# Spectral Analysis of Tissue Displacement for Cardiac Activation Mapping: Ex Vivo Working Heart and In Vivo Study

Jade Robert, Francis Bessière, Élodie Cao, Virginie Loyer, Emma Abell, Fanny Vaillant, Bruno Quesson, Stefan Catheline, Cyril Lafon

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## To cite this version:

Jade Robert, Francis Bessière, Élodie Cao, Virginie Loyer, Emma Abell, et al.. Spectral Analysis of Tissue Displacement for Cardiac Activation Mapping: Ex Vivo Working Heart and In Vivo Study. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2022, 69 (3), pp.942-956. 10.1109/tuffc.2021.3137989. hal-05338971

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https://hal.science/hal-05338971v1

Submitted on 30 Oct 2025

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# Spectral Analysis of Tissue Displacement for Cardiac Activation Mapping: Ex Vivo Working Heart and In Vivo Study

Jade Robert, Francis Bessière, Élodie Cao, Virginie Loyer, Emma Abell, Fanny Vaillant, Bruno Quesson, Stefan Catheline, and Cyril Lafon

## Abstract

Characterizing myocardial activation is of major interest for understanding the underlying mechanism of cardiac arrhythmias. Electromechanical wave imaging (EWI) is an ultrafast ultrasound-based method used to map the propagation of the local contraction triggered by electrical activation of the heart. This study introduces a novel way to characterize cardiac activation based on the time evolution of the instantaneous frequency content of the cardiac tissue displacement curves. The first validation of this method was performed on an ex vivo dataset of 36 acquisitions acquired from two working heart models in paced rhythms. It was shown that the activation mapping described by spectral analysis of interframe displacement is similar to the standard EWI method based on zero-crossing of interframe strain. An average median error of 3.3 ms was found in the ex vivo dataset between the activation maps obtained with the two methods. The feasibility of mapping cardiac activation by EWI was then investigated on two open-chest pigs during sinus and paced rhythms in a pilot trial of cardiac mapping with an intracardiac probe. Seventy-five acquisitions were performed with reasonable stability and analyzed with the novel algorithm to map cardiac contraction propagation in the left ventricle (LV). Sixty-one qualitatively continuous isochrones were successfully computed based on this method. The region of contraction onset was coherently described while pacing in the imaging plane. These findings highlight the potential of implementing EWI acquisition on intracardiac probes and emphasize the benefit of performing short time-frequency analysis of displacement data to characterize cardiac activation in vivo.

Key words —Cardiac activation, electromechanical wave imaging (EWI), intracardiac imaging, preclinical study, short time Fourier transform (STFT), ultrafast ultrasound.

## INTRODUCTION

Cardiac arrhythmias are characterized by electrical dysfunction of cardiac cells leading to abnormal heartbeats. A coordinated contraction pattern of cardiac cells triggered by their electrical activation ensures efficient blood pumping in the normal heart. Disturbance of this electrical activity might thus lead to contraction abnormalities and can impact the heart's ability to perform its role. In most severe cases, especially ventricular arrhythmias, serious functional and even life-threatening consequences might occur if not treated. Radio frequency catheter ablation (RFCA) is the clinical standard of care when an intervention is required. Currently, 3-D electroanatomical mapping is the most common interventional
method used to understand the underlying mechanism of each patient-specific arrhythmogenic behavior and thus guide the intervention [1], [2]. In focal arrhythmias, the exact location of arrhythmogenic foci must be detected and treated. However, electroanatomical mapping only provides surface mapping of cardiac activity, either at the endocardium or the epicardium. This approach is often insufficient to adequately describe the ongoing mechanism in deeper layers of the tissue and thus ensure efficient treatment, especially when considering ventricular arrhythmias [3]. Mapping nontolerated or nonsustainable arrhythmias is also challenging with the current method, as arrhythmogenic behavior only lasts a few heartbeats, which might not be enough for proper mapping [4]. These limitations occupy considerable time dedicated to cardiac activity mapping, and thus less time is allocated to the treatment itself during an electrophysiological intervention. Consequently, this could be the cause for RFCA's limited efficacy, as long-term follow-ups demonstrated a 40%-50% recurrence rate for the treatment of ventricular arrhythmias [5], [6].

Novel mapping techniques are currently under investigation to address this limitation. Electrocardiographic imaging (ECGI) is based on the reconstruction of epicardial electrical activity from noninvasive, high-density body-surface potential recordings [7], [8]. ECGI, however, requires precise registration with a 3-D geometrical scan and lacks accuracy, especially in the presence of scar tissue [9]. Techniques based on cardiac MRI have also shown promising results for tissue characterization in electrophysiology [10], [11]. However, these methods depict late gadolinium enhancement distribution and are limited to substrate analysis [12].

In recent decades, intracardiac echocardiography has been integrated into electrophysiology procedures to improve treatment guidance and efficacy while possibly reducing patient exposure time to radiation by providing high-resolution and real-time imaging of cardiac anatomy [13]. In the last few years, an increase in acquisition frame rate capacity capability with compounding methods has allowed the development of novel ultrasound-based methods to map short-lived events occurring during cardiac activation [14]. Using an acoustic approach during electrophysiology intervention is of great interest, as it would offer the possibility of mapping cardiac activity into the myocardium, which is not feasible with standard 3-D electroanatomical mapping. Tissue Doppler imaging (TDI) and clutter filter wave imaging (CFWI) are ultrasound-based methods based on tissue velocity analysis that can be used for cardiac activity characterization [15], [16]. TDI is already available in clinical scanners but is angledependent and has high computational costs [17]. CFWI is based on selective high-pass filtering of tissue velocities and has higher SNRs and lower computational costs than TDI [16], [18]. Cardiac activation is then visualized from the motion of the attenuated velocity band. Visualization of EW propagation thus requires prior knowledge of the velocities that are to be attenuated and user selection of the velocity threshold for filtering. Ultrafast acoustoelectric imaging is also an ultrasound-based method developed to measure cardiac activation [19]. This method combines ultrasound emissions and invasive electrical recordings to map cardiac current densities and has been applied to isolated rat hearts [20] and in vivo swine model [21].

Electromechanical wave imaging (EWI) relies on mechanical mapping of the cardiac activity derived from ultrafast ultrasound acquisitions. EWI allows tracking of local contraction propagation induced by electrical activation: the electromechanical wave (EW). Strain-based EWI involves deriving contraction maps from the timing of zero-crossing of interframe
strain computed from ultrafast ultrasound acquisitions. Strain-based EWI was shown to accurately describe cardiac activation in an in vivo animal model [22]. It has also been validated against electrophysiological measurements during sinus and paced rhythms [23], [24]. Angle independence was also demonstrated in healthy patients [25]. Strain-based EWI analysis also identified the region of early activation in clinical studies [26], [27]. However, using strain-based EWI requires derivation of displacement data that might be noisy especially in in vivo conditions. Drawing activation isochrones from strain data requires manual selection of a zero-crossing timing on strain curves plotted for hundreds of points, which is time-consuming and might lead to interoperator variability.

