Polyvinyl Alcohol Phantoms With Heterogeneous Plaques: Estimation of Pulse Wave Velocity at the Stenotic Region Using Pulse Wave Imaging.

OBJECTIVE: Plaque characterization is essential for stroke prevention. In the study reported herein, we describe a heterogeneous phantom manufacturing technique with varying plaque compositions of different stiffness using polyvinyl alcohol (PVA) to emulate stenotic arteries and evaluated the use of pulse wave imaging (PWI) to assess plaque stiffness by comparing derived pulse wave velocities, with the goal of assessing plaque vulnerability and identifying high-risk patients for stroke. METHODS: Five stenotic phantoms (50% stenosis) were fabricated by pouring PVA solutions into 3-D-printed molds. Two of the phantoms had heterogeneous plaque compositions of soft (E0 = 13 kPa) and intermediate (E0 = 40 kPa) materials and of stiff (E0 = 54 kPa) and intermediate materials. Ultrasound sequences were acquired as the arterial phantoms were connected to a pulsating pump, and PWI was performed on the ultrasound acquisition using normalized cross-correlation to track the pulse-induced phantom wall distension propagations. Pulse wave velocities were estimated by fitting a linear regression line between the arrival time of the peak acceleration of the wall distension waveform and the corresponding location. RESULTS: Arterial phantoms with heterogeneous plaque stiffness were successfully fabricated. Pulse wave velocities of 2.06, 2.21, 2.49, 2.67 and 3.31 m/s were found in the phantom experiments using PWI for homogeneous soft plaque, the heterogeneous soft and intermediate plaque, homogeneous intermediate plaque, the heterogeneous stiff and intermediate plaque and homogeneous stiff plaque, respectively. CONCLUSION: A novel arterial phantom building technique was reported with varying heterogenous plaque compositions of different stiffness. The feasibility of using PWI to evaluate plaque stiffness in stenotic arteries was determined and found that PWI can distinguish between plaques of distinct stiffness and composition.

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

Objective. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascular system. Ultrasound elasticity imaging techniques have shown great promise as a low-cost and non-invasive tool to enable localized maps of arterial wall stiffness. Such techniques rely upon motion detection algorithms that provide arterial wall displacement estimation.Approach. In this study, we propose an unsupervised deep learning-based approach, originally proposed for image registration, in order to enable improved quality arterial wall displacement estimation at high temporal and spatial resolutions. The performance of the proposed network was assessed through phantom experiments, where various models were trained by using ultrasound RF signals, or B-mode images, as well as different loss functions.Main results. Using the mean square error (MSE) for the training process provided the highest signal-to-noise ratio when training on the B-modes images (30.36 ± 1.14 dB) and highest contrast-to-noise ratio when training on the RF signals (32.84 ± 1.89 dB). In addition, training the model on RF signals demonstrated the capability of providing accurate localized pulse wave velocity (PWV) maps, with a mean relative error (MREPWV) of 3.32 ± 1.80% and anR2 of 0.97 ± 0.03. Finally, the developed model was tested in human common carotid arteriesin vivo, providing accurate tracking of the distension pulse wave propagation, with an MREPWV= 3.86 ± 2.69% andR2 = 0.95 ± 0.03.Significance. In conclusion, a novel displacement estimation approach was presented, showing promise in improving vascular elasticity imaging techniques.

Pulse wave and vector flow Imaging for atherosclerotic disease progression in hypercholesterolemic swine.

Non-invasive monitoring of atherosclerosis remains challenging. Pulse Wave Imaging (PWI) is a non-invasive technique to measure the local stiffness at diastolic and end-systolic pressures and quantify the hemodynamics. The objective of this study is twofold, namely (1) to investigate the capability of (adaptive) PWI to assess progressive change in local stiffness and homogeneity of the carotid in a high-cholesterol swine model and (2) to assess the ability of PWI to monitor the change in hemodynamics and a corresponding change in stiffness. Nine (n=9) hypercholesterolemic swine were included in this study and followed for up to 9 months. A ligation in the left carotid was used to cause a hemodynamic disturbance. The carotids with detectable hemodynamic disturbance showed a reduction in wall shear stress immediately after ligation (2.12 ± 0.49 to 0.98 ± 0.47 Pa for 40-90% ligation (Group B) and 1.82 ± 0.25 to 0.49 ± 0.46 Pa for >90% ligation (Group C)). Histology revealed subsequent lesion formation after 8-9 months, and the type of lesion formation was dependent on the type of the induced ligation, with more complex plaques observed in the carotids with a more significant ligation (C: >90%). The compliance progression appears differed for groups B and C, with an increase in compliance to 2.09 ± 2.90×10-10 m2 Pa-1 for group C whereas the compliance of group B remained low at 8 months (0.95 ± 0.94×10-10 m2 Pa-1). In summary, PWI appeared capable of monitoring a change in wall shear stress and separating two distinct progression pathways resulting in distinct compliances.

