Pigs (n = 3; 30–40 kg) were pre-medicated with acepromazine (0.1 mg/kg) and ketamine (20 mg/kg), anaesthetized with propofol (1 mg/kg) and maintained under isofluorane, 2%, in air/O₂ (50/50) after intratracheal intubation. The thorax was opened and heparin (2 ml) infused intravenously. 1.5 L of blood was collected during intravenous infusion of Voluven. Cardiac arrest was induced with cold cardioplegia introduced into the aortic root. The heart was rapidly excised and immersed in ice-cold Tyrode’s solution. The aorta was cannulated and the heart perfused in Langendorff mode with blood oxygenated with 95/5% O_2/CO₂, pH 7.4, temperature 37°C.

An epicardial electrode sock (108 electrodes) was attached to the heart (Figure 1A) and bipolar pacing leads were fixed to the right atria (RA) and ventricles with hooks (∼2 mm between electrodes tips). The left anterior descending artery (LAD) was freed above the first diagonal branch for a distance of 5 mm. A cannula was introduced through a small incision, and held in place with a ligature. The cannula was connected to the main perfusion system via a miniature heat exchanger. After instrumentation, perfusion was changed to 100% Tyrode’s solution containing (mM) NaCl, 128; NaHCO₃, 28; NaH₂PO₄, 0.5; MgCl₂, 0.7; glucose, 11; KCl, 4.7; CaCl₂, 1.5. The solution was oxygenated with 95/5% O_2/CO₂ and kept at pH 7.4, temperature 37°C. The heart was transferred to a human-shaped torso tank made from clear plastic with 256 electrodes embedded in the surface (Figure 1B).

The anterior epicardial surface was imaged using optical mapping through the chest of the torso tank (Figure 1C). Prior to imaging, the heart was mechanically-uncoupled using blebbistatin (15 μM), and stained with the voltage-sensitive dye, Di-4-ANBDQBS (10 μM). The epicardial surface was illuminated with monochromatic LEDs at 627 nm (Cairn Research Ltd., Kent, United Kingdom). Optical images (100 × 100 pixels) of signals passed through a 715 nm long-pass filter were acquired using a Micam Ultima CMOS camera (SciMedia USA Ltd., Costa Mesa, CA, United States) with a spatial resolution of 700 μm/pixel.

The heart was paced by 2 ms pulses at 2 Hz, with constant current amplitudes 2× the diastolic threshold, on either the left (LV) or right (RV) ventricular epicardial surface, mimicking ectopic activity, and during RA pacing representing normal sinus rhythm. Regional activation and repolarization heterogeneities were introduced through cooling then heating of the LAD perfusate to various temperature (min 21°C; max 40°C), as well as through local perfusion of Sotalol (10 mg/mL) from Sigma-Aldrich (Zwijndrecht, Netherlands). Recordings were taken in these different states during sinus rhythm and ventricular pacing. In total 55 different sequences were obtained across the three hearts.

Electrical and optical signals were measured simultaneously for each sequence. Tank and sock unipolar electrograms were recorded at 2 kHz (BioSemi, Netherlands) and referenced to a Wilson’s central terminal defined using tank electrodes. Optical mapping signals were acquired simultaneously at a frame rate of 1 kHz

3D angiographic fluoroscopy (Artis, Siemens) was used at the end of each experiment to obtain the exact location and orientation of the epicardium (mean edge length 4 ± 1 mm), perfusion beds and electrodes with respect to the tank (Figure 2A). To align the electrical and optical maps, a perspective projection based on a 3D camera position X_COP and a 3D focal center point X_FOC (Figure 2C) was used to project the torso and sock 3D electrode locations (E_3D) onto the 2D optical mapping frames (E^2D) (Figure 2B)

Where P is the projection matrix. The camera and focal points were first optimized by minimizing the mean Euclidean distance between projected electrodes positions (E^2D) and their true 2D locations (E_2D) visible in optical images (Figure 2C,D).

Where J is the cost function and n the number of electrodes used in the optimization. This proved to be insufficient due to error in identifying electrodes points in 2D and 3D, therefore a second optimization step was performed minimizing the mean absolute difference in activation times (AT) derived from sock electrodes and optical maps for a single activation sequence:

To validate the final projection, activation maps (that were not used in the optimization process) derived from sock electrograms and optical mapping signals were compared across all sinus rhythm and pacing sequences (methods described below). The same projection matrix was then applied on the epicardial and LAD geometries, to define their 2D locations in optical mapping window. As the posterior sock electrodes and epicardial surface were not visible in this window, they were removed for all comparisons.