The frequency content of ultrasound-derived data has also been studied in the heart. The frequency content of the EW was indeed studied with electromechanical cycle length mapping [28]. In this methodology, the activation rate of the cardiac tissue is derived from the dominant frequency (DF) of the interframe strain computed over several heartbeats. A similar approach was also proposed to map the propagation of a DF of a long incremental strain signal in atrial flutter [29].

Early stages of EWI development were based on the analysis of interframe tissue displacement, from which interframe strain is derived [30]–[32]. In these studies, contraction propagation into the heart was successfully visualized based on the temporal evolution of myocardium interframe displacement. EWI based on interframe displacement cineloops also allowed accurate discrimination of endocardial from epicardial pacing and from sinus rhythm in a previous study on a working heart model [33]. However, it required a trained reader for cineloop interpretation. The interpretation could be even more challenging on more complex datasets, such as in vivo conditions. An objective representation of cardiac activation patterns also seems more suited to future clinical applications of EWI.

The motivation for this study thus originates from these previous studies, which demonstrated that active mechanical waves could be visualized by interframe displacement computation but required reader interpretation. As a result, this study aims to introduce a novel method of representing cardiac activation objectively by semiautomatically processing displacement-based EWI data. This study also proposes the first report of cardiac contraction mapping using an intracardiac echocardiographic (ICE) probe in vivo. The objectives of the study are, first, to assess whether the introduced processing method can accurately retrieve contraction patterns in a controlled, isolated working heart setup and, second, to evaluate the feasibility of visualizing the source of contraction during in vivo conditions with an intracardiac probe based on the same processing method.

## MATERIALS AND METHODS

The novel processing method was first tested on an ex vivo dataset published by our group [33]. The dataset was first processed as in our previous study, and further processing steps were then applied, as described below. EWI results were compared against strain-based EWI to validate the resulting cardiac activation maps. The ex vivo setup allowed controlled yet physiological experimental conditions, with known outcomes. This high-quality ex vivo dataset was thus suited to initially test the algorithm. Cardiac activation patterns were then computed, based on this method, from data acquired on an in vivo swine model. An in vivo study was set up to evaluate the feasibility of the method under interventional conditions.

### A. Ex Vivo Experimental Protocol
The ex vivo dataset comprises ultrafast ultrasound acquisitions from an isolated working heart model during paced rhythm. This model offers physiological behavior comparable to in vivo behavior and is particularly suited for electrophysiologic testing [34]. The methods used in this experiment were extensively described in our previous study [33] and are very similar to the in vivo protocol. Briefly, pacing electrodes were sutured on the left ventricle (LV) epicardium and endocardium, facing each other. An extracorporeal probe operating at 15-MHz central frequency was positioned to ensure acoustic coupling throughout the entire cardiac cycle while minimizing the constraint on cardiac wall movement. Ultrasound acquisitions were performed at 4000 fps for 200 ms (800 images) and were synchronized with local electrical activity recorded near the pacing region. The probe was positioned so that the imaging field of view (FOV) was slightly shifted compared to the epicardial lead position, as insertion of a lead between the transducer and the tissue would have compromised the acoustic coupling. FOV included the endocardial lead. The dataset consisted of 36 acquisitions centered on different regions of the LV of two working hearts during either epicardial or endocardial pacing (18 acquisitions for each pacing type). Each imaging plane was located along the short axis of the LV.

## B. In Vivo Experimental Protocol

1) **Animal Model:** The animal protocol was approved by the local Animal Research Ethics Committee CEEA50 and the "Ministère de l'Enseignement Supérieur et de la Recherche" according to the European rules for animal experimentation (APAFIS #28022-202011171630690). Pigs (Large White Landrace, 40 kg, n = 2) were premedicated with an intramuscular injection of ketamine (10-20 mg/kg), acepromazine (0.1 mg/kg), and buprenorphine (9 µg/kg). Anesthesia was induced using an intravenous propofol bolus of 1-2 mg/kg and maintained with isoflurane while ventilation (air/oxygen 50/50) was ensured after intubation. ECG, arterial pressure, temperature, and oxygen saturation were monitored throughout the experiment. An anti-arrhythmic protocol was also set with continuous perfusion of lidocaine (7-10 mg/kg/h), amiodarone (1-2 mg/kg/h), and magnesium sulfate (20 mg/kg/h). Sternotomy was finally performed to expose the heart for lead placement.

2) **Ultrasound Probe:** A 64-element ICE probe with a 6.25-MHz central frequency and a 0.205-mm pitch integrated into a 9F catheter was used in this study. Two access points were used during this study to achieve LV wall imaging with the ICE probe. Probe insertion in the right ventricle through the inferior vena cava was facilitated by using a sheath, which also helped stabilize the probe position and thus the resulting imaging plane. The LV wall was quite deep in the imaging plane in this configuration. The aortic retrograde access allowed for the insertion of the ICE probe directly into the LV and thus bring the probe closer to the cardiac tissue. However, the sheath used in this study was too large to be inserted through this access point, and the stability of the probe position had to be manually maintained throughout all acquisitions. Fluoroscopy (Axiom Artis Scanner, Siemens Health Care, Erlangen, Germany) was used to guide ICE probe placement. Real-time B-mode imaging with the ICE probe itself was also performed using a research scanner (Vantage, Verasonics, Kirkland, WA, USA) to position the probe properly. The imaging FOV was a 60 sector with an approximately 6-cm depth to include part of the lateral or anterior wall of the LV.
**3) Pacing and Electrical Recording Leads Placement:** For each imaging position of the ICE probe, pacemaker leads (Tendril 52 cm, Abbott, Minneapolis, MN, USA) were screwed on the epicardium of the LV wall to trigger various cardiac activations by induction of controlled focal ventricular tachycardia. At least one lead was positioned in the imaging FOV guided by B-mode imaging of the ICE probe itself. A second lead was positioned at another position of the ventricle wall outside the imaging plane. Usually, this "out-of-plane" lead was sutured approximately 2-3 cm away from the "in-plane" lead. An example of lead placement is depicted in Fig. 1. Each lead was successively connected, through a wire, to a device controller (Merlin, Abbott, Minneapolis, MN, USA) for every imaging plane. Pacing parameters were set 0.2 V above the 3-V threshold to ensure local capture that would not jeopardize activation mapping within the LV wall. The duration of the stimulus was set to range between 1 and 1.5 ms. Sites were consecutively paced at least 20 bpm above the spontaneous sinus rhythm. Two electrodes were fixed at the surface of the heart, as shown in Fig. 1, to acquire a local bipolar signal using a differential amplifier (DAM 50, Word Precision Instrument, Sarasota, FL, USA) and recorded using an oscilloscope (PicoScope 3000, Pico Technology, St. Neots, U.K.). Bipolar local electrical activity was recorded near the imaged region (approximately 1 cm away from the in-plane pacing electrode).

