Mapping of mechanical properties, dependent on the frequency of motion, is relevant in diagnosis, monitoring treatment response, or intra-operative surgical resection planning. While shear wave speeds at different frequencies have been described elsewhere, the effect of frequency on the "on-axis" acoustic radiation force (ARF)-induced displacement has not been previously investigated. Instead of generating single transducer-harmonic motion imaging (ST-HMI)-derived peak-to-peak displacement (P2PD) image at a particular frequency, a novel multi-frequency excitation pulse is proposed to generate P2PD images at 100-1000 Hz simultaneously. The performance of the proposed excitation pulse is compared with the ARFI by imaging 16 different inclusions (Young's moduli of 6, 9, 36, 70 kPa and diameters of 1.6, 2.5, 6.5, and 10.4 mm) embedded in an 18 kPa background. Depending on inclusion size and stiffness, the maximum CNR and contrast were achieved at different frequencies and were always higher than ARFI. The frequency, at which maximum CNR and contrast were achieved, increased with stiffness for fixed inclusion's size and decreased with size for fixed stiffness. In vivo feasibility is tested by imaging a 4T1 breast cancer mouse tumor on Day 6, 12, and 19 post-injection of tumor cells. Similar to phantoms, the CNR of ST-HMI images was higher than ARFI and increased with frequency for the tumor on Day 6. Besides, P2PD at 100-1000 Hz indicated that the tumor became stiffer with respect to the neighboring non-cancerous tissue over time. These results indicate the importance of using a multi-frequency excitation pulse to simultaneously generate displacement at multiple frequencies to better delineate inclusions or tumors.

Imaging applications tailored towards ultrasound-based treatment, such as high intensity focused ultrasound (FUS), where higher power ultrasound generates a radiation force for ultrasound elasticity imaging or therapeutics/theranostics, are affected by interference from FUS. The artifact becomes more pronounced with intensity and power. To overcome this limitation, we propose FUS-net, a method that incorporates a CNN-based U-net autoencoder trained end-to-end on 'clean' and 'corrupted' RF data in Tensorflow 2.3 for FUS artifact removal. The network learns the representation of RF data and FUS artifacts in latent space so that the output of corrupted RF input is clean RF data. We find that FUS-net perform 15% better than stacked autoencoders (SAE) on evaluated test datasets. B-mode images beamformed from FUS-net RF shows superior speckle quality and better contrast-to-noise (CNR) than both notch-filtered and adaptive least means squares filtered RF data. Furthermore, FUS-net filtered images had lower errors and higher similarity to clean images collected from unseen scans at all pressure levels. Lastly, FUS-net RF can be used with existing cross-correlation speckle-tracking algorithms to generate displacement maps. FUS-net currently outperforms conventional filtering and SAEs for removing high pressure FUS interference from RF data, and hence may be applicable to all FUS-based imaging and therapeutic methods.

Harmonic motion imaging (HMI) interrogates the mechanical properties of tissues by simultaneously generating and tracking harmonic oscillation using focused ultrasound and imaging transducers, respectively. Instead of using two transducers, the objective of this work is to develop a single transducer HMI (ST-HMI) to both generate and track harmonic motion at "on-axis" to the force for facilitating data acquisition. In ST-HMI, the amplitude-modulated force was generated by modulating excitation pulse duration and tracking of motion was performed by transmitting tracking pulses interleaved between excitation pulses. The feasibility of ST-HMI was performed by imaging two elastic phantoms with three inclusions (N = 6) and comparing it with acoustic radiation force impulse (ARFI) imaging, in vivo longitudinal monitoring of 4T1, orthotropic breast cancer mice (N = 4), and patients (N = 3) with breast masses in vivo. Six inclusions with Young's moduli of 8, 10, 15, 20, 40, and 60 kPa were embedded in a 5 kPa background. The ST-HMI-derived peak-to-peak displacement (P2PD) successfully detected all inclusions with [Formula: see text] of the linear regression between the P2PD ratio of background to inclusion versus Young's moduli ratio of inclusion to background. The contrasts of 10 and 15 kPa inclusions were higher in ST-HMI than ARFI-derived images. In the mouse study, the median P2PD ratio of tumor to non-cancerous tissues was 3.0, 5.1, 6.1, and 7.7 at 1, 2, 3, and 4 weeks post-injection of the tumor cells, respectively. In the clinical study, ST-HMI detected breast masses including fibroadenoma, pseudo angiomatous stromal hyperplasia, and invasive ductal carcinoma with a P2PD ratio of 1.37, 1.61, and 1.78, respectively. These results indicate that ST-HMI can assess the mechanical properties of tissues via generation and tracking of harmonic motion "on-axis" to the ARF. This study is the first step towards translating ST-HMI in clinics.

