Carotid Plaque Strain Indices Were Correlated With Cognitive Performance in a Cohort With Advanced Atherosclerosis, and Traditional Doppler Measures Showed no Association.

OBJECTIVES: Traditional Doppler measures have been used to predict cognitive performance in patients with carotid atherosclerosis. Novel measures, such as carotid plaque strain indices (CPSIs), have shown associations with cognitive performance. We hypothesized that lower mean middle cerebral artery (MCA) velocities, higher bulb-internal carotid artery (ICA) velocities, the MCA pulsatility index (PI), and CPSIs would be associated with poorer cognitive performance in individuals with advanced atherosclerosis. METHODS: Neurocognitive testing, carotid ultrasound imaging, transcranial Doppler imaging, and carotid strain imaging were performed on 40 patients scheduled for carotid endarterectomy. Kendall tau correlations were used to examine relationships between cognitive tests and the surgical-side maximum peak systolic velocity (PSV; from the bulb, proximal, mid, or distal ICA), mean MCA velocity and PI, and maximum CPSIs (axial, lateral, and shear strain indices used to characterize plaque deformations with arterial pulsation). Cognitive measures included age-adjusted indices of verbal fluency, verbal and visual learning/memory, psychomotor speed, auditory attention/working memory, visuospatial construction, and mental flexibility. RESULTS: Participants had a median age of 71.0 (interquartile range, 9.75) years; 26 were male (65%), and 14 were female (35%). Traditional Doppler parameters, PSV, mean MCA velocity, and MCA PI did not predict cognitive performance (all P > .05). Maximum CPSIs were significantly associated with cognitive performance (P < .05). CONCLUSIONS: Traditional velocity measurements of the maximum bulb-ICA PSV, mean MCA velocity, and PI were not associated with cognitive performance in patients with advanced atherosclerotic disease; however, maximum CPSIs were associated with cognitive performance. These findings suggest that cognition may be associated with unstable plaque rather than blood flow.

Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture.

Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which the dilated convolution layers were used in the bottleneck. Both a fully automatic and a semi-automatic approach with a bounding box was implemented. The performance degradation in plaque segmentation due to errors in the bounding box is quantified. We found that the bounding box significantly improved the performance of the networks with U-Net Dice coefficients of 0.48 for automatic and 0.83 for semi-automatic segmentation of plaque. Similar results were also obtained for the dilated U-Net with Dice coefficients of 0.55 for automatic and 0.84 for semi-automatic when compared to manual segmentations of the same plaque by an experienced sonographer. A 5% error in the bounding box in both dimensions reduced the Dice coefficient to 0.79 and 0.80 for U-Net and dilated U-Net respectively.

Lagrangian carotid strain imaging indices normalized to blood pressure for vulnerable plaque.

OBJECTIVE: Ultrasound Lagrangian carotid strain imaging (LCSI) utilizes physiological deformation caused by arterial pressure variations to generate strain tensor maps of the vessel walls and plaques. LCSI has been criticized for the lack of normalization of magnitude-based strain indices to physiological stimuli, namely blood pressure. We evaluated the impact of normalization of magnitude-based strain indices to blood pressure measured immediately after the acquisition of radiofrequency (RF) data loops for LCSI. MATERIALS AND METHODS: A complete clinical ultrasound examination along with RF data loops for LCSI was performed on 50 patients (30 males and 20 females) who presented with >60% carotid stenosis and were scheduled for carotid endarterectomy. Cognition was assessed using the 60-minute neuropsychological test protocol. RESULTS: For axial strains correlation of maximum accumulated strain indices (MASI), cognition scores were -0.46 for non-normalized and -0.45, -0.49, -0.37, and -0.48 for systolic, diastolic, pulse pressure, and mean arterial pressure normalized data, respectively. The corresponding area under the curve (AUC) values for classifiers designed using maximum likelihood estimation of a binormal distribution with a median-split of the executive function cognition scores were 0.73, 0.70, 0.71, 0.70, and 0.71, respectively. CONCLUSIONS: No significant differences in the AUC estimates were obtained between normalized and non-normalized magnitude-based strain indices.

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.

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.

Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results.