**4) Ultrasound Acquisition:** All ultrafast ultrasound acquisitions were obtained with a programmable research scanner (Vantage, Verasonics). Acquisitions were performed for three positions of the ICE probe for the first swine, including a section of the LV. These positions were arbitrarily selected as long as they included part of the LV anterior or lateral wall, as no 3-D guidance was available. For the first two positions (P1 and P2), the ICE probe was inserted directly into the LV from the aorta. For the third position (P3), the ICE probe was inserted into the right atrium from the femoral vein. For the second swine, acquisitions were performed along two positions, both with the ICE probe inserted from the femoral vein into the right atrium for the first position (P4-5) and into the right ventricle for the second position (P6). For position P4-5, two pacing electrodes were screwed into the section of the ventricle wall being imaged in the current FOV. Acquisitions in sinus rhythm and out-of-plane pacing were consequently doubled in that position, hence the name P4-5 for this position. For all the other positions (P1, P2, P3, and P6), only one pacing electrode was included in the FOV. Sinus rhythm and out-of-plane pacing EWI acquisitions were obtained to provide "control" images and assess whether EWI for in-plane pacing can specifically identify pacing sources and thus differ from other types of EWI with different rhythms, either natural or stimulated.

For each imaging plane, acquisitions triggered by the local electrical recordings were obtained five times in sinus rhythm (without pacing) and five times for each pacing electrode (15 acquisitions per imaging plane). As there were two pacing electrodes in the FOV of P4-5, acquisitions in sinus rhythm or by pacing from the electrode outside the FOV were repeated ten times. To achieve ultrafast acquisitions, compounded diverging waves at five angles (−20°, −10°, 0°, 10°, 20°) were used. These compounding angles are standard optimized values in Vantage sequences for acquisitions with diverging waves. The acquisition frame rate was set to 2500 fps after compounding for P1 and P2 and 2000 fps for P3, P4-5, and P6. The imaging depth was set at approximately 6 cm (220–260 wavelengths). In summary, among the 90 acquisitions performed, 30 were achieved while pacing with the electrode included in the imaging FOV, 30 while pacing from outside the FOV, and 30 in sinus rhythm. All acquisitions performed are summarized in Table I.
### C. Data Processing

All steps of data processing are summarized in Fig. 2. Except for segmentation and electrical activity delimitation, all processing steps are performed automatically. Raw data will be defined as raw complex image-reconstructed data (so-called “IQ-data” in Vantage software). Verasonics Vantage software was used for RF data reconstruction.

1) **Preprocessing Steps:** B-mode images were reconstructed from raw data to define the region of interest for each acquired frame. Square-root compression was applied to the absolute value of the raw data. Manual segmentation was then performed for each B-mode dataset for a frame located within the electrical activity time window exhibiting a stable imaging plane (no significant cardiac tissue movement). Some noisy data required B-mode cineloop analysis to perform segmentation. For ex vivo data, tissue regions whose displacements were constrained by the probe were excluded from the segmentation. For in vivo data, as probe stability was not always guaranteed, a reproducibility metric was computed on segmentation masks. The 2-D cross-correlation coefficient was computed for each pair of masks among all acquisitions at one probe position. For each position, the average 2-D crosscorrelation coefficient was then computed. Time windows of interest were manually defined for each acquisition by selecting the period that included local electrical activity on the recordings. Axial interframe displacement maps were computed using a phase-tracking algorithm [35], [36] from the raw data and were registered with local electrical recordings. It is here assumed that influence of the tissue rigid motion on the displacement measurement is negligible, as the acquisition is performed locally at the first milliseconds right after the contraction onset.

2) **Short-Time Fourier Transform (STFT) of Interframe Displacement:** For each pixel of cardiac tissue in the imaging plane, the time evolution of the instantaneous frequency content of its displacement was assessed using a STFT. A STFT of the 1-D-displacement signal was performed over a sliding window of 40 frames (=10 ms) for ex vivo data and 30 frames (=12 ms for P1 and P2 and =15 ms for P3, P4-5, and P6) for in vivo data. In other words, for each pixel, the interframe displacement signal was divided into equal, overlapping segments. A Fourier transform was then applied to each segment separately for all positions of the sliding window. The sizes of the sliding windows were chosen empirically as a trade-off between the temporal and frequency resolutions of the STFT. The minimum widow length to discriminate cardiac mechanical wave frequencies (100 Hz at highest) from higher noise frequencies was 10 ms. The first window for STFT was centered at the local onset of electrical activity, as manually determined on the electrical recordings. The window was then repeatedly shifted by three frames (=0.8 ms, overlap 93%) for ex vivo data and two frames (=0.8 ms for P1 and P2 and = 1 ms for P3, P4-5, and P6, overlap 93%) for in vivo data until reaching the end of the electrical activity, as depicted in Fig. 2(d), right panel.

3) **Time Evolution of Dominant Frequencies:** At each time window, the frequency spectrum of the interframe displacement signal segment was computed. For a given window, the frequency with the highest amplitude in the spectrum was retrieved for all pixels, as illustrated in the left panel [see Fig. 2(d)]. This frequency will be referred to as the “DF” in the
rest of the text. For each pixel, one could thus plot the variation of the DF with the position
of the sliding window for STFT analysis. This curve illustrates the variation of the
instantaneous DF as a function of the selected segment in the displacement curve, as shown
in Fig. 2(d). It was then considered that EW propagation at a given pixel could be indicated by
a peak value of the DF during the period of electrical activity.

Global motion of the heart occurs during the cardiac cycle due to the overall activation of the heart. During diastole and in late ventricular systole, large displacements are induced by cavity filling and blood ejection. The frequency content of these displacements is mainly related to the activation rate of the heart. The heart rate in our study was between 60 and 120 bpm, which represents 1–2 Hz. On the other hand, cardiac mechanical waves exhibit spectral components in the range of 20–100 Hz, such as during contraction. During early systole, the rationale for tracking the DF was thus based on this spectral distribution variation. It was thus assumed here that the appearance of higher DF (higher than 1–2 Hz), exhibited by short-term analysis, could be associated with local displacement harboring the spectral content of a cardiac mechanical wave, which could be attributed to EW propagation.

4) **Refining the Time Period of Interest Using DF Peaks Histogram:** Several DF peaks were observed for some pixels, as illustrated in Fig. 2(e), upper panel. Therefore, an additional processing step had to be implemented to refine the time period of interest during which the DF peak would be attributed to cardiac contraction. DF peak histograms were plotted for all pixels of the cardiac tissue to define the final period of interest. The bin containing the highest amplitude was considered the timing at which most of the tissue was contracting, as shown in Fig. 2(e), lower panel. EWs were indeed considered propagating into the cardiac tissue of the FOV when the majority of the pixels were experiencing a DF peak. The final time period of interest was set to 15 or 30 ms (for ex vivo and in vivo data, respectively) before the timing of contraction to visualize the initiation of cardiac contraction. In other words, the DF peak of a given pixel was considered representative of contraction only when occurring 15 or 30 ms before the timing of contraction of the majority of the cardiac tissue, at the earliest. This step was necessary to avoid selecting DF peaks that occurred before the EW, possibly caused by noise or other mechanical waves, for example. It is assumed that the DF peak selected corresponds to contraction activity because of the synchronization with local electrical activation of the imaged region.

5) **2-D Cardiac Activation Map: First DF Peak Timing Isochrone:** Finally, 2-D activation maps were computed from the DF curves into the refined time period of interest. The first time the DF reached a peak was defined as the contraction timing at a given pixel. In the rest of the study, the term "DF shift" will refer to the shift of DF to its peak value. 2-D activation maps were then obtained by plotting contraction timing isochrones, as shown in Fig. 2(f). The time origins for these DF-based isochrones were defined as the onset of local electrical activity as determined on the electrical recordings. On all isochrones, the cardiac tissue experiencing the earliest contraction is indicated by red pixels, whereas those experiencing the latest contraction are in blue. In other words, the alleged position of the contraction source is depicted in red. The electromechanical delay was measured between the onset of local electrical activity and the earliest mechanical activation time observed for in-plane pacing acquisitions.
6) **Comparison With Strain-Based EWI:** Strain-based EWI was implemented following methods described in the literature. The interframe strain was evaluated by computing the spatial gradient of the interframe displacement (4-mm 1-D-kernel). The strain field was then spatially filtered using a 2-D median filter (0.25 mm × 0.4 mm 2-D kernel). Finally, a temporal median filter was applied to remove high-frequency noise. Two hundred points were randomly selected, and the 1-D-interframe strain was plotted for each point. Random selection was performed to obtain an unbiased and uniform distribution. Contraction of the myocardium leads to radial thickening of the cardiac wall. Consequently, electromechanical activation timing was defined as the first negative-to-positive zero-crossing of the incremental strain curve (manually selected) [37], [38]. Manual selection was thus performed on a total of seven 200 strain-curve and was here facilitated by application on a known dataset. The final strain-based isochrone was then obtained by applying cubic interpolation on the 200 activation timings. The origin of time (t = 0) and color code of activation timing were defined similarly for DF- and strain-based isochrones.

## RESULTS

### A. Interframe Displacement and DF-Based Activation Isochrone on Ex Vivo Data

First, STFT was computed on data acquired with an extracorporeal probe on a working heart model. Fig. 3(a) shows interframe displacement of the cardiac tissue during endocardial pacing at different timings. Red regions indicate movement downward, away from the probe, whereas blue regions indicate upward movement of the cardiac wall. In this configuration, tissue contraction can thus be tracked by the transition of the displacement direction of the contracting tissue (blue to red). For this specific case, the onset of the EW appears at the endocardium and propagates toward the epicardium of the ventricle wall.

The same propagation pattern can be seen when considering the DF distribution at different time windows, as shown in Fig. 3(c). Immediately before contraction, most of the cardiac tissue exhibits very low DF. This corresponds to the end of the global motion of the tissue at the end of diastole. The cavity is filled with blood and reaches a maximal position indicated by a large displacement toward the probe. When an isovolumetric contraction starts at the endocardium, higher DF values appear locally and propagate toward the epicardium. This shift in spectral content corresponds to smaller displacements induced by local contraction propagation into the myocardium.

The corresponding contraction timing isochrone is represented in Fig. 5. The propagation pattern displayed on the isochrone is similar to that visualized using displacement cineloops. From the 36 acquisitions analyzed, 32 (89%) continuous contraction isochrones were automatically obtained. The four other isochrones displayed no continuous region and could thus not be representative of mechanical wave propagation. "Voids" in isochrones indicate regions where no DF peak was detected within the final time window of interest. All isochrones displayed propagation patterns matching those visualized by displacement cineloop readings.

### B. Comparison of Strain-Based and DF-Based Isochrones on Ex Vivo Data

Usually, in previously published EWI studies, contraction isochrones are derived from zero-crossing timings of interframe strain curves. Both methods were applied to the ex vivo
dataset to assess the validity of isochrone computation from the timing of the DF peak. In Fig. 4, the strain curve is plotted along with DF time evolution at a given pixel, indicated by a white dot in Fig. 5(a) and (b). The algorithm selected the second DF peak for contraction timing. The first one could result from another mechanical wave and was not within the final period of interest. The timing of the second DF peak also corresponds to the appearance of the contraction pattern displayed in Fig. 3, which confirmed this peak selection.

The zero-crossing timing of the strain matches the selected DF peak timing, as they are only 0.5 ms apart (23.5 vs. 24 ms, respectively) at the selected point. Isochrones obtained with both methods are displayed for epicardial and endocardial pacing in Fig. 5. The cardiac activation depicted displays similar patterns and timing for both pacing types. Across the 32 isochrones obtained from the DF peak, 31 qualitatively matched those obtained with zero-crossing strain when considering the contraction patterns. Amongst these 31 isochrones, an average median error of 3.3 ± 1.4 ms was obtained between DF-based and strain-based isochrones.

### C. In Vivo Intracardiac DF-Based EWI

Two access points were used during this study to achieve LV wall imaging with the ICE probe. Probe insertion in the RV through the inferior vena cava was facilitated by using a sheath, which also helped stabilize the probe position and thus the resulting imaging plane. The LV wall was quite deep in the imaging plane in this configuration. Aortic retrograde access allowed the insertion of the ICE probe directly into the LV and thus brought the probe closer to the cardiac tissue. However, the sheath used in this study was too large to be inserted through this access point, and the probe position had to be manually held to maintain stability throughout all acquisitions. Because of this issue, imaging planes acquired at the P1 position were largely different and could not be compared.

2-D cross correlation coefficients also supported this visual assessment. The average coefficients for P1, P2, P3, P4-5, and P6 were 49%, 66%, 90%, 81%, and 76%, respectively. It must be noted that these comparisons are made based on a manually segmented mask that, in essence, could not be perfectly reproducible. Moreover, even in stable conditions, the image planes are similar but never perfectly identical. With regard to the low reproducibility of the imaging plane of P1 and because cardiac tissue sometimes moved out of the plane, acquisitions at this position were consequently not considered for the rest of the study.

Therefore, the following results only concern the 75 remaining acquisitions from P2 to P6. STFT analysis was then performed on the in vivo dataset. Amongst the 75 remaining acquisitions, 61 (81%) continuous contraction isochrones were automatically obtained. The 14 other isochrones displayed no region with continuous pattern and could thus not be representative of an EW propagation. As for ex vivo data, "voids" in isochrones indicate regions where no DF peak was detected within the final time window of interest. Pixels whose displacement signals were not correlated with the neighboring pixels signals were also excluded, as they were most likely noise.

Figs. 6–9 show B-mode images for each ICE probe position (for P2, P3, P4-5, and P6, respectively), displaying the alleged position of the pacing electrode in the FOV and one isochrone obtained for each pacing method (sinus rhythm, pacing in FOV and outside FOV).
The imaging plane was thus less stable at P2 compared to the remaining probe positions, as can be observed between the isochrones and as attested by the average 2-D cross-correlation coefficients. During in-plane pacing, the resulting isochrones were quite different. The onset of mechanical activation can be observed near the pacing region on most of the isochrones obtained with this rhythm but closer to the endocardium than the epicardium, as depicted in Fig. 6(c). Sinus rhythm and out-of-plane pacing isochrones resulted in qualitatively more similar patterns, with cardiac activation onset near the endocardium, as shown in Fig. 6(b) and (d), respectively. During in-plane pacing, the cardiac contraction starts during the first half of the electrical activation period, whereas for out-of-plane pacing and sinus rhythm, the contraction starts in the second half and at the end of the electrical activation period, respectively. In the latter cases, the regions of first cardiac activation (reddest region) are broader than in-plane pacing.