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.

Imaging arterial mechanical properties may improve vascular disease diagnosis. Pulse wave velocity (PWV) is a marker of arterial stiffness linked to cardio-vascular mortality. Pulse wave imaging (PWI) is a technique for imaging the pulse wave propagation at high spatial and temporal resolution. In this paper, we introduce adaptive PWI, a technique for the automated partition of heterogeneous arteries into individual segments characterized by most homogeneous pulse wave propagation, allowing for more robust PWV estimation. This technique was validated in a silicone phantom with a soft-stiff interface. The mean detection error of the interface was 4.67 ± 0.73 mm and 3.64 ± 0.14 mm in the stiff-to-soft and soft-to-stiff pulse wave transmission direction, respectively. This technique was tested in monitoring the progression of atherosclerosis in mouse aortas in vivo ( n = 11 ). The PWV was found to already increase at the early stage of 10 weeks of high-fat diet (3.17 ± 0.67 m/sec compared to baseline 2.55 ± 0.47 m/sec, ) and further increase after 20 weeks of high-fat diet (3.76±1.20 m/sec). The number of detected segments of the imaged aortas monotonically increased with the duration of high-fat diet indicating an increase in arterial wall property inhomogeneity. The performance of adaptive PWI was also tested in aneurysmal mouse aortas in vivo. Aneurysmal boundaries were detected with a mean error of 0.68±0.44 mm. Finally, initial feasibility was shown in the carotid arteries of healthy and atherosclerotic human subjects in vivo ( n = 3 each). Consequently, adaptive PWI was successful in detecting stiffness inhomogeneity at its early onset and monitoring atherosclerosis progression in vivo.

Focused ultrasound (FUS) is an emerging technique for neuromodulation due to its noninvasive application and high depth penetration. Recent studies have reported success in modulation of brain circuits, peripheral nerves, ion channels, and organ structures. In particular, neuromodulation of peripheral nerves and the underlying mechanisms remain comparatively unexplored in vivo. Lack of methodologies for FUS targeting and monitoring impede further research in in vivo studies. Thus, we developed a method that non-invasively measures nerve engagement, via tissue displacement, during FUS neuromodulation of in vivo nerves using simultaneous high frame-rate ultrasound imaging. Using this system, we can validate, in real-time, FUS targeting of the nerve and characterize subsequent compound muscle action potentials (CMAPs) elicited from sciatic nerve activation in mice using 0.5 to 5 ms pulse durations and 22 - 28 MPa peak positive stimulus pressures at 4 MHz. Interestingly, successful motor excitation from FUS neuromodulation required a minimum interframe nerve displacement of 18 μm without any displacement incurred at the skin or muscle levels. Moreover, CMAPs detected in mice monotonically increased with interframe nerve displacements within the range of 18 to 300 μm . Thus, correlation between nerve displacement and motor activation constitutes strong evidence FUS neuromodulation is driven by a mechanical effect given that tissue deflection is a result of highly focused acoustic radiation force.

In biological tissue, an increase in elasticity is often a marker of abnormalities. Techniques such as quasi-static ultrasound elastography have been developed to assess the strain distribution in soft tissues in two dimensions using a quasi-static compression. However, as abnormalities can exhibit very heterogeneous shapes, a three dimensional approach would be necessary to accurately measure their volume and remove operator dependency. Acquisition of volumes at high rates is also critical to performing real-time imaging with a simple freehand compression. In this study, we developed for the first time a 3D quasi-static ultrasound elastography method with plane waves that estimates axial strain distribution in vivo in entire volumes at high volume rate. Acquisitions were performed with a 2D matrix array probe of 2.5 MHz frequency and 256 elements. Plane waves were emitted at a volume rate of 100 volumes/s during a continuous motorized and freehand compression. 3D B-mode volumes and 3D cumulative axial strain volumes were successfully estimated in inclusion phantoms and in ex vivo canine liver before and after a high intensity focused ultrasound ablation. We also demonstrated the in vivo feasibility of the method using freehand compression on the calf muscle of a human volunteer and were able to retrieve 3D axial strain volume at a high volume rate depicting the differences in stiffness of the two muscles which compose the calf muscle. 3D ultrasound quasi-static elastography with plane waves could become an important technique for the imaging of the elasticity in human bodies in three dimensions using simple freehand scanning.

Strain evaluation is of major interest in clinical cardiology as it can quantify the cardiac function. Myocardial elastography, a radio-frequency (RF)-based cross-correlation method, has been developed to evaluate the local strain distribution in the heart in vivo. However, inhomogeneities such as RF ablation lesions or infarction require a three-dimensional approach to be measured accurately. In addition, acquisitions at high volume rate are essential to evaluate the cardiac strain in three dimensions. Conventional focused transmit schemes using 2D matrix arrays, trade off sufficient volume rate for beam density or sector size to image rapid moving structure such as the heart, which lowers accuracy and precision in the strain estimation. In this study, we developed 3D myocardial elastography at high volume rates using diverging wave transmits to evaluate the local axial strain distribution in three dimensions in three open-chest canines before and after radio-frequency ablation. Acquisitions were performed with a 2.5 MHz 2D matrix array fully programmable used to emit 2000 diverging waves at 2000 volumes/s. Incremental displacements and strains enabled the visualization of rapid events during the QRS complex along with the different phases of the cardiac cycle in entire volumes. Cumulative displacement and strain volumes depict high contrast between non-ablated and ablated myocardium at the lesion location, mapping the tissue coagulation. 3D myocardial strain elastography could thus become an important technique to measure the regional strain distribution in three dimensions in humans.

Atherosclerosis and Abdominal Aortic Aneurysms (AAAs) are two common vascular diseases associated with mechanical changes in the arterial wall. Pulse Wave Imaging (PWI), a technique developed by our group to assess and quantify the mechanical properties of the aortic wall in vivo, may provide valuable diagnostic information. This work implements piecewise PWI (pPWI), an enhanced version of PWI designed for focal vascular diseases. Localized, sub-regional PWVs and PWI moduli ( EPWI ) were estimated within 2-4 mm wall segments of murine normal, atherosclerotic and aneurysmal arteries. Overall, stiffness was found to increase in the atherosclerotic cases. The mean sub-regional PWV was found to be 2.57±0.18 m/s for the normal aortas (n = 7) with a corresponding mean EPWI of 43.82±5.86 kPa. A significant increase ( (p ≤ 0.001)) in the group means of the sub-regional PWVs was found between the normal aortas and the aortas of mice on high-fat diet for 20 ( 3.30±0.36 m/s) and 30 weeks ( 3.56±0.29 m/s). The mean of the sub-regional PWVs ( 1.57±0.78 m/s) and EPWI values ( 19.23±15.47 kPa) decreased significantly in the aneurysmal aortas (p ≤ 0.05) . Furthermore, the mean coefficient of determination (r(2)) of the normal aortas was significantly higher (p ≤ 0.05) than those of the aneurysmal and atherosclerotic cases. These findings demonstrated that pPWI may be able to provide useful biomarkers for monitoring focal vascular diseases.

Harmonic motion imaging for focused ultrasound (HMIFU) utilizes an amplitude-modulated HIFU beam to induce a localized focal oscillatory motion simultaneously estimated. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system. A single divergent transmit beam was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface with frame rates up to 15 Hz, a 100-fold increase compared to conventional CPU-based processing. The real-time feedback rate does not require interrupting the HIFU treatment. Results in phantom experiments showed reproducible HMI images and monitoring of 22 in vitro HIFU treatments using the new 2-D system demonstrated reproducible displacement imaging, and monitoring of 22 in vitro HIFU treatments using the new 2-D system showed a consistent average focal displacement decrease of 46.7 ±14.6% during lesion formation. Complementary focal temperature monitoring also indicated an average rate of displacement increase and decrease with focal temperature at 0.84±1.15%/(°)C, and 2.03±0.93%/(°)C , respectively. These results reinforce the HMIFU capability of estimating and monitoring stiffness related changes in real time. Current ongoing studies include clinical translation of the presented system for monitoring of HIFU treatment for breast and pancreatic tumor applications.

Electromechanical wave imaging (EWI) has recently been introduced as a noninvasive, ultrasound-based imaging modality, which could map the electrical activation of the heart in various echocardiographic planes in mice, dogs, and humans in vivo. By acquiring radio-frequency (RF) frames at very high frame rates (390-520 Hz), the onset of small, localized, transient deformations resulting from the electrical activation of the heart, i.e., generating the electromechanical wave (EMW), can be mapped. The correlation between the EMW and the electrical activation speed and pacing scheme has previously been reported. In this study, we pursue the development of EWI using both displacements and strains and analysis of the EMW properties in dogs in vivo for early detection of ischemia. EWI was performed in normal and ischemic open-chest dogs during sinus rhythm. Ischemia of increasing severity was obtained by gradually obstructing the left-anterior descending (LAD) coronary artery flow. We also introduce the novel method of motion-matching that achieves the reconstruction of the full EWI ciné-loop at very high frame rates even when the ECG may be irregular or unavailable. Incremental displacements were previously used by our group to map the EMW. This paper focuses on the associated incremental strains, which facilitate the interpretation of the EMW by relating it directly to contraction. Moreover, we define the onset of the EMW as the time, at which the incremental strains change sign after the onset of the QRS complex of the ECG. Based on this definition, isochronal representations of the EMW were generated using a semi-automated method. The isochronal representation of the EMW during sinus rhythm was reproducible and shown similar to electrical activation maps previously reported in the literature. After segmentation using a contour-tracking method, the two- and four-chamber views were imaged and displayed in bi-plane views, allowing a 3-D interpretation of the EMW. EWI was shown to be sensitive to the presence of intermediate ischemia. EWI localized the ischemic region when the LAD flow was obstructed at 60% and beyond and was capable of mapping the increase of the ischemic region size as the LAD occlusion level increased. In conclusion, the activation maps and wave patterns obtained with EWI were similar to the electrical equivalents previously reported in the literature. Moreover, EWI was found to be sensitive enough to detect and map intermediate ischemia. Those results indicate that EWI could be used to assess the conduction properties of the myocardium, and detect its ischemic onset and disease progression entirely noninvasively.

The abdominal aortic aneurysm (AAA) is a common vascular disease. The current clinical criterion for treating AAAs is an increased diameter above a critical value. However, the maximum diameter does not correlate well with aortic rupture, the main cause of death from AAA disease. AAA disease leads to changes in the aortic wall mechanical properties. The pulse-wave velocity (PWV) may indicate such a change. Because of limitations in temporal and spatial resolution, the widely used foot-to-foot method measures the global, instead of regional, PWV between two points at a certain distance in the circulation. However, mechanical properties are nonuniform along the normal and pathological (e.g., the AAA and atherosclerosis) arteries; thus, such changes are typically regional. Pulse-wave imaging (PWI) has been developed by our group to map the pulse-wave propagation along the abdominal aorta in mice in vivo. By using a retrospective electrocardiogram (ECG) gating technique, the radio-frequency (RF) signals over one cardiac cycle were obtained in murine aortas at the extremely high frame rate of 8 kHz and with a field-of-view (FOV) of 12 x 12 mm(2). The velocities of the aortic wall were estimated using an RF-based speckle tracking method. An Angiotensin II (AngII) infusion-based AAA model was used to simulate the human AAA case. Sequences of wall velocity images can noninvasively and quantitatively map the propagation of the pulse wave along the aortic wall. In the normal and sham aortas, the propagation of the pulse wave was relatively uniform along the wall, while in the AngII-treated aortas, the propagation was shown to be nonuniform. There was no significant difference ( p > 0.05) in the PWV between sham (4.67 +/- 1.15 m/s, n=5) and AngII-treated (4.34 +/- 1.48 m/s, n=17) aortas. The correlation coefficient of the linear regression was significantly higher ( p < 0.005) in the sham aortas (0.89 +/- 0.03, n=5 ) than in the AngII-treated ones (0.61 +/- 0.15, n=17). The wall velocities induced by the pulse wave were lower and the pulse wave moved nonuniformly along the AngII-treated aorta ( p < 0.005), with the lowest velocities at the aneurysmal regions. The discrepancy in the regional wall velocity and the nonuniform pulse-wave propagation along the AngII-treated aorta indicated the inhomogeneities in the aortic wall properties, and the reduced wall velocities indicated stiffening of the aneurysmal wall. This novel technique may thus constitute an early detection tool of vascular degeneration as well as serve as a suitable predictor of AAA rupture, complementary to the current clinical screening practice.

In vivo plaque characterization is an important research field in interventional cardiology. We will study the realistic challenges to this goal by deploying 40 MHz single-element, mechanically rotating transducers. The intrinsic variability among the transducers' spectral parameters as well as tissue signals will be demonstrated. Subsequently, we will show that global data normalization is not suited for data calibration, due to the aforementioned variations as well as the stringent characteristics of spectral features. We will describe the sensitivity of an existing feature extraction algorithm based on eight spectral signatures (integrated backscatter coefficient, slope, midband-fit (MBF), intercept, and maximum and minimum powers and their relative frequencies) to a number of factors, such as the window size and order of the autoregressive (AR) model. It will be further demonstrated that the variations in the transducer's spectral parameters (i.e., center frequency and bandwidth) cause inconsistencies among extracted features. In this paper, two fundamental questions are addressed: 1) what is the best reliable way to extract the most informative features? and 2) which classification algorithm is the most appropriate for this problem? We will present a full-spectrum analysis as an alternative to the eight-feature approach. For the first time, different classification algorithms, such as k-nearest neighbors (k-NN) and linear Fisher, will be employed and their performances quantified. Finally, we will explore the reliability of the training dataset and the complexity of the recognition algorithm and illustrate that these two aspects can highly impact the accuracy of the end result, which has not been considered until now.