Translation of Simultaneous Vessel Wall Motion and Vectorial Blood Flow Imaging in Healthy and Diseased Carotids to the Clinic: A Pilot Study

Quantification of Blood Flow in the Carotid Bifurcation of Healthy Subjects

Locally disturbed blood flow patterns are known to create an atherogenic environment, particularly in the presence of other cardiovascular risk factors. Given the geometry of a healthy carotid artery, complex flow patterns are expected to be present. This study aims to characterize (complex) blood flow patterns and estimate flow-derived parameters in the carotid bifurcation of healthy subjects. Ultrasound-based velocity vector imaging (US-VVI) was acquired in the carotid bifurcation of 20 healthy subjects. Hemodynamic parameters, including temporal velocity profile, vector complexity (VC), vortex presence, and wall shear stress (WSS), were derived and compared between two age groups (20-30 and 65-75 years). Lower velocities and higher VC values were observed in the older age group for all timepoints. The highest presence of vortices was observed during the systolic deceleration, which was more exposed in younger subjects (5 out of 10) compared to older subjects (3 out of 9). A quick build-up and consequent resolving of the vortices was reflected by the relatively short vortex duration, with a vortex presence of 11.4% (7.9-15.6) and 13.1% (5.9-18.6) as a percentage of the cardiac cycle in younger and older subjects, respectively. Larger WSS estimates, represented as median along the complete vessel wall, were found in the younger subjects at all timestamps, except at systolic deceleration. In conclusion, the presence of complex flow patterns was confirmed in healthy subjects and multiple flow-derived hemodynamic parameters were evaluated in two age groups, providing an insight into age-related differences in hemodynamics. Aging seemed to result in higher vector complexities, whereas the presence of recirculating flow is less in older subjects.

FLUST: A fast, open source framework for ultrasound blood flow simulations

BACKGROUND AND OBJECTIVE

Ultrasound based blood velocity estimation is a continuously developing frontier, where the vast number of possible acquisition setups and velocity estimators makes it challenging to assess which combination is better suited for a given imaging application. FLUST, the Flow-Line based Ultrasound Simulation Tool, may be used to address this challenge, providing a common platform for evaluation of velocity estimation schemes on in silico data. However, the FLUST approach had some limitations in its original form, including reduced robustness for phase sensitive setups and the need for manual selection of integrity parameters. In addition, implementation of the technique and therefore also documentation of signal integrity was left to potential users of the approach.

METHODS

In this work, several improvements to the FLUST technique are proposed and investigated, and a robust, open source simulation framework developed. The software supports several transducer types and acquisition setups, in addition to a range of different flow phantoms. The main goal of this work is to offer a robust, computationally cheap and user-friendly framework to simulate ultrasound data from stationary blood velocity fields and thereby facilitate design and evaluation of estimation schemes, including acquisition design, velocity estimation and other post-processing steps.

RESULTS

The technical improvements proposed in this work resulted in reduced interpolation errors, reduced variability in signal power, and also automatic selection of spatial and temporal discretization parameters. Results are presented illustrating the challenges and the effectiveness of the solutions. The integrity of the improved simulation framework is validated in an extensive study, with results indicating that speckle statistics, spatial and temporal correlation and frequency content all correspond well with theoretical predictions. Finally, an illustrative example shows how FLUST may be used throughout the design and optimization process of a velocity estimator.

CONCLUSIONS

The FLUST framework is available as a part of the UltraSound ToolBox (USTB), and the results in this paper demonstrate that it can be used as an efficient and reliable tool for the development and validation of ultrasound-based velocity estimation schemes.

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.

Two-Dimensional Wavenumber Analysis Implemented in Ultrasonic Vector Doppler Method with Focused Transmit Beams

The multi-angle Doppler method was introduced for the estimation of velocity vectors by measuring axial velocities from multiple directions. We have recently reported that the autocorrelation-based velocity vector estimation could be ameliorated significantly by estimating the wavenumbers in two dimensions. Since two-dimensional wavenumber estimation requires a snapshot of an ultrasonic field, the method was first implemented in plane wave imaging. Although plane wave imaging is predominantly useful for examining blood flows at an extremely high temporal resolution, it was reported that the contrast in a B-mode image obtained with a few plane wave emissions was lower than that obtained with focused beams. In this study, the two-dimensional wavenumber analysis was first implemented in a framework with focused transmit beams. The simulations showed that the proposed method achieved an accuracy in velocity estimation comparable to that of the method with plane wave imaging. Furthermore, the performances of the methods implemented in focused beam and plane wave imaging were compared by measuring human common carotid arteries in vivo. Image contrasts were analyzed in normal and clutter-filtered B-mode images. The method with focused beam imaging achieved a better contrast in normal B-mode imaging, and similar velocity magnitudes and angles were obtained by both the methods with focused beam and plane wave imaging. In contrast, the method with plane wave imaging gave a better contrast in a clutter-filtered B-mode image and smaller variances in velocity magnitudes than those with focused beams.

On the Investigation of Autocorrelation-Based Vector Doppler Method With Plane Wave Imaging

Although color flow imaging is one of the representative applications of the Doppler method, it can estimate only the velocity component in the direction of ultrasonic propagation, that is, the axial velocity component. The vector Doppler method with high-frame-rate plane wave imaging overcomes such a limitation by estimating the blood flow velocity vectors using the axial velocities obtained by emitting plane waves in multiple directions. The autocorrelation technique can be used for the estimation of the axial velocity using the phase shift of an ultrasonic echo signal between two transmit-receive events. The technique also requires the frequency of the received echo signal. Although the center frequency of the emitted ultrasonic signal is commonly used in the estimation of axial velocities, the center frequency should be estimated from the received signals. In this study, a method for the estimation of the center frequency designed particularly for the high-frame-rate plane wave imaging was developed. The proposed method estimates the wavenumbers of the received signal in lateral and vertical directions to estimate the wavenumber in the axial direction, from which the center frequency was estimated. The beam steering angle was also estimated from the wavenumbers in the two directions. The effect of the proposed method was validated in simulations. The absolute bias error (ABE) and root-mean squared error in estimated velocity vectors obtained by plane wave imaging with three beam steering angles (-15°, 0°, and 15°) were reduced from 9.27% and 14.80% to 1.15% and 8.75%, respectively, by the proposed method. The applicability of the proposed method to in vivo measurements was also demonstrated using the in vivo recordings of human common carotid arteries. Physiologically consistent blood flow velocity distributions were obtained with respect to three subjects using the proposed method.