At position P3, isochrones computed for each pacing protocol are similar for a given rhythm, and activation patterns vary with the pacing type. When pacing out-of-plane, the activation front seems to come from the left part of the imaged cardiac tissue and propagates along the cardiac wall, as depicted in Fig. 7(d). The onset of cardiac activation during sinus rhythm is localized at the endocardial level and propagates toward the epicardium and along the wall, as shown in Fig. 7(b). Isochrone of Fig. 7(c), computed for in-plane pacing, shows an unexpected contraction onset on the endocardium at the central part of the imaged cardiac tissue that propagates toward the middle layer of the cardiac wall. However, a delayed contraction front seems to originate at the epicardial level near the pacing site (light blue) and appears to propagate toward the middle layer of the cardiac wall. This position was the only one for which most of the in-plane pacing isochrones depicted a contraction onset located at the wrong position. This anomaly may result from improper positioning of the ICE probe toward the pacing electrode position or the influence of other simulated mechanical waves. For position P2, cardiac activation during in-plane pacing still occurs earlier in the period of local electrical activation than for sinus rhythm.

Isochrones obtained at position P4-5 during sinus rhythm, and in-plane pacing with electrode n°1 might seem similar, as represented in Fig. 8(b) and (c), respectively. Both depict early activation sites near the endocardial layer of the pacing site of electrode n1. However, the activation occurs earlier concerning local electrical activity onset for in-plane pacing than for sinus rhythm. Activation of the septal wall coincides with initial activation of the LV lateral wall during sinus rhythm, which is not the case for in-plane pacing with electrode n°1. During the latter, early epicardial activation near the corresponding pacing site can also be seen in some isochrones, as depicted in Fig. 8(c). All isochrones obtained while in-plane pacing with electrode n2 show localized cardiac activation at the epicardium near the corresponding pacing site, as observed in Fig. 8(d). Some isochrones obtained with this pacing, however, also show additional sites of early activation. Finally, continuous isochrones obtained during out-of-plane pacing depict a first global activation near the septal wall, as shown in Fig. 8(e).

For ICE position P6, leads for electrical recordings were sutured approximately 5 cm away from the imaged region, closer to the base of the heart, for improved signal quality. Consequently, the period of interest defined by local electrical activity was shifted 50 ms earlier (by considering an electrical propagation speed of 1 m/s) to match the processing at the other positions. Epicardial activation onset near the pacing site could be seen on some isochrones obtained during in-plane pacing, with propagation toward the endocardium and
along the cardiac wall, as shown in Fig. 9(c). The first activation observed during sinus rhythm also appears near the in-plane pacing site but occurs later. The wavefront, however, seems to originate from the left part of the imaging plane and then propagates along the cardiac wall, as shown in Fig. 9(b). No proper isochrone could be obtained during out-of-plane pacing, as it appears that the activation starts after the shifted period of electrical activation.

In total, when considering only the 25 acquisitions with in-plane pacing, 24 isochrones with continuous regions were obtained. Twenty (83%) resulted in isochrones with the onset of contraction accurately located at the pacing site. Moreover, they were qualitatively different from isochrones obtained while pacing out of the imaging plane or during sinus rhythm in terms of pattern or timing concerning the electrical activation period. Among these 20 isochrones, 1 depicted activation onset at the opposite layer (endocardium instead of epicardium), and 6 were not clear enough to determine the exact activation layer. Six isochrones also showed other early activation regions, probably caused by other mechanical waves. An average electromechanical delay of 21.9 ± 11.3 ms between electrical and mechanical activity onset was also observed during in-plane pacing.

## DISCUSSION

A novel processing method was introduced that represented cardiac contraction based on ultrafast ultrasound acquisitions. Isochrones were computed from the time evolution of the spectral content of displacement data, and cardiac activation times were derived from this analysis. The dataset of a previous ex vivo study on two isolated working heart models was first processed with this method to evaluate its pertinence. An in vivo study was then set up on two open-chest pigs to assess the feasibility of detecting the contraction onset location using the described processing method on ultrasound data acquired with an ICE probe.

### A. Initial Validation on an Ex Vivo Dataset

Analysis of a previously published dataset acquired on isolated working hearts provided valuable results toward validating the DF approach. First, the spatiotemporal evolution of the displacement DFs allowed us to retrieve propagation patterns similar to those observed with displacement cineloop analysis. Second, occurrences of DF peaks within the tissue allowed us to map cardiac contraction patterns displayed through DF-based isochrones. Propagation patterns matched those qualitatively described by readers from displacement cineloop analysis. Epicardial pacing could be distinguished from endocardial pacing patterns, as regions of earliest activation were coherent with the paced surface. Contraction propagation onset consistent with local electrical activation was also observed.

It was also shown that DF-based isochrones qualitatively and quantitatively matched conventional strain-based isochrones computed from manually selected timing of strain curve zero-crossing. Cardiac activation patterns were indeed similar, and a low average median error between both methods was found.

### B. DF-Based Versus Other Activation Characterization Methods

Displacement cineloop analysis has already allowed us to accurately distinguish paced from sinus rhythms and detect the surface of pacing [33]. However, the need for trained readers
is a significant drawback for this characterization method. Contraction isochrones based on frequency analysis bring no additional information than displacement cineloop analysis, as performed in our previous study. However, interpretation of the cineloop requires training and is thus more prone to interreader variability. With DF-based contraction isochrones, all information is summarized in a 2-D map and thus offers better readability of the same data and less subjectivity in its interpretation. No displacement movie visualization and thus interpretation is now required with DF-based isochrones. Only the scale of the display colormap remains to be adjusted by the reader.

Interframe displacement was chosen to map mechanical activity within the heart instead of the interframe strain, usually used in EWI studies. Major drawbacks for strainbased isochrone generation are noise enhancement by derivation of displacement data and manual selection of strain zero-crossing. Defining the appropriate filtering method and selecting the right zero-crossing point might be challenging, time-consuming, and requires training. Here, selection of several thousands of points was achieved by an untrained operator but on a known dataset. The same operation with no prior knowledge on the data would have been much more complicated, as several zero-crossings might occur. Despite automatization attempts [39], manual selection of the zerocrossing corresponding to contraction timing is still required in most strain-based EWI studies [22], [25]–[27]. Recently, a machine learning approach was proposed [40] to perform zero-crossing selection. However, such a method requires experienced operators for ground truth zero-crossing selection. Moreover, zero-crossing is only performed over a few spatial points, and interpolation is required to display a full strainbased isochrone within the cardiac tissue. Consequently, the majority of the collected data are not exploited. DF-based isochrone generation automatically attributes a contraction timing to all pixels within the segmented region. All acquired datapoints thus contribute to the final map, contrary to strainbased isochrones. Isochrones obtained from spectral analysis of interframe displacement could allow for a semiautomatic description of cardiac activation patterns without manually interpolating selected scattered activation timings.

