Processing and analysis of cardiac optical mapping data obtained with potentiometric dyes

Abstract

Optical mapping has become an increasingly important tool to study cardiac electrophysiology in the past 20 years. Multiple methods are used to process and analyze cardiac optical mapping data, and no consensus currently exists regarding the optimum methods. The specific methods chosen to process optical mapping data are important because inappropriate data processing can affect the content of the data and thus alter the conclusions of the studies. Details of the different steps in processing optical imaging data, including image segmentation, spatial filtering, temporal filtering, and baseline drift removal, are provided in this review. We also provide descriptions of the common analyses performed on data obtained from cardiac optical imaging, including activation mapping, action potential duration mapping, repolarization mapping, conduction velocity measurements, and optical action potential upstroke analysis. Optical mapping is often used to study complex arrhythmias, and we also discuss dominant frequency analysis and phase mapping techniques used for the analysis of cardiac fibrillation.

Fig. 1.

Overview of cardiac optical mapping. A: experimental setup and data acquisition. The heart stained with a voltage-sensitive dye emits fluorescent light when excited by the excitation light. The emitted fluorescent light is filtered and detected, and the data stored as a series of matrixes of fluorescent intensity at different time points. B: data processing and analysis. Following acquisition, the raw data undergo processing, including data masking (1), digital filtering (2), and drift removal. The processed optical signals are then used for electrophysiological analyses (3).

Fig. 2.

Data masking. Pixels over myocardial tissue (foreground) are segmented from the pixels in the background, which are removed. A: thresholding method for data masking. The background image is converted into a 256-bit grayscale image, and a grayscale threshold is selected and applied. Pixels above the threshold (white) are retained, and those below threshold (black) are rejected. Nonmyocardial pixels in the thresholded image (in this example, a stimulating electrode, blue circle) are removed using a connected component analysis. B: edge-detection algorithms for data masking. Edges are detected using specific algorithms, and these edges are then dilated to create an enclosed object. Enclosed regions (blue circle) are then filled with a flood algorithm, and further dilation or erosion is performed as required.

Fig. 3.

Spatial filtering (binning). A: example of 3 × 3 binning. The fluorescence intensity of each pixel (F_i,j) is averaged with its 8 immediate neighbors to obtain its filtered or binned value. This operation is done for all the pixels in the matrix. B: fluorescence intensity map and the optical action potential (AP) from a single pixel for raw unfiltered data. C: fluorescence intensity map and optical AP (OAP) following 3 × 3 binning. Binning has removed some of the high-frequency noise from the OAP but creates spatial broadening, where a spatial averaging effect is seen on the binned fluorescent intensity map. D: increasing spatial broadening effect with greater bin sizes.

Fig. 4.

Power spectra for representative OAPs from 3 experimental species. The spectra for mouse (A), rabbit (B), and human (C) OAPs during a regular paced rhythm are shown. The dominant frequencies (f_d) for each of these examples correspond to the pacing frequency (the reciprocal of the pacing cycle length). The power spectrum for an example of rabbit ventricular fibrillation (VF; D) is also shown. All the major frequency peaks are below 100 Hz, suggesting that low-pass filters that attenuate frequencies above 100 Hz are suitable for processing optical mapping data.

Fig. 5.

Overview of temporal filters. A: ideal lowpass filter (“brickwall” filter) is a filter that attenuates all frequencies in the stopband and passes all frequencies in the passband with no phase delay. B: magnitude (solid lines) and phase (dashed lines) responses for 6 common lowpass infinite impulse response (IIR) and finite inpulse response (FIR) filters (n denotes the order of the filter). Of these 6 examples, the 50th-order FIR filter is closest in its magnitude and phase response to the ideal filter shown in A. C: because IIR filters impart a nonuniform phase delay as shown in the phase responses in B, bidirectional filtering is required to produce zero phase delay. D: instability in higher-order IIR filters. The magnitude responses for a 5th- and 25th-order Butterworth filter are shown (top). The instability of the 25th-order filter creates a rippling effect on the OAP plateau, which may be mistaken for afterdepolarizations.

Fig. 6.

Examples of overfiltering and appropriate filtering. A: blurring effect of moving average filters. Inset: the greater the number of time points averaged using the moving average filter, the greater the blurring effect on OAP upstroke. The moving average filter prolonged the rise time of the OAP, decreased maximum first derivative of the fluorescent signal (dF/dt_max), and can alter activation times. B: ringing with IIR filters. Reducing the passband frequency from 150 Hz (blue) to 75 Hz (red) creates a ringing effect, most prominent immediately before and after the OAP upstroke (arrows). These fluctuations resulting from overfiltering should not be mistaken for early or delayed afterdepolarizations. Mag, magnitude. C: appropriate filtering and filter testing. Windowed, uniform white noise (150–400 Hz) superimposed onto an ideal OAP (black) is to create an OAP with high-frequency noise (red). This result is then filtered to remove the high-frequency noise to produce the filtered OAP (blue). The amount of error created by the filter is calculated by quantifying the difference between the filtered OAP and the original ideal OAP.

Fig. 7.

Drift removal and normalization. A: background drift from the raw data (black) is removed by fitting a polynomial equation to the background drift (a 4th-order polynomial in this example, dashed red line), and subtracting that from the raw signal to remove drift (cyan). B: OAPs from all the pixels are normalized to the same amplitude so that peak fluorescence (F = 1) corresponds to a depolarized membrane potential and baseline fluorescence (F = 0) corresponds to the resting membrane potential (V_m).

Fig. 8.

Analysis of optical mapping data. A: activation mapping. The OAP upstrokes and their corresponding dF/dt traces for 4 points on a rabbit heart. Each point is assigned an activation time (t_act) based on the time of dF/dt_max to generate the isochronal activation map. B: Action potential duration (APD) measurements. APD₅₀ is measured from t_act to time of 50% repolarization, whereas APD₈₀ is measured to 80% repolarization. APD maps demonstrating regional heterogeneities are shown (right). C: conduction velocity analysis. Data are taken from the activation map (left) to fit onto a smoothed surface (middle). The surface was smoothed with a 5 × 5 Gaussian kernel. Local conduction vectors are then calculated from the smoothed activation surface with the magnitudes of local conduction velocity represented by the length of the arrows and the directions of activation represented by the directions of the arrows.

Fig. 9.

Arrhythmia analysis. A: Hilbert transform (HT). To calculate instantaneous phase, an HT is first performed. A, left: optical transmembrane voltage signal (black) and its corresponding HT signal (gray). The HT values are then plotted against the optical signal values on the imaginary (Im) and real (Re) axes of a phase space plot respectively (right) and the instantaneous phase at a given time point is the angle θ. B: phase mapping. B, top: OAP recordings from 2 locations on a rabbit heart in VF (red and blue). B, bottom: corresponding phase maps, showing the instantaneous phase for all the pixels within the region of interest, at 3 time points. These sequential phase maps show rotation around a phase singularity (open circle), with the white arrows showing the direction of rotation. C: dominant frequency (DF) mapping. A DF map during rabbit VF is shown (left). Distinct areas with different dominant frequencies are present. The transmembrane voltage traces (middle) and power spectra (right) from a region with highest DF (12.7 Hz, red) and a second region with lower DF (8.8 Hz, blue) are shown.