Pulse Wave Imaging in Carotid Artery Stenosis Human Patients in Vivo

Characteristics and evaluation of atherosclerotic plaques: an overview of state-of-the-art techniques

Atherosclerosis is an important cause of cerebrovascular and cardiovascular disease (CVD). Lipid infiltration, inflammation, and altered vascular stress are the critical mechanisms that cause atherosclerotic plaque formation. The hallmarks of the progression of atherosclerosis include plaque ulceration, rupture, neovascularization, and intraplaque hemorrhage, all of which are closely associated with the occurrence of CVD. Assessing the severity of atherosclerosis and plaque vulnerability is crucial for the prevention and treatment of CVD. Integrating imaging techniques for evaluating the characteristics of atherosclerotic plaques with computer simulations yields insights into plaque inflammation levels, spatial morphology, and intravascular stress distribution, resulting in a more realistic and accurate estimation of plaque state. Here, we review the characteristics and advancing techniques used to analyze intracranial and extracranial atherosclerotic plaques to provide a comprehensive understanding of atheroma.

In Vivo Adaptive Bayesian Regularized Lagrangian Carotid Strain Imaging for Murine Carotid Arteries and Its Associations With Histological Findings

OBJECTIVES

Non-invasive methods for monitoring arterial health and identifying early injury to optimize treatment for patients are desirable. The objective of this study was to demonstrate the use of an adaptive Bayesian regularized Lagrangian carotid strain imaging (ABR-LCSI) algorithm for monitoring of atherogenesis in a murine model and examine associations between the ultrasound strain measures and histology.

METHODS

Ultrasound radiofrequency (RF) data were acquired from both the right and left common carotid artery (CCA) of 10 (5 male and 5 female) ApoE tm1Unc/J mice at 6, 16 and 24 wk. Lagrangian accumulated axial, lateral and shear strain images and three strain indices-maximum accumulated strain index (MASI), peak mean strain of full region of interest (ROI) index (PMSRI) and strain at peak axial displacement index (SPADI)-were estimated using the ABR-LCSI algorithm. Mice were euthanized (n = 2 at 6 and 16 wk, n = 6 at 24 wk) for histology examination.

RESULTS

Sex-specific differences in strain indices of mice at 6, 16 and 24 wk were observed. For male mice, axial PMSRI and SPADI changed significantly from 6 to 24 wk (mean axial PMSRI at 6 wk = 14.10 ± 5.33% and that at 24 wk = -3.03 ± 5.61%, p < 0.001). For female mice, lateral MASI increased significantly from 6 to 24 wk (mean lateral MASI at 6 wk = 10.26 ± 3.13% and that at 24 wk = 16.42 ± 7.15%, p = 0.048). Both cohorts exhibited strong associations with ex vivo histological findings (male mice: correlation between number of elastin fibers and axial PMSRI: rs = 0.83, p = 0.01; female mice: correlation between shear MASI and plaque score: rs = 0.77, p = 0.009).

CONCLUSION

The results indicate that ABR-LCSI can be used to measure arterial wall strain in a murine model and that changes in strain are associated with changes in arterial wall structure and plaque formation.

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 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.

Role of Preoperative Ultrasound Shear-Wave Elastography and Radiofrequency-Based Arterial Wall Tracking in Assessing the Vulnerability of Carotid Plaques: Preliminary Results