### C. Parameters of DF-Based Analysis and their Influence

In this study, shifts of DF between 20 and 100 Hz were usually observed. However, one must keep in mind that the frequency resolution in the Fourier transform is limited by the small length and low sampling of the displacement segments, defined by the size of the STFT sliding window. Consequently, DF values are only rough estimations, despite spectral smoothing allowed by zero-padding the displacement signal segment before Fourier transform. The size of the sliding window was designed so that the resulting frequency resolution of the spectrum would be 100 Hz at the highest to discriminate the frequency components of mechanical waves. In this study, the shift of DF to a value in the 20- to 100-Hz range and the timing of occurrence of this DF peak are of interest, not the value of the DF itself. Consequently, the poor resolution of the spectrum does not compromise the resulting isochrone.

Consequently, the frequency range observed in the study remains consistent with the reported spectral content of passive and active mechanical waves propagating in the heart, triggered by the closure of cardiac valves [41], [42]. The spectral content of electrical cardiac activation (QRS signals) is also in the same range [43] as the frequencies found in this study for electromechanical activation.
There is a trade-off between temporal resolution and frequency resolution. Considerable overlap between consecutive windows will also allow STFT analysis but at a higher computational cost. As we are interested in visualizing shortlived events, relatively small and largely overlapping windows were chosen. The choice of the window function might also influence the time-frequency resolution and could be improved. Here, a boxcar window (no smoothing on the edges of the signal segment) was chosen for simplicity purposes, but the effect of the tapering window function on the resulting isochrones could also be studied in future work.

Another option to validate the DF accuracy, in in vivo or ex vivo beating heart models, would be to acquire ultrasound data over a long period (several heartbeats) and verify whether DF, computed on longer time windows, matches the pacing rate, as was done in other studies [28], [29]. Here, the duration of the performed acquisitions was not long enough to achieve such a study.

To define the final time period of interest before selecting the DF peak and selecting the contraction timing for isochrone generation, we chose to consider only DF peaks occurring 30 ms before the timing of majority DF peak occurrence throughout the cardiac tissue. The choice of this delay can be explained by the duration of the isovolumetric contraction, which is approximately 30–50 ms in the human heart [44], [45]. Thus, to observe the displacement of the cardiac tissue at the onset of the contraction, it was defined that the earliest displacement event, specifically attributable to contraction, should occur within the 30 ms before the timing at which most of the tissue was experiencing a DF shift peak. However, the robustness of this parameter remains to be evaluated and could be optimized in further studies. The extent of the FOV or the distance with electrical recordings could be considered for that purpose.

Finally, to fully characterize the effects of STFT-related parameters, a more straightforward experiment could be set up. A more controlled environment could allow precise assessment of DF-based possibility on wave tracking and source identification in various noise conditions. Parameters could thus be better optimized depending on the experimental conditions.

## D. Current Limitations of DF-Based Analysis

The analysis of the instantaneous spectral content is not discriminative of displacement induced by EW propagation. Indeed, both passive and active mechanical waves propagate into the myocardium [46] and both could induce a DF shift with similar frequency content. In our study, the critical point to detect EW-related activity is synchronization with local electrical activity. This result underlines the importance of recording local electrical activity close to the region of interest. Otherwise, contraction visualized by DF-based EWI might occur outside the period of electrical activity, which first coarsely defines the time period of interest for isochrone generation. As ultrasound acquisition is triggered by electrical activation, the EW could be missed if the local signal is incorrectly recorded. Moreover, rigid motion of the myocardium could also impede displacement measurement to visualize contraction. Here, it is assumed that acquisition synchronization with local electrical activity allows to neglect global motion impact on the first milliseconds of recordings, as only local contraction is first triggered by pacing. However, further efforts to consider global motion impact would be required to achieve mapping using displacement in larger FOV and thus longer duration.
In future applications, an actual electrophysiology catheter, or even a multifunctional
catheter combining ultrasonic transducers and electrophysiology sensors [48], may provide
local electrical recordings closer to the imaged tissue in the FOV and thus synchronize
ultrasound acquisitions. Another solution would be to record local electrical activity at a
given distance from the imaged region (with an electrophysiology catheter already used
during the clinical intervention, for example) and track the position of both catheters. The
electromechanical delay between the site of electrical recording and the imaged site could
thus be estimated to adapt the time window for spectral time-evolution analysis.

In parallel, other studies could be envisioned first to confirm active mechanical wave capture
and second to derive a specific marker of contraction. Such studies could use intraventricular
pressure measurement to correlate wave appearance with contraction, for example.

Another concern about this method could be the angle between ultrasound imaging and the
cardiac wall. As we are tracking omnidirectional waves, it is believed that the angle should
not affect the ability to identify the source of the wave itself. However, such considerations
could be validated in future studies.

Except for tissue segmentation and electrical activity period delimitation, all processing steps
for isochrone generation are automatic. Segmentation was performed manually for simplicity
reasons but could be automated. During the experiment, the B-mode cineloops
reconstructed for these ultrafast data allowed for cardiac wall identification, as the dynamic
nature compensated the display's poor image quality. However, during offline processing,
poor B-mode quality makes the segmentation more complex and thus hardly automatable.

Automatic cardiac tissue segmentation on echocardiography should be considered in future applications and is already being studied [47]. In our case, it would require implementing a high-quality B-mode acquisition sequence after the ultrafast acquisition, registration of these B-mode images with the B-mode reconstructed from ultrafast data, and implementation of a more complex algorithm, which would increase computational cost. However, automatizing the whole processing chain remains of great interest for future clinical applications and analysis of larger datasets. Parallelization may also accelerate data processing.

E. First in Vivo EWI Study With an ICE Probe

These first findings were obtained in an isolated heart model, where positioning of the
pacing electrode and ultrasound probe could be easily achieved. Toward clinical application
of EWI and the proposed processing method, in vivo trials were achieved with an ultrasound
probe intended for echocardiographic use. To date, all cardiac activation mapping based on
ultrasound acquisitions has been achieved using extracorporeal probes. These probes allow
for a large FOV but with limited resolution. This study described an in vivo trial of cardiac
activation mapping by EWI implemented on an ICE probe for the first time.

ICE probes operate at a higher frequency and would thus offer higher resolution than
extracorporeal probes. Higher resolution would allow more refined mapping within tissue
thickness and potentially offer precise localization of arrhythmogenic foci. The intracardiac
approach is also advantageous over the transesophageal approach for interventional
cardiology, as no general anesthesia is required. Risks associated with esophageal intubation,
such as injury to the gastrointestinal tract, could also be avoided [48].

The purpose of this in vivo study was to determine whether the contraction source could be
detected when it was located in the imaging plane. On the 75 analyzed datasets, continuous
maps depicting realistic EW front propagation were achieved in 81% of the cases. Most discontinuous maps that could not be interpreted as a 2-D propagation pattern were obtained for sinus rhythm or out-of-plane pacing. Only one completely discontinuous map was obtained with in-plane pacing. Qualitatively similar activation patterns were usually obtained from consecutive acquisitions with the same rhythm.