Cardiac strain imaging (CSI) plays a critical role in the detection of myocardial motion abnormalities. Displacement estimation is an important processing step to ensure the accuracy and precision of derived strain tensors. In this paper, we propose and implement Spatiotemporal Bayesian regularization (STBR) algorithms for two-dimensional (2-D) normalized cross-correlation (NCC) based multi-level block matching along with incorporation into a Lagrangian cardiac strain estimation framework. Assuming smooth temporal variation over a short span of time, the proposed STBR algorithm performs displacement estimation using at least four consecutive ultrasound radio-frequency (RF) frames by iteratively regularizing 2-D NCC matrices using information from a local spatiotemporal neighborhood in a Bayesian sense. Two STBR schemes are proposed to construct Bayesian likelihood functions termed as Spatial then Temporal Bayesian (STBR-1) and simultaneous Spatiotemporal Bayesian (STBR-2). Radial and longitudinal strain estimated from a finite-element-analysis (FEA) model of realistic canine myocardial deformation were utilized to quantify strain bias, normalized strain error and total temporal relative error (TTR). Statistical analysis with one-way analysis of variance (ANOVA) showed that all Bayesian regularization methods significantly outperform NCC with lower bias and errors (p < 0.001). However, there was no significant difference among Bayesian methods. For example, mean longitudinal TTR for NCC, SBR, STBR-1 and STBR-2 were 25.41%, 9.27%, 10.38% and 10.13% respectively An in vivo feasibility study using RF data from ten healthy mice hearts were used to compare the elastographic signal-to-noise ratio (SNR e ) calculated using stochastic analysis. STBR-2 had the highest expected SNR e both for radial and longitudinal strain. The mean expected SNR e values for accumulated radial strain for NCC, SBR, STBR-1 and STBR-2 were 5.03, 9.43, 9.42 and 10.58, respectively. Overall results suggest that STBR improves CSI in vivo.

Locally optimized correlation-guided Bayesian adaptive regularization for ultrasound strain imaging.

Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a 2D multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using uniform and inclusion phantoms and canine cardiac deformation simulation models. Performance of adaptive Bayesian regularization was compared to conventional NCC and Bayesian regularization with fixed number of iterations. Results from uniform phantom simulation study show significant improvement in lateral displacement and strain estimation accuracy. For instance, at 1.5% lateral strain in a uniform phantom, Bayesian regularization with five iterations incurred a lateral strain error of 104.49%, which was significantly reduced using our adaptive approach to 27.51% (p  < 0.001). Contrast-to-noise (CNR e ) ratios obtained from inclusion phantom indicate improved lesion detectability for both axial and lateral strain images. For instance, at 1.5% lateral strain, Bayesian regularization with five iterations had lateral CNR e of -0.31 dB which was significantly increased using the adaptive approach to 7.42 dB (p  < 0.001). Similar results are seen with cardiac deformation modelling with improvement in myocardial strain images. In vivo feasibility was also demonstrated using data from a healthy murine heart. Overall, the proposed method makes Bayesian regularization robust for clinical and preclinical applications.

Study of the Relationship Between Ultrasound Strain Indices and Cognitive Decline for Vulnerable Carotid Plaque.

A relationship between ultrasound strain indices in carotid plaque to cognitive domains of executive and language function are studied in 42 symptomatic and 34 asymptomatic patients. The mean and standard deviation of the percentage stenosis were 72.10 ± 15.19 and 77.41 ± 11.20 for symptomatic and asymptomatic patients respectively. Pearson's correlation between axial, lateral and shear strain indices versus executive and language composite scores was performed.. A significant inverse correlation for both executive and language function for symptomatic patients to strain indices was found. On the other hand, for asymptomatic patients only executive function was inversely correlated with the corresponding strain indices. Our hypothesis that microemboli from vulnerable plaque and possible 'silent strokes' may be responsible for decline in executive function for both symptomatic and asymptomatic patients'. Strokes and transient ischemic attacks may be responsible for further cognitive decline in language function for symptomatic patients.

Hierarchical Motion Estimation With Bayesian Regularization in Cardiac Elastography: Simulation and In Vivo Validation.

Cardiac elastography (CE) is an ultrasound-based technique utilizing radio-frequency (RF) signals for assessing global and regional myocardial function. In this work, a complete strain estimation pipeline for incorporating a Bayesian regularization-based hierarchical block-matching algorithm, with Lagrangian motion description and myocardial polar strain estimation is presented. The proposed regularization approach is validated using finite-element analysis (FEA) simulations of a canine cardiac deformation model that is incorporated into an ultrasound simulation program. Interframe displacements are initially estimated using a hierarchical motion estimation framework. Incremental displacements are then accumulated under a Lagrangian description of cardiac motion from end-diastole (ED) to end-systole (ES). In-plane Lagrangian finite strain tensors are then derived from the accumulated displacements. Cartesian to cardiac coordinate transformation is utilized to calculate radial and longitudinal strains for ease of interpretation. Benefits of regularization are demonstrated by comparing the same hierarchical block-matching algorithm with and without regularization. Application of Bayesian regularization in the canine FEA model provided improved ES radial and longitudinal strain estimation with statistically significant ( ) error reduction of 48.88% and 50.16%, respectively. Bayesian regularization also improved the quality of temporal radial and longitudinal strain curves with error reductions of 78.38% and 86.67% ( ), respectively. Qualitative and quantitