Feasibility of longitudinal monitoring of atherosclerosis with pulse wave imaging in a swine model

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.

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.