Pulse wave imaging of a stenotic artery model with plaque constituents of different stiffnesses: Experimental demonstration in phantoms and fluid-structure interaction simulation.

Vulnerable plaques associated with softer components may rupture, releasing thrombotic emboli to smaller vessels in the brain, thus causing an ischemic stroke. Pulse Wave Imaging (PWI) is an ultrasound-based method that allows for pulse wave visualization while the regional pulse wave velocity (PWV) is mapped along the arterial wall to infer the underlying wall compliance. One potential application of PWI is the non-invasive estimation of plaque's mechanical properties for investigating its vulnerability. In this study, the accuracy of PWV estimation in stenotic vessels was investigated by computational simulation and PWI in validation phantoms to evaluate this modality for assessing future stroke risk. Polyvinyl alcohol (PVA) phantoms with plaque constituents of different stiffnesses were designed and constructed to emulate stenotic arteries in the experiment, and the novel fabrication process was described. Finite-element fluid-structure interaction simulations were performed in a stenotic phantom model that matched the geometry and parameters of the experiment in phantoms. The peak distension acceleration of the phantom wall was tracked to estimate PWV. PWVs of 2.57 ms-1, 3.41 ms-1, and 4.48 ms-1 were respectively obtained in the soft, intermediate, and stiff plaque material in phantoms during the experiment using PWI. PWVs of 2.10 ms-1, 3.33 ms-1, and 4.02 ms-1 were respectively found in the soft, intermediate, and stiff plaque material in the computational simulation. These results demonstrate that PWI can effectively distinguish the mechanical properties of plaque in phantoms as compared to computational simulation.

Adaptive Wall Shear Stress Imaging in Phantoms, Simulations and In Vivo.

WSS measurement is challenging since it requires sensitive flow measurements at a distance close to the wall. The aim of this study is to develop an ultrasound imaging technique which combines vector flow imaging with an unsupervised data clustering approach that automatically detects the region close to the wall with optimally linear flow profile, to provide direct and robust WSS estimation. The proposed technique was evaluated in phantoms, mimicking normal and atherosclerotic vessels, and spatially registered Fluid Structure Interaction (FSI) simulations. A relative error of 6.7% and 19.8% was obtained for peak systolic (WSSPS) and end diastolic (WSSED) WSS in the straight phantom, while in the stenotic phantom, a good similarity was found between measured and simulated WSS distribution, with a correlation coefficient, R, of 0.89 and 0.85 for WSSPS and WSSED, respectively. Moreover, the feasibility of the technique to detect pre-clinical atherosclerosis was tested in an atherosclerotic swine model. Six swines were fed atherogenic diet, while their left carotid artery was ligated in order to disturb flow patterns. Ligated arterial segments that were exposed to low WSSPS and WSS characterized by high frequency oscillations at baseline, developed either moderately or highly stenotic plaques (p < 0.05). Finally, feasibility of the technique was demonstrated in normal and atherosclerotic human subjects. Atherosclerotic carotid arteries with low stenosis had lower WSSPS as compared to control subjects (p < 0.01), while in one subject with high stenosis, elevated WSS was found on an arterial segment, which coincided with plaque rupture site, as determined through histological examination.

Numerical study of the dielectric liquid around an electrical discharge generated vapor bubble in ultrasonic assisted EDM.

In electrical discharge machining due to the electrical current, very small bubbles are created in the dielectric fluid between the tool and the workpiece. Increase of the number of bubbles and their growth in size generate a single bubble. The bubble has an important role in electrical discharge machining. In this paper the effect of ultrasonic vibration of the tool and the velocity fields and pressure distribution in the dielectric fluid around the bubble in the process of electrical discharge machining are studied numerically. The boundary integral equation method is applied for the numerical solution of the problem. It is shown that ultrasonic vibration of the tool has great influence on the evolution of the bubble, fluid behavior and the efficiency of the machining in EDM. At the last stages of the collapse phase of the bubble, a liquid jet develops on the bubble which has different shapes. Due to the different cases, and a high pressure region appears just near the jet of the bubble. Also the fluid particles have the highest relative velocity just near the liquid jet of the bubble.