Detection of Regional Mechanical Activation of the Left Ventricular Myocardium Using High Frame Rate Ultrasound Imaging

High volume-rate echocardiography for simultaneous imaging of electromechanical activation and cardiac strain of the whole heart in a single heartbeat in humans

BACKGROUND

Imaging both electrical and mechanical cardiac function can better characterize cardiac disease and improve patient care. Currently, there is no noninvasive technique that can simultaneously image both electrical and mechanical function of the whole heart at the point of care. Here, our aim is to demonstrate that high volume-rate echocardiography can simultaneously map cardiac electromechanical activation and end-systolic cardiac strain of the whole heart in a single heartbeat.

METHOD

A 32x32 ultrasound matrix array connected to four synchronized ultrasound scanners were used for transthoracic high volume-rate imaging (840 volumes/s) in sixteen young volunteers (28.1±4.2 y.o.). An electromechanical activation map of the whole heart and volumetric end-systolic atrial and ventricular strain images were obtained.

RESULTS

The whole heart activation sequence was found to be consistent across volunteers and in agreement with previously reported normal electrical activation sequences. The mean electromechanical activation time was 72.6±15.2 ms in the atria, 132.4±19.7 ms in the ventricles and 154.5±19.6 ms in the whole heart. Volumetric right and left atrial as well as right and left ventricular strains were also consistent across all volunteers, with a mean end-systolic global longitudinal strain of 26.8±6.5% in the atria and -16.6±3.4% in the ventricles.

CONCLUSIONS

This initial feasibility study demonstrates that noninvasive high-volume rate imaging of the heart in a single heartbeat is feasible and can provide electromechanical activation and systolic strains simultaneously in all four cardiac chambers. This technique can be further developed and used at the point of care to assist for screening, diagnosis, therapy guidance and follow-up of heart disease patients.

A new Electromechanical Wave Imaging dispersion metric for the characterization of ventricular activation in different Cardiac Resynchronization Therapy pacing schemes

Conventional biventricular (BiV) pacing cardiac resynchronization therapy (CRT) is an established treatment for heart failure patients. Recently, multiple novel CRT delivering technologies such as His-Bundle pacing have been investigated as alternative pacing strategies for optimal treatment benefit. Electromechanical Wave Imaging (EWI), a high frame-rate echocardiography-based modality, is capable of visualizing the change from dyssynchronous activation to resynchronized BiV-paced ventricles in 3D. This proof-of-concept study introduces a new EWI-based dispersion metric to further characterize ventricular activation. Patients with His-Bundle device implantation (n=4), left-bundle branch block (n=10), right-ventricular (RV) pacing (n=10), or BiV pacing (n=15) were imaged, as well as four volunteers in normal sinus rhythm (NSR). EWI successfully mapped the ventricular activation resulting from His-Bundle pacing. Additionally, very similar activation patterns were obtained in the NSR subjects, confirming recovery of physiological activation with His pacing. The dispersion metric was the most sensitive EWI-based metric that identified His pacing as the most efficient treatment (lowest activation time spread), followed by BiV and RV pacing. More specifically, the dispersion metric significantly (p 0.005) distinguished His pacing from the other two pacing schemes as well as LBBB. The initial findings presented herein indicate that EWI and its new dispersion metric may provide a useful resynchronization evaluation clinical tool in CRT patients under both novel His-Bundle pacing and more conventional BiV pacing strategies.

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

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.

Electromechanical Wave Imaging With Machine Learning for Automated Isochrone Generation

Standard Electromechanical Wave Imaging isochrone generation relies on manual selection of zero-crossing (ZC) locations on incremental strain curves for a number of pixels in the segmented myocardium for each echocardiographic view and patient. When considering large populations, this becomes a time-consuming process, that can be limited by inter-observer variability and operator bias. In this study, we developed and optimized an automated ZC selection algorithm, towards a faster more robust isochrone generation approach. The algorithm either relies on heuristic-based baselines or machine learning classifiers. Manually generated isochrones, previously validated against 3D intracardiac mapping, were considered as ground truth during training and performance evaluation steps. The machine learning models applied herein for the first time were: i) logistic regression; ii) support vector machine (SVM); and iii) Random Forest. The SVM and Random Forest classifiers successfully identified accessory pathways in Wolff-Parkinson-White patients, characterized sinus rhythm in humans, and localized the pacing electrode location in left ventricular paced canines on the resulting isochrones. Nevertheless, the best performing classifier was proven to be Random Forest with a precision rising from 89.5% to 97%, obtained with the voting approach that sets a probability threshold upon ZC candidate selection. Furthermore, the predictivity was not dependent on the type of testing dataset it was applied to, contrary to SVM that exhibited a 5% drop in precision on the canine testing dataset. Finally, these findings indicate that a machine learning approach can reduce user variability and considerably decrease the durations required for isochrone generation, while preserving accurate activation patterns.

Stepwise local stitching ultrasound image algorithms based on adaptive iterative threshold Harris corner features

Herein, a Harris corner detection algorithm is proposed based on the concepts of iterated threshold segmentation and adaptive iterative threshold (AIT-Harris), and a stepwise local stitching algorithm is used to obtain wide-field ultrasound (US) images.Cone-beam computer tomography (CBCT) and US images from 9 cervical cancer patients and 1 prostate cancer patient were examined. In the experiment, corner features were extracted based on the AIT-Harris, Harris, and Morave algorithms. Accordingly, wide-field ultrasonic images were obtained based on the extracted features after local stitching, and the corner matching rates of all tested algorithms were compared. The accuracies of the drawn contours of organs at risk (OARs) were compared based on the stitched ultrasonic images and CBCT.The corner matching rate of the Morave algorithm was compared with those obtained by the Harris and AIT-Harris algorithms, and paired sample t tests were conducted (t = 6.142, t = 31.859, P < .05). The results showed that the differences were statistically significant. The average Dice similarity coefficient between the automatically delineated bladder region based on wide-field US images and the manually delineated bladder region based on ground truth CBCT images was 0.924, and the average Jaccard coefficient was 0.894.The proposed algorithm improved the accuracy of corner detection, and the stitched wide-field US image could modify the delineation range of OARs in the pelvic cavity.

Detection of Tissue Fibrosis using Natural Mechanical Wave Velocity Estimation: Feasibility Study

In the feasibility study described here, we developed and tested a novel method for mechanical wave velocity estimation for tissue fibrosis detection in the myocardium. High-frame-rate ultrasound imaging and a novel signal processing method called clutter filter wave imaging was used. A mechanical wave propagating through the left ventricle shortly after the atrial contraction was measured in the three different apical acquisition planes, for 20 infarct patients and 10 healthy controls. The results obtained were correlated with fibrosis locations from magnetic resonance imaging, and a sensitivity ≥60% was achieved for all infarcts larger than 10% of the left ventricle. The stability of the wave through several heart cycles was assessed and found to be of high quality. This method therefore has potential for non-invasive fibrosis detection in the myocardium, but further validation in a larger group of subjects is needed.