DF-based activation isochrones computed for “in-plane” pacing showed encouraging results. Sources of contraction depicted by the isochrones indeed coarsely matched the pacing site. For example, the exact location of the pacing source was accurately retrieved, and no other zone of early activation compromised this source identification. Electromechanical delays observed for these acquisitions were consistent with delays observed in paced regions in the literature [24], [49]. In contrast, isochrones obtained in sinus rhythm or out-of-plane pacing systematically depicted mechanical activation onset at a different location than the pacing site or occurring later in the period of electrical activity.

This first in vivo feasibility study thus provided coherent and promising results, although further improvements of the method are required to allow a more precise and robust characterization method.

## F. Technical Limitations of the in Vivo Study

In this in vivo study, only the onset region of contraction depicted on isochrones could be compared with the paced region when performing in-plane pacing. No 3-D electrophysiological recordings were performed in this first in vivo feasibility study, as it would have complicated the experiment and data processing. Consequently, patterns obtained while pacing out of the imaging plane or during sinus rhythm could not be confirmed. Nonetheless, even if they were not validated, they were still different from in-plane pacing patterns. Moreover, strong concordance between the validated strain-based EWI and the novel DF-based EWI underlines the potential of this processing method.

To further validate this method in vivo, comparisons against established electrical activation mapping methods, such as 3-D electroanatomical mapping and sonomicrometry, could be envisioned, as was performed in other validation studies [18], [24]. However, this comparison would offer only partial validation, as no method exists to precisely map cardiac activation into the myocardium thickness.

The unclear or inaccurate location of the contraction sources observed could be explained by the inherent difficulty of in vivo intracardiac acquisitions with our probe. Difficulty in guiding and stabilizing the probe was indeed encountered, which led to a potential misalignment between the pacing site and the 2-D imaging plane. The positioning was guided only by low-resolution B-mode imaging and intermittent fluoroscopy between each probe position and not between each acquisition. B-mode images were reconstructed based on the ultrafast acquisition sequence with the compounding of only five diverging waves. B-mode quality could thus be easily improved by implementing a separate high-resolution imaging sequence for better guidance. For further improvement, magnetic tracking of the probe position and real-time fusion with 3-D anatomical imaging, such as CT scans or MRI, could be envisioned [50].

The limited stability of the probe also influenced the repeatability assessment of EWI on consecutive heartbeats. When inserting the probe in a sheath, one could benefit from its tension control knob to secure the probe position. However, this resulted in a catheter with a larger diameter than the ICE alone, and it was thus impossible to insert the sheath in the LV
from aortic access. In this study, the ICE probe used was a prototype. A more advanced
probe with an integrated control knob and better imaging capacities could offer more
imaging possibilities with better control of the probe position and thus better control of the
imaging plane stability and image quality.

Improving the insertion capacity and maneuverability of the ICE probe would also allow better image quality for deep tissues. The imaged LV walls were located further than 200 wavelengths away from the probe; thus, ultrasound signals were highly attenuated.

Achieving higher proximity with the tissue under study, in addition to guaranteeing imaging plane stability, would thus allow the acquisition of less noisy data. The frame rate could also be increased with higher proximity to allow greater spatiotemporal resolution for spectral analysis. Under such conditions, a more significant number of compounding angles could also improve data quality.

G. Perspectives

In future applications, EWI implementation on ICE probes could be suited to detect a source of contraction within the imaging plane by raster scanning the myocardium. Semiautomatic isochrone generation based on spectral content analysis could also be adapted for simple interpretation in a clinical situation. For example, in the context of focal arrhythmia treatment, multiple acquisitions along different imaging planes could be performed until focal contraction onset is observed, early on the period of local electrical activity. In more general cases, this method might be employed to characterize cardiac activation patterns in more complex arrhythmias, even though extended validation against standard methods would first be required.

Finally, EWI could benefit from recent advances in 3-D ultrafast ultrasound acquisitions [51]. With a sufficient frame rate and image quality, it may be possible to map the 3-D behavior of the contraction propagation [52], instead of being limited to a 2-D interpretation of a 3-D phenomenon, as this study is. 4-D EWI could open the path for a precise volumetric characterization method of cardiac activation. In shorter terms, performing multiple 2-D EWI acquisitions with an ICE probe could be guided with a 3-D spatial location like that already integrated into clinical ICE probes such as in the CartoSoundTM system (Biosense Webster, Inc., Diamond Bar, CA, USA).

CONCLUSION

A novel processing method to describe cardiac electromechanical activation was introduced in this study. This method relies on analyzing the evolution of the instantaneous spectral content of interframe displacement of cardiac tissue. It was demonstrated that DF-based isochrones obtained by this spectral analysis were qualitatively and quantitatively similar to the standard strain-based isochrones. A first in vivo trial of cardiac activation mapping by EWI using an ICE probe was also performed. Spectral analysis was also applied to these data, and promising results were obtained, as DFbased contraction isochrones correctly depicted the location of the source of contraction when the pacing electrode was in the imaging FOV, in most cases. These initial findings thus indicate that EWI based on spectral analysis of interframe displacement could have great potential for detecting the source of arrhythmias and that an intracardiac approach is feasible. Parameters of the spectral analysis should be evaluated for their robustness and could be optimized to achieve higher success rates in
cardiac activation mapping. Despite further improvements required in the processing method and probe guidance and the need for further validation, precise localization of arrhythmogenic foci could be explored in future applications.

## ACKNOWLEDGMENT

Jade Robert would like to thank Dr. Ivan M. Suarez Castellanos and American Journal Experts editors for proofreading the manuscript.

This work was supported in part by the French National Research Agency (ANR) under the CHORUS Grant Project ANR17-CE19-0017, in part by the France Life Imaging under Grant WP3 2019, and in part by the Focused Ultrasound Foundation. (Corresponding author: Jade Robert.)

This work involved animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Local Animal Research Ethics Committee CEEA50 and the "Ministère de l'Enseignement Supérieur et de la Recherche" under Application APAFIS #28022-202011171630690, and performed in line with the European rules for animal experimentation.