We aimed at evaluating the ability of point shear-wave elastography (pSWE) and of a radiofrequency (RF) echo-tracking-based method in preoperatively assessing the vulnerability of the carotid plaque in patients undergoing carotid endarterectomy (CEA) for significant asymptomatic stenosis. All patients who underwent CEA from 03/2021 to 03/2022 performed a preoperative pSWE and an RF echo-based wall evaluation of arterial stiffness using an Esaote MyLab ultrasound system (EsaoteTM, Genova, Italy) with dedicated software. The data derived from these evaluations (Young's modulus (YM), augmentation index (AIx), pulse-wave velocity (PWV)) were correlated with the outcome of the analysis of the plaque removed during the surgery. Data were analyzed on 63 patients (33 vulnerable and 30 stable plaques). In stable plaques, YM was significantly higher than in vulnerable plaques (49.6 + 8.1 kPa vs. 24.6 + 4.3 kPa, p = 0.009). AIx also tended to be slightly higher in stable plaques, even if it was not statistically significant (10.4 + 0.9% vs. 7.7 + 0.9%, p = 0.16). The PWV was similar (12.2 + 0.9 m/s for stable plaques vs. 10.6 + 0.5 m/s for vulnerable plaques, p = 0.16). For YM, values >34 kPa had a sensitivity of 50% and a specificity of 73.3% in predicting plaque nonvulnerability (area under the curve = 0.66). Preoperative measurement of YM by means of pSWE could be a noninvasive and easily applicable tool for assessing the preoperative risk of plaque vulnerability in asymptomatic patients who are candidates for CEA.

Spectral analysis of ultrasound backscatter for non-invasive measurement of plaque composition

Carotid atherosclerotic plaque composition may be an important indication of patient risk for future cerebrovascular events. Ultrasound spectral analysis has the potential to provide a robust measure of plaque composition in vivo if the backscatter transfer function can be sufficiently isolated from the effects of attenuation from overlying tissue, receive and transmit transfer functions from the ultrasound system and transducer, and diffraction. This study examines the usefulness of the nonlinearly generated second harmonic portion of the backscatter signal and the effects of a variety of attenuation compensation techniques for noninvasively characterizing human carotid plaque using spectral analysis and machine learning. Post-beamformed ultrasound backscatter radiofrequency (RF) data were acquired from 6 normal subjects and 119 carotid endarterectomy patients prior to surgery. Plaque obtained following surgery was histologically processed, and regions of interest (ROI) corresponding to homogenous tissue types (fibrous/fibro-lipidic, hemorrhagic and/or necrotic core and calcified) were selected from RF data. Both the harmonic and fundamental power spectra for each ROI was obtained and normalized by data from a uniform phantom (0.5 dB/cm-MHz slope of attenuation). Additional attenuation compensation approaches were compared to simply using the reference phantom: (1) optimum power spectral shift estimation, (2) one-step adventitial, or (3) two-step adventitial. Spectral parameters extracted from both the fundamental and harmonic estimates of the backscatter transfer function of 363 ROI's from 152 plaque specimens were used to train and test random forest and support vector machine classification models. The best results came from using spectral parameters derived from both the fundamental and second harmonic bands with a predictive accuracy of 65-68%, kappa statistic of 0.49-0.54, and accuracies of 84% for fibrous, 68-74% for hemorrhagic and/or necrotic core, and 78-81% for calcified ROI's. The result indicated that the nonlinearly generated second harmonic portion of backscatter is useful for carotid plaque tissue characterization and that a reference phantom approach with a 0.5 dB/cm-MHz slope of attenuation works as well as more complicated approaches.

Noninvasive Aortic Ultrafast Pulse Wave Velocity Associated With Framingham Risk Model: in vivo Feasibility Study

Background

Aortic pulse wave velocity (PWV) enables the direct assessment of aortic stiffness, which is an independent risk factor of cardiovascular (CV) events. The aim of this study is to evaluate the association between aortic PWV and CV risk model classified into three groups based on the Framingham risk score (FRS), i.e., low-risk (&lt;10%), intermediate-risk (10~20%) and high-risk (&gt;20%).

Methods

To noninvasively estimate local PWV in an abdominal aorta, a high-spatiotemporal resolution PWV measurement method (&gt;1 kHz) based on wide field-of-view ultrafast curved array imaging (ufcPWV) is proposed. In the ufcPWV measurement, a new aortic wall motion tracking algorithm based on adaptive reference frame update is performed to compensate errors from temporally accumulated out-of-plane motion. In addition, an aortic pressure waveform is simultaneously measured by applanation tonometry, and a theoretical PWV based on the Bramwell-Hill model (bhPWV) is derived. A total of 69 subjects (aged 23–86 years) according to the CV risk model were enrolled and examined with abdominal ultrasound scan.