Tank and sock channels in which signals were absent as a result of lead fracture or poor electrode contact were immediately evident on visual inspection and were discarded. Electrical signals were temporally aligned to optical maps by a square wave output generated by the optical mapping setup during camera operation that was recorded directly by the BioSemi system. Optical signals were filtered using spatial averaging (kernel 2.1 mm) and temporal averaging (kernel 1.5 ms). A data mask was defined by removing optical signals with an amplitude less than 20% of the maximum signal, removing unconnected components and using a dilation erosion technique to smooth the edges (Laughner et al., 2012). A multi-lead signal averaging algorithm was used to remove remaining noise in both electrical and optical recordings (Aström et al., 2000).

Electrocardiographic imaging electrograms were reconstructed from tank potentials to experiment-specific epicardial surfaces derived from fluoroscopy scans using the method of fundamental solutions (Wang and Rudy, 2006) with Tikhonov regularization (Tikhonov and Arsenin, 1977) and the CRESO method (Colli-Franzone et al., 1985b) to define the regularization parameter.

Activation/depolarization times (AT) were defined from recorded sock electrograms as the time of minimum derivative (dV/dT) of the intrinsic deflection, for optical action potentials as the maximum dF/dT during the action potential upstroke and for ECGI signals by fitting a global activation field to electrogram delays between electrograms (Duchateau et al., 2016). Recovery/repolarization times (RT) were defined from recorded sock and ECGI-derived electrograms as the time of maximum dV/dT of the T-wave, a widely used for experimental and clinical electrophysiological studies using unipolar electrograms (Coronel et al., 2009; Lux and Gettes, 2011). For optical signals, repolarization was defined as the time of minimum dF/dT, an index shown to closely match the maximum dV/dT of electrograms (Potse et al., 2009).

The minimum dV/dT of recorded electrograms has been shown to very closely match the maximum dV/dT of the action potential upstroke as measured from a floating micro electrode within 1 mm of the small unipolar electrode (Millar et al., 1985; Haws and Lux, 1990). Therefore, transformation and alignment of the 3D geometries into the 2D optical mapping frame were first validated by comparing recorded sock and optical activation maps during sinus rhythm and pacing. As with ATs, a relationship exists between RTs derived from unipolar electrograms and action potentials. RT recorded by sock electrodes were also compared their optical equivalents. Optical activation and repolarization maps were then used to validate ECGI reconstructions. Quantitative comparison of marker timings was performed by defining the nearest optical pixel to each sock electrode and heart mesh node using the Euclidean distance. Activation and RT at these locations directly compared using a root mean square error (RMSE) and Pearson’s correlation coefficient (CC).

Statistical analysis was conducted using GraphPad Prism 7.04. For each metric, the significance of differences was tested using paired t-tests with p < 0.05 defined as significant. Data are expressed as mean ± SD unless otherwise stated.

In order to use the developed experimental setup for ECGI validation, alignment of the 2D optical mapping window with the 3D epicardial mesh derived from fluoroscopy is critical. Many metrics used for ECGI validation are distance-based measurements, where error in the alignment of the “ground truth” would impact the result e.g., localization error of VT exit sites (Wang et al., 2016), premature ventricular contractions (Van Dam et al., 2009), focal discharges and rotor cores (Figuera et al., 2016). In addition, quantitative comparisons of action potentials or activation maps using correlation and RMSE could be dramatically affected by spatial inaccuracies.

In this study, we have developed and validated an optimization method to define the transformation and alignment of the 3D epicardial mesh into the 2D optical window based on markers visible in the both dimensions and one activation pattern derived from recorded sock electrograms and optical mapping (Figure 2).

Comparison of all optical and sock activation maps for each experiment demonstrated that this provides a reliable and robust transformation (Figure 3). On the other hand, repolarization maps showed a larger discrepancy in marker placement between optical and sock signals. As with activation markers, a relationship exists between RT derived from unipolar electrograms and action potentials. However, unlike with ATs this relationship is very sensitive to conditions (Steinhaus, 1989) and has experimentally shown differences in RTs with a standard error from 11 to 26 ms (Wyatt et al., 1981). One explanation for the difference seen here is the fact that the optical signals at the wavelengths used in this study represents an average over a significant depth (up to 4 mm) and lateral distance. Likewise, unipolar electrograms represent the integration of activity through the heart, and particularly the myocardial wall. Although the dF/dt max gives the “true” epicardial activation time [ADD REF to Walton et al. Biophys J 2012], there will still be some blurring, which could play a role in RT marker placement. It is unknown what effects transmural gradients in repolarization play on marker placement for these signals. The large differences seen might also simply be due to the increased inaccuracy in computation of repolarization compared to activation where small movement, ischemia, and signal noise affects the smoothness/duration of the T-wave resulting in larger errors in marker placement using automated algorithms. Futhermore, there exists several methods to compute RT from both optical action potentials and unipolar electrograms. While we have chosen indices that have been shown to correlate well from simulation studies (Potse et al., 2009) alternative indices, such as the max d²V/dT² typically used for optical mapping signals (Laughner et al., 2012), may result in a closer match.