Ultrasound deep learning for monitoring of flow-vessel dynamics in murine carotid artery

Several arterial diseases are closely related with mechanical properties of the blood vessel and interactions of flow-vessel dynamics such as mean flow velocity, wall shear stress (WSS) and vascular strain. However, there is an opportunity to improve the measurement accuracy of vascular properties and hemodynamics by adopting deep learning-based ultrasound imaging for flow-vessel dynamics (DL-UFV). In this study, the DL-UFV is proposed by devising an integrated neural network for super-resolved localization and vessel wall segmentation, and it is also combined with tissue motion estimation and flow measurement techniques such as speckle image velocimetry and speckle tracking velocimetry for measuring velocity field information of blood flow. Performance of the DL-UFV is verified by comparing with other conventional techniques in tissue-mimicking phantoms. After the performance verification, in vivo feasibility is demonstrated in the murine carotid artery with different pathologies: aging and diabetes mellitus (DM). The mutual comparison of flow-vessel dynamics and histological analyses shows correlations between the immunoreactive region and abnormal flow-vessel dynamics interactions. The DL-UFV improves biases in measurements of velocity, WSS, and strain with up to 4.6-fold, 15.1-fold, and 22.2-fold in the tissue-mimicking phantom, respectively. Mean flow velocities and WSS values of the DM group decrease by 30% and 20% of those of the control group, respectively. Mean flow velocities and WSS values of the aging group (34.11 cm/s and 13.17 dyne/cm²) are slightly smaller than those of the control group (36.22 cm/s and 14.25 dyne/cm²). However, the strain values of the aging and DM groups are much smaller than those of the control group (p < 0.05). This study shows that the DL-UFV performs better than the conventional ultrasound-based flow and strain measurement techniques for measuring vascular stiffness and complicated flow-vessel dynamics. Furthermore, the DL-UFV demonstrates its excellent performance in the analysis of the hemodynamic and hemorheological effects of DM and aging on the flow and vascular characteristics. This work provides useful hemodynamic information, including mean flow velocity, WSS and strain with high-resolution for diagnosing the pathogenesis of arterial diseases. This information can be used for monitoring progression and regression of atherosclerotic diseases in clinical practice.

Spatial Coherence Adaptive Clutter Filtering in Color Flow Imaging-Part I: Simulation Studies

The appropriate selection of a clutter filter is critical for ensuring the accuracy of velocity estimates in ultrasound color flow imaging. Given the complex spatio-temporal dynamics of flow signal and clutter, however, the manual selection of filters can be a significant challenge, increasing the risk for bias and variance introduced by the removal of flow signal and/or poor clutter suppression. We propose a novel framework to adaptively select clutter filter settings based on color flow image quality feedback derived from the spatial coherence of ultrasonic backscatter. This framework seeks to relax assumptions of clutter magnitude and velocity that are traditionally required in existing adaptive filtering methods to generalize clutter filtering to a wider range of clinically-relevant color flow imaging conditions. In this study, the relationship between color flow velocity estimation error and the spatial coherence of clutter filtered channel signals was investigated in Field II simulations for a wide range of flow and clutter conditions. This relationship was leveraged in a basic implementation of coherence-adaptive clutter filtering (CACF) designed to dynamically adapt clutter filters at each imaging pixel and frame based on local measurements of spatial coherence. In simulation studies with known scatterer and clutter motion, CACF was demonstrated to reduce velocity estimation bias while maintaining variance on par with conventional filtering.

On the Depth-Dependent Accuracy of Plane-Wave-Based Vector Velocity Measurements With Linear Arrays

High-frame-rate vector Doppler methods are used to measure blood velocities over large 2-D regions, but their accuracy is often estimated over a short range of depths. This article thoroughly examines the dependence of velocity measurement accuracy on the target position. Simulations were carried out on flat and parabolic flow profiles, for different Doppler angles, and considering a 2-D vector flow imaging (2-D VFI) method based on plane wave transmission and speckle tracking. The results were also compared with those obtained by the reference spectral Doppler (SD) method. Although, as expected, the bias and standard deviation generally tend to worsen at increasing depths, the measurements also show the following. First, the errors are much lower for the flat profile (from ≈ -4 ± 3% at 20 mm to ≈ -17 ± 4% at 100 mm) than for the parabolic profile (from ≈ -4 ± 3% to ≈ -38 ±%). Second, only part of the relative estimation error is related to the inherent low resolution of the 2-D VFI method. For example, even for SD, the error bias increases (on average) from -0.7% (20 mm) to -17% (60 mm) up to -26% (100 mm). Third, conversely, the beam divergence associated with the linear array acoustic lens was found to have a great impact on the velocity measurements. By simply removing such lens, the average bias for 2-D VFI at 60 and 100 mm dropped down to -9.4% and -19.4%, respectively. In conclusion, the results indicate that the transmission beam broadening on the elevation plane, which is not limited by reception dynamic focusing, is the main cause of velocity underestimation in the presence of high spatial gradients.

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.