Results

The ufcPWV was significantly correlated with bhPWV (r = 0.847, p &lt; 0.01), and it showed a statistically significant difference between low- and intermediate-risk groups (5.3 ± 1.1 vs. 8.3 ± 3.1 m/s, p &lt; 0.01), and low- and high-risk groups (5.3 ± 1.1 vs. 10.8 ± 2.5 m/s, p &lt; 0.01) while there is no significant difference between intermediate- and high-risk groups (8.3 ± 3.1 vs. 10.8 ± 2.5 m/s, p = 0.121). Moreover, it showed a significant difference between two evaluation groups [low- (&lt;10%) vs. higher-risk group (≥10%)] (5.3 ± 1.1 vs. 9.4 ± 3.1 m/s, p &lt; 0.01) when the intermediate- and high-risk groups were merged into a higher-risk group.

Conclusion

This feasibility study based on CV risk model demonstrated that the aortic ufcPWV measurement has the potential to be a new approach to overcome the limitations of conventional systemic measurement methods in the assessment of aortic stiffness.

The Association of Arterial Stiffness With Significant Carotid Atherosclerosis and Carotid Plaque Vulnerability

Arterial stiffness and its valid index, the cardio-ankle vascular index (CAVI), have emerged as predictors of adverse cardiovascular outcomes. We investigated the relationship of the CAVI with significant carotid stenosis (> 50%) and the related cerebrovascular symptoms or carotid plaque echogenicity, assessed by ultrasound gray-scale median (GSM) score, at baseline and after carotid artery stenting (CAS). We prospectively enrolled 113 patients with carotid stenosis (70-99% for asymptomatic and > 50% for symptomatic participants) eligible for CAS. Age- and sex-matched individuals (n = 38) served as controls (CON). Clinical data, CAVI, and biochemical profile were obtained at baseline. Clinical assessment and CAVI measurement were performed 6 months after CAS. Compared with the CON group, the CAS group had a higher incidence of co-morbidities (diabetes, hypertension, and hyperlipidemia), higher CAVI values (9.94 ± 2.14 vs 7.85 ± .97 m/sec, P < .001), but a better lipid profile due to increased prescription of statins. The symptomatic CAS subgroup showed higher CAVI ( P < .001), high-sensitivity C-reactive protein ( P = .048), and osteoprotegerin ( P = .002) levels than the asymptomatic one. In multivariate analysis, CAVI at baseline was independently associated with the presence of significant carotid atherosclerosis (β = .695, P < .001), cerebrovascular events (β = .474, P < .001), and GSM score (β = −.275, P = .042). Raised CAVI values were independently associated with significant carotid stenosis and plaque vulnerability.

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

Objective.Atherosclerosis is a vascular disease characterized by compositional and mechanical changes in the arterial walls that lead to a plaque buildup. Depending on its geometry and composition, a plaque can ruptured and cause stroke, ischemia or infarction. Pulse wave imaging (PWI) is an ultrasound-based technique developed to locally quantify the stiffness of arteries. This technique has shown promising results when applied to patients. The objective of this study is to assess the capability of PWI to monitor the disease progression in a swine model that mimics human pathology.Approach.The left common carotid of three hypercholesterolemic Wisconsin miniature swines, fed an atherogenic diet, was ligated. Ligated and contralateral carotids were imaged once a month over 9 months, at a high-frame-rate, with a 5-plane wave compounding sequence and a 5 MHz linear array. Each acquisition was repeated after probe repositioning to evaluate the reproducibility. Wall displacements were estimated from the beamformed RF-data and were arranged as spatiotemporal maps depicting the wave propagation. The pulse wave velocity (PWV) estimated by tracking the 50% upstroke of the wave was converted in compliance using the Bramwell-Hill model. At the termination of the experiment, the carotids were extracted for histology analysis.Main results.PWI was able to monitor the evolution of compliance in both carotids of the animals. Reproducibility was demonstrated as the difference of PWV between cardiac cycles was similar to the difference between acquisitions (9.04% versus 9.91%). The plaque components were similar to the ones usually observed in patients. Each animal presented a unique pattern of compliance progression, which was confirmed by the plaque composition observed histologically.Significance.This study provides important insights on the vascular wall stiffness progression in an atherosclerotic swine model. It therefore paves the way for a thorough longitudinal study that examines the role of stiffness in both the plaque formation and plaque progression.

Feasibility of Bilinear Mechanical Characterization of the Abdominal Aorta in a Hypertensive Mouse Model

A change in elastin and collagen content is indicative of damage caused by hypertension, which changes the non-linear behavior of the vessel wall. This study was aimed at investigating the feasibility of monitoring the non-linear material behavior in an angiotensin II hypertensive mice model. Aortas from 13 hypertensive mice were imaged with pulse wave imaging (PWI) over 4 wk using a 40-MHz linear array. The pulse wave velocity was estimated using two wave features: (i) the maximum axial acceleration of the foot (PWV_dia) and (ii) the maximum axial acceleration of the dicrotic notch (PWV_end-sys). The Bramwell-Hill equation was used to derive the compliance at diastolic and end-systolic pressure. This study determined the potential of PWI in a hypertensive mouse model to image and quantify the non-linear material behavior in vivo. End-systolic compliance could differentiate between the sham and angiotensin II groups, whereas diastolic compliance could not, indicating that PWI can detect early collagen-dominated remodeling.

Pulse Wave Imaging Coupled With Vector Flow Mapping: A Phantom, Simulation, and In Vivo Study

Pulse wave imaging (PWI) is an ultrasound imaging modality that estimates the wall stiffness of an imaged arterial segment by tracking the pulse wave propagation. The aim of the present study is to integrate PWI with vector flow imaging, enabling simultaneous and co-localized mapping of vessel wall mechanical properties and 2-D flow patterns. Two vector flow imaging techniques were implemented using the PWI acquisition sequence: 1) multiangle vector Doppler and 2) a cross-correlation-based vector flow imaging (CC VFI) method. The two vector flow imaging techniques were evaluated in vitro using a vessel phantom with an embedded plaque, along with spatially registered fluid structure interaction (FSI) simulations with the same geometry and inlet flow as the phantom setup. The flow magnitude and vector direction obtained through simulations and phantom experiments were compared in a prestenotic and stenotic segment of the phantom and at five different time frames. In most comparisons, CC VFI provided significantly lower bias or precision than the vector Doppler method ( ) indicating better performance. In addition, the proposed technique was applied to the carotid arteries of nonatherosclerotic subjects of different ages to investigate the relationship between PWI-derived compliance of the arterial wall and flow velocity in vivo. Spearman's rank-order test revealed positive correlation between compliance and peak flow velocity magnitude ( rs = 0.90 and ), while significantly lower compliance ( ) and lower peak flow velocity magnitude ( ) were determined in older (54-73 y.o.) compared with young (24-32 y.o.) subjects. Finally, initial feasibility was shown in an atherosclerotic common carotid artery in vivo. The proposed imaging modality successfully provided information on blood flow patterns and arterial wall stiffness and is expected to provide additional insight in studying carotid artery biomechanics, as well as aid in carotid artery disease diagnosis and monitoring.

Dealiasing High-Frame-Rate Color Doppler Using Dual-Wavelength Processing

Doppler ultrasound is the premier modality to analyze blood flow dynamics in clinical practice. With conventional systems, Doppler can either provide a time-resolved quantification of the flow dynamics in sample volumes (spectral Doppler) or an average Doppler velocity/power [color flow imaging (CFI)] in a wide field of view (FOV) but with a limited frame rate. The recent development of ultrafast parallel systems made it possible to evaluate simultaneously color, power, and spectral Doppler in a wide FOV and at high-frame rates but at the expense of signal-to-noise ratio (SNR). However, like conventional Doppler, ultrafast Doppler is subject to aliasing for large velocities and/or large depths. In a recent study, staggered multi-pulse repetition frequency (PRF) sequences were investigated to dealias color-Doppler images. In this work, we exploit the broadband nature of pulse-echo ultrasound and propose a dual-wavelength approach for CFI dealiasing with a constant PRF. We tested the dual-wavelength bandpass processing, in silico, in laminar flow phantom and validated it in vivo in human carotid arteries ( n = 25 ). The in silico results showed that the Nyquist velocity could be extended up to four times the theoretical limit. In vivo, dealiased CFI were highly consistent with unfolded Spectral Doppler ( r²=0.83 , y=1.1x+0.1 , N=25 ) and provided consistent vector flow images. Our results demonstrate that dual-wavelength processing is an efficient method for high-velocity CFI.