Once the transformation and alignment were defined, ECGI reconstructed activation and repolarization patterns were validated using the optical maps. This is the first study to use optical mapping for the validation of ECGI reconstructions. Several previous studies have evaluated similar ECGI methods using epicardial sock recordings of various resolution to define the ground truth (Bear et al., 2018a,b). In a recent study using the same torso tank model presented here, ECGI was shown to reconstruct activation maps with a correlation of 0.68 ± 0.25 and RMSE = 13.4 ± 5.3 ms in LBBB (Bear et al., 2018a), comparable to the results seen using optical mapping as the reference. The only other studies to report quantitative accuracy of activation maps derived using potential-based ECGI methods have used in vivo experimental models. The first in dogs using a 103 non-uniformly spaced electrodes reported a mean correlation values of 0.82 (Cluitmans et al., 2017). The second in pigs using a 239-electrode sock array a mean correlation of 0.78 (Bear et al., 2018b). Despite the differences between these studies in spatial resolution for the ground truth recordings, the results are remarkably similar. This corroboration with previous ECGI validation studies provides further validation of the accurate alignment of the 2D and 3D mapping domains.

Very few studies have evaluated the reconstruction of RT using ECGI, and the only previous experimental study directly comparable to ours in terms of methodology was the study recently performed by Cluitmans et al. (2017) using the in vivo dog model. Like activation, despite validation being based on lower resolution ground truth data, the correlation values reported between measured and reconstructed repolarization maps match almost exactly to those seen in this study.

Rather than in the simple comparison of activation and recovery maps, the real benefit of high-resolution optical mapping for ECGI validation is seen in the ability to compare high-resolutions features. Here we have demonstrated one such application in the comparison of activation and repolarization gradients, which has never been previously attempted. It was demonstrated that ECGI reveals the gradients present in both activation and repolarization, although these could be under- and over-estimated, and in some cases shifted slightly in space. For activation maps, underestimation of the gradient may be a reflection of the algorithm used on ECGI signals for marker placement which spatially smooths the activation map. While this typically improves ECGI activation map reconstruction in normal hearts (Duchateau et al., 2016; Bear et al., 2018b), it may also smooth a line of conduction block into a region of slow conduction as seen in Figure 5. In contrast, the over-estimation of recovery gradients seen in Figure 7 is likely due to that same problem that produces artefactual jumps in AT that has previously been noted in ECGI validation studies (Duchateau et al., 2016, 2018). This data and experimental setup will help us to further develop new algorithms to improve activation and repolarization marker detection in the presence of heterogeneities.

In addition to repolarization gradients, this set up will be useful in the validation of ECGI for fibrillation, where an adequate spatial resolution is paramount to the accurate detection of focal sources and rotor cores (Roney et al., 2017); the optimization of parameter selection for ECGI algorithms, such as the choice of regularization parameter; and to evaluate ECGI formulations using transmembrane potentials as the cardiac source, which previously could not be achieved with post-processing of the signals.

The results presented should be considered in light of limitations inherent in the study. First, the field of view of the optical mapping window was limited to approximately 10 cm × 10 cm, only capturing the anterior surface of the heart. While validation of any ECGI algorithm would ideally compare the reconstructions for the entire cardiac surface used, typically the region of interest for high resolution would fit inside this window (e.g., regions of myocardial infarction), and lower resolution mapping of the rest of the heart can be achieved using a traditional electrode sock as has been used in this study.

Another limitation of optical mapping is the necessity of electro-mechanical uncoupling. This removes a condition that is present in clinical applications, and that might impact ECGI reconstruction accuracy particularly during repolarization. However, removing contraction is also an advantage as it enables one to assess the efficacy of ECGI reconstructions without motion artefact. Furthermore, methods are currently being developed to enable optical mapping to counteract motion artefacts, and these methods could be integrated into the system in the future. The use of optical mapping for ECGI validation is limited to using an ex vivo model and thus a torso tank with uniform isotropic electrical properties. This is not the case in vivo and can result in validation studies reporting better results than are seen using an in vivo model. However, like with removing contraction from the heart, torso tank models also allow us to assess the efficacy of a different ECGI formulation when the forward problem is accurately formulated.

Finally, the n number for this study is low, and does not allow insight into inter-heart variability.