspondence Between Simple 3-D MRI-Based Computer Models and In-Vivo EP Measurements in Swine With Chronic Infarctions

The aim of this paper was to compare several in-vivo electrophysiological (EP) characteristics measured in a swine model of chronic infarct, with those predicted by simple 3-D MRI-based computer models built from ex-vivo scans (voxel size <;1 mm3). Specifically, we recorded electroanatomical voltage maps (EAVM) in six animals, and ECG waves during induction of arrhythmia in two of these cases. The infarct heterogeneities (dense scar and border zone) as well as fiber directions were estimated using diffusion weighted DW-MRI. We found a good correspondence (r = 0.9) between scar areas delineated on the EAVM and MRI maps. For theoretical predictions, we used a simple two-variable macroscopic model and computed the propagation of action potential after application of a train of stimuli, with location and timing replicating the stimulation protocol used in the in-vivo EP study. Simulation results are exemplified for two hearts: one with noninducible ventricular tachycardia (VT), and another with a macroreentrant VT (for the latter, the average predicted VT cycle length was 273 ms, compared to a recorded VT of 250 ms).

A 3D MRI-based cardiac computer model to study arrhythmia and its in-vivo experimental validation

The aim of this work was to develop a simple and fast 3D MRI-based computer model of arrhythmia inducibility in porcine hearts with chronic infarct scar, and to further validate it using electrophysiology (EP) measures obtained in-vivo. The heart model was built from MRI scans (with voxel size smaller than 1mm3) and had fiber directions extracted from diffusion tensor DT-MRI. We used a macroscopic model that calculates the propagation of action potential (AP) after application of a train of stimuli, with location and timing replicating precisely the stimulation protocol used in the in-vivo EP study. Simulation results were performed for two infarct hearts: one with noninducible and the other with inducible ventricular tachycardia (VT), successfully predicting the study outcome like in the in-vivo cases; for the inducible heart, the average predicted VT cycle length was 273ms, compared to a recorded VT of approximately 250ms. We also generated synthetic fibers for each heart and found the associated helix angle whose transmural variation (in healthy zones) from endo- to epicardium gave the smallest difference (i.e., approx. 41°) when compared to the helix angle corresponding to fibers from DW-MRI. Mean differences between activation times computed using DT-MRI fibers and using synthetic fibers for the two hearts were 6 ms and 11 ms, respectively.

Analysis of Activation-Recovery Intervals from Intra-cardiac Electrograms in a Pre-clinical Chronic Model of Myocardial Infarction

Mapping of intracardiac electrical signals is a well-established clinical method used to identify the foci of abnormal heart rhythms associated with chronic myocardial infarct (a major cause of death). These foci reside in the ‘border zone’ (BZ) between healthy tissue and dense collagenous scar, and are the targets of ablation therapy. In this work we analyzed detailed features of the electrical signals recorded in a translational animal model of chronic infarct. Specifically, activation maps and bipolar voltages were recorded in vivo from 6 pigs at ~5 weeks following infarct creation, as well as 6 control (normal) pigs. Endocardial and epicardial maps were obtained during normal sinus rhythm and/or pacing conditions via X-ray guided catheter-based mapping using an electro-anatomical CARTO system. The depolarization and repolarization maps were derived through manual annotation of electro-cardiogram waves, where the peak of the QRS wave marked the time of depolarization and the peak of the T wave marked the recovery time. Then, at each recording point, activation-recovery intervals ARIs (clinical surrogates of action potential duration) were found by subtracting activation times from repolarization times. Overall, we observed that ARI values in the BZ have recovered from the acute stage and were close to values in healthy tissue. In general we observed a weak negative correlation between the activation times and ARI values, also not a significant variation (p < 0.5) between mean ARI values in the BZ area and those in the healthy areas.

Pacing Spikes All Over

Ventricular safety pacing (VSP) is used to avoid cross talk by delivering ventricular stimulus shortly after an atrial-paced event if ventricular-sensed event occurs. Although VSP is a protective feature that exists for decades in different pacing devices, there are some reports of unfavorable outcomes of this algorithm. More so, health care providers sometimes face difficulties in interpreting and dealing with VSP strips. This case report discusses an important pacemaker algorithm and encourages further attention to possible pitfalls and hence avoids unnecessary interventions.

A pre-clinical framework to characterize peri-infarct remodelling using in vivo t 1 maps and CARTO data

The purpose of this work was to use in vivo MR imaging and electro-anatomical maps to characterize dense scars and border zone, BZ (a mixture of collagen and viable fibers). To better understand how these measures might probe potentially arrhythmogenic substrates, we developed a preclinical swine model of chronic infarction and integrated in vivo MRI and electrophysiology (EP) data in five swine at 5-6 weeks post-infarction. Specifically, we first aligned and registered T1-maps (from MR studies) and bipolar voltage maps (from CARTO-EP studies) using Vurtigo, an open source software. We then performed a quantitative analysis based on circumferential segments defined in the short-axis of MR images. Our results demonstrated a negative linear relation between bipolar voltage maps and T1 maps within the first two mm of the endocardial surface. The results of our novel approach suggest that T1-maps combined with limited EP measurements can be used to evaluate the biophysical properties of healing myocardium post-infarction, and to distinguish between the infarct categories (i.e., dense scar vs. BZ) with remodelled electrical characteristics.