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<figure><img src="image_3.png" /><figcaption>Fig. 1. US acquisitions protocol. For all five imaging planes studied, five consecutive ultrafast ultrasound acquisitions were performed by stimulating the heart using successively the pacing electrode screwed in the imaging FOV (left), the one screwed outside the imaging FOV (middle) and during sinus rhythm without pacing (right). For position P4-5, acquisitions in sinus rhythm and by pacing outside the FOV were doubled as there were two pacing electrodes screwed into the imaging FOV. In total, 90 ultrafast datasets were acquired. Acquisitions were synchronized with local electrical activity through bipolar recordings. It must be noticed that this figure is only intended for illustration purposes of the experimental set-up. Multiple positions of the probe were used to achieve imaging of the anterior or lateral walls of the LV. In this configuration, lateral wall imaging can be achieved. Other articulations of the probe were achieved to acquired data on other regions of the LV. The “out-of-plane” pacing electrode and the electrical recordings leads were often not in the same 2-D plane than the imaging plane. Usually, the recording electrodes were approximatively 1 cm away from the “in-plane” electrode and the “out-of-plane” pacing electrode could be positioned anywhere on the LV, usually 2–3 cm away from the “in-plane” electrode.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 2. DF-based isochrones processing chain. (a) B-mode images are reconstructed from amplitude of IQ data and by applying square-root compression and an eightfold numerical gain. Manual cardiac tissue segmentation is performed on the reconstructed B-mode. (b) Acquired frames of ultrasound data are time-registered with the electrical signal recorded locally on the LV epicardium and period of electrical activity is manually delimited. (c) Local displacement of cardiac tissue is computed from raw data between each pair of consecutive frames with a phase-tracking algorithm [35], [36]. (d) STFT of the 1-D-displacement curve is computed on a temporal sliding window of 30 frames shifted throughout the whole period of electrical activity defined in (b), for each pixel of the cardiac tissue segmented in (a). For each pixel, time evolution of DF is computed by retrieving the frequency with maximum amplitude in the spectrum for each position of the window. (e) All DF peaks across the cardiac tissue are detected and a histogram with occurrence timing of these peaks is plotted. The beginning of the final time window of interest for contraction timing selection is defined 30 ms earlier than the timing at which majority of the cardiac tissue is experiencing a DF peak. (f) Contraction timing is then defined as the timing of occurrence of the first DF peak within the final time window of interest. A contraction isochrone is finally computed by affecting each pixel with its contraction timing. Origin of time is defined as the beginning of electrical activity defined on (b).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Comparison of displacement field and DF distribution at different timings during the period of local electrical activity. (a) Interframe displacement of the cardiac wall is displayed overlaid on B-mode image. This 2-D + time cineloop representation was used to analyze EW propagation in our previous study. (b) Local electrical activity recorded simultaneously with the ultrasound data. The timing at which the corresponding interframe displacement was computed is indicated with a red dot and position of the time-window for STFT of displacement curves and, thus for generating the corresponding DF map, is indicated in light blue. (c) 2-D representation of the DF distribution time evolution. Positions of the STFT windows were chosen to allow visual comparison with displacement field. Gray regions indicate pixels with 0-Hz DF at the given time-window. The propagation pattern of positive interframe displacement is similar to the propagation pattern of nonzero dominant frequencies, both indicative of the EW propagation.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

Fig. 4. Top: Comparison of time evolution of DF and interframe strain at the same point. The blue dot (t = 23.5 ms) indicates timing of negative-to-positive zero-crossing of the interframe strain curve, which is associated with contraction timing in strain-based isochrones. The orange dot (t = 24 ms) indicates DF peak selected by the algorithm, associated with contraction timing in DF-based isochrones. The first observable peak was not comprised in the refined time period of interest defined in Fig. 2(e), and could be attributable to other mechanical waves propagating. Bottom: Local electrical activity recording. Time origin is indicated by the red dashed line and corresponds to onset of electrical activity.
<figure><img src="image_7.png" /><figcaption>Fig. 5. Comparison of isochrone computation obtained from DF peak after spectral analysis (left) of interframe displacement versus zerocrossing of interframe strain (right). Activation isochrones were computed on both methods during either endocardial pacing (top row) or epicardial pacing (bottom row). Same origin of time was considered for DF-based or strain-based isochrone computation and was defined as onset of electrical activity. Color code is the same for all maps: regions in red are activated earlier whereas blue regions are activated later. Pacing sites are indicated by white stars. For epicardial pacing, the lead could not be included in the imaging plane and was positioned right next to the probe, for acoustic coupling reasons, hence the position of the star outside themap. The white dot indicates the position of the point chosen for interframe strain and DF time evolution display in Fig. 4.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 6. Cardiac activation isochrones computed from acquisitions with ICE probe at position P2. (a) Imaging FOV with LV lateral wall segmented in light yellow. Position of the pacing lead in the FOV is indicated by the red star. Resulting isochrones are displayed overlaid on B-mode image during (b) sinus rhythm, (c) in-plane pacing, or (d) out-of-plane pacing. Early activation site (red) matching electrode position can be seen for in-plane pacing. The contraction onset seems however closer to the endocardium than to the epicardium.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 7. Cardiac activation isochrones computed from acquisitions with ICE probe at position P3. (a) Imaging FOV with LV lateral wall segmented in light yellow. Position of the pacing lead in the FOV is indicated by the red star. Resulting isochrones are displayed overlaid on B-mode image during (b) sinus rhythm, (c) in-plane pacing, or (d) out-of-plane pacing. Contraction onset for in-plane pacing does not match pacing electrode position in that case.</figcaption></figure>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 8. Cardiac activation isochrones computed from acquisitions with ICE probe at position P4-5. (a) Imaging FOV with LV lateral and septal wall segmented in light yellow. Positions of the two pacing electrodes in the FOV are indicated by red stars. Resulting isochrones are displayed overlaid on B-mode image during (b) sinus rhythm, (c) in-plane pacing with electrode n°1, (d) in-plane pacing with electrode n°2, or (e) out-of-plane pacing. Early activation sites (red) matching electrode positions can be seen for the two in-plane pacing isochrones. The exact layer of contraction onset cannot be determined for electrode n°1. The contraction onset is accurately located at the epicardium for electrode n°2 but another region of early activation can be observed.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Fig. 9. Cardiac activation isochrones computed from acquisitions with ICE probe at position P6. (a) Imaging FOV with LV anterior and septal wall segmented in light yellow. Positions of the pacing electrode in the FOV is indicated by the red star. Resulting isochrones are displayed overlaid on B-mode image during (b) in-plane pacing with electrode or (c) sinus rhythm. Early activation site (red) matching electrode position can be seen for in-plane pacing. The onset is accurately located at the epicardium. No continuous isochrone was obtained for out-of-plane pacing within the time period of interest.</figcaption></figure>
<table><caption>TABLE I
Summary of in vivo ultrasound acquisitions for each ICE probe position</caption><thead><tr><th>ICE probe position</th><th>P1 (Pig1)</th><th>P2 (Pig 1)</th><th>P3 (Pig 1)</th><th>P4-5 (Pig 2)</th><th>P6 (Pig2)</th></tr></thead><tbody><tr><td>ICE insertion access</td><td>Aorta</td><td>Aorta</td><td>Inferior vena cava</td><td>Inferior vena cava</td><td>Inferior vena cava</td></tr><tr><td>LV region imaged</td><td>Anterior wall</td><td>Lateral wall</td><td>Lateral wall</td><td>Lateral wall</td><td>Anterior wall</td></tr><tr><td>Acquisition frame rate (fps)</td><td>2500</td><td>2500</td><td>2000</td><td>2000</td><td>2000</td></tr><tr><td>Imaging depth (λ/mm)</td><td>220/54</td><td>220/54</td><td>250/62</td><td>260/64</td><td>270/66</td></tr><tr><td>Nb. Of acquisitions in sinus rythm / in FOV / outside FOV</td><td>5 / 5 / 5 (failed)</td><td>5 / 5 / 5</td><td>5 / 5 / 5</td><td>10 / 10 (5 for each pacing lead) / 10</td><td>5 / 5 / 5</td></tr></tbody></table>