Modeling Pulse Wave Propagation Through a Stenotic Artery With Fluid Structure Interaction: A Validation Study Using Ultrasound Pulse Wave Imaging

Pulse wave imaging (PWI) is an ultrasound-based method that allows spatiotemporal mapping of the arterial pulse wave propagation, from which the local pulse wave velocity (PWV) can be derived. Recent reports indicate that PWI can help the assessment of atherosclerotic plaque composition and mechanical properties. However, the effect of the atherosclerotic plaque's geometry and mechanics on the arterial wall distension and local PWV remains unclear. In this study, we investigated the accuracy of a finite element (FE) fluid-structure interaction (FSI) approach to predict the velocity of a pulse wave propagating through a stenotic artery with an asymmetrical plaque, as quantified with PWI method. Experiments were designed to compare FE-FSI modeling of the pulse wave propagation through a stenotic artery against PWI obtained with manufactured phantom arteries made of polyvinyl alcohol (PVA) material. FSI-generated spatiotemporal maps were used to estimate PWV at the plaque region and compared it to the experimental results. Velocity of the pulse wave propagation and magnitude of the wall distension were correctly predicted with the FE analysis. In addition, findings indicate that a plaque with a high degree of stenosis (>70%) attenuates the propagation of the pulse pressure wave. Results of this study support the validity of the FE-FSI methods to investigate the effect of arterial wall structural and mechanical properties on the pulse wave propagation. This modeling method can help to guide the optimization of PWI to characterize plaque properties and substantiate clinical findings.

A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls

It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system.

Arterial wall mechanical inhomogeneity detection and atherosclerotic plaque characterization using high frame rate pulse wave imaging in carotid artery disease patients in vivo

Pulse wave imaging (PWI) is a non-invasive, ultrasound-based technique, which provides information on arterial wall stiffness by estimating the pulse wave velocity (PWV) along an imaged arterial wall segment. The aims of the present study were to: (1) utilize the PWI information to automatically and optimally divide the artery into the segments with most homogeneous properties and (2) assess the feasibility of this method to provide arterial wall mechanical characterization in normal and atherosclerotic carotid arteries in vivo. A silicone phantom consisting of a soft and stiff segment along its longitudinal axis was scanned at the stiffness transition, and the PWV in each segment was estimated through static testing. The proposed algorithm detected the stiffness interface with an average error of 0.98  ±  0.49 mm and 1.04  ±  0.27 mm in the soft-to-stiff and stiff-to-soft pulse wave transmission direction, respectively. Mean PWVs estimated in the case of the soft-to-stiff pulse wave transmission direction were 2.47 [Formula: see text] 0.04 m s^-1 and 3.43 [Formula: see text] 0.08 m s^-1 for the soft and stiff phantom segments, respectively, while in the case of stiff-to-soft transmission direction PWVs were 2.60 [Formula: see text] 0.18 m s^-1 and 3.72 [Formula: see text] 0.08 m s^-1 for the soft and stiff phantom segments, respectively, which were in good agreement with the PWVs obtained through static testing (soft segment: 2.41 m s^-1, stiff segment: 3.52 m s^-1). Furthermore, the carotid arteries of N  =  9 young subjects (22-32 y.o.) and N  =  9 elderly subjects (60-73 y.o.) with no prior history of carotid artery disease were scanned, in vivo, as well as the atherosclerotic carotid arteries of N  =  12 (59-85 y.o.) carotid artery disease patients. One-way ANOVA with Holm-Sidak correction showed that the number of most homogeneous segments in which the artery was divided was significantly higher in the case of carotid artery disease patients compared to young (3.25 [Formula: see text] 0.86 segments versus 1.00 [Formula: see text] 0.00 segments, p -value  <  0.0001) and elderly non-atherosclerotic subjects (3.25 [Formula: see text] 0.86 segments versus 1.44 [Formula: see text] 0.51 segments p -value  <  0.0001), indicating increased wall inhomogeneity in atherosclerotic arteries. The compliance provided by the proposed algorithm was significantly higher in non-calcified/high-lipid plaques as compared with calcified plaques (3.35 [Formula: see text] 2.45 *[Formula: see text] versus 0.22 [Formula: see text] 0.18 * [Formula: see text], p -value  <  0.01) and the compliance estimated in elderly subjects (3.35 [Formula: see text] 2.45 * [Formula: see text] versus 0.79 [Formula: see text] 0.30 * [Formula: see text], p -value  <  0.01). Moreover, lower compliance was estimated in cases where vulnerable plaque characteristics were present (i.e. necrotic lipid core, thrombus), compared to stable plaque components (calcification), as evaluated through plaque histological examination. The proposed algorithm was thus capable of evaluating arterial wall inhomogeneity and characterize wall mechanical properties, showing promise in vascular disease diagnosis and monitoring.

Adaptive Pulse Wave Imaging: Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo

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.

In vivo carotid strain imaging using principal strains in longitudinal view

Carotid plaque rupture can result in stroke or transient ischemic attack that can be devastating for patients. Ultrasound strain imaging provides a noninvasive method to identify unstable plaque likely to rupture. Axial, lateral and shear strains in carotid plaque have been shown to be linked to carotid plaque instability. Recently, there has been interest in using principal strains, which do not depend on angle of insonification of the carotid artery for quantifying instability in plaque along the longitudinal view. In this work relationships between angle dependent axial, lateral and shear strain along with axis independent principal strains are compared. Three strain indices were defined, 1) Average Mean Strain (AMS), 2) Maximum Mean Strain (MMS) and 3) Mean Standard Deviation (MSD) to identify relationships between these five strain image types in a group of 76 in vivo patients. The maximum principal strain demonstrated the highest strain values when compared to axial strain for all patients with a linear regression slope of 1.6 and a y intercept of 2.4 percent strain for AMS. The maximum shear strain when compared to shear strain had a slope of 1.15 and a y intercept of 0.21 percent for AMS. Next, the effect of insonification angle, which is the angle subtended by the artery at the location of plaque was studied. Patients were divided into three sub groups, i.e. less than 5 degrees (n = 31), between 5 and 10 degrees (n = 24) and above 10 degrees (n = 21). The angle of insonification did not make a significant difference between the three angle groups when comparing the relationship between the angle dependent and independent strain values.

Delineation of Human Carotid Plaque Features In Vivo by Exploiting Displacement Variance

While in vivo acoustic radiation force impulse (ARFI)-induced peak displacement (PD) has been demonstrated to have high sensitivity and specificity for differentiating soft from stiff plaque components in patients with carotid plaque, the parameter exhibits poorer performance for distinguishing between plaque features with similar stiffness. To improve discrimination of carotid plaque features relative to PD, we hypothesize that signal correlation and signal-to-noise ratio (SNR) can be combined, outright or via displacement variance. Plaque feature detection by displacement variance, evaluated as the decadic logarithm of the variance of acceleration and termed "log(VoA)," was compared to that achieved by exploiting SNR, cross correlation coefficient, and ARFI-induced PD outcome metrics. Parametric images were rendered for 25 patients undergoing carotid endarterectomy, with spatially matched histology confirming plaque composition and structure. On average, across all plaques, log(VoA) was the only outcome metric with values that statistically differed between regions of lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), collagen (COL), and calcium (CAL). Further, log(VoA) achieved the highest contrast-to-noise ratio (CNR) for discriminating between LRNC and IPH, COL and CAL, and grouped soft (LRNC and IPH) and stiff (COL and CAL) plaque components. More specifically, relative to the previously demonstrated ARFI PD parameter, log(VoA) achieved 73% higher CNR between LRNC and IPH and 59% higher CNR between COL and CAL. These results suggest that log(VoA) enhances the differentiation of LRNC, IPH, COL, and CAL in human carotid plaques, in vivo, which is clinically relevant to improving stroke risk prediction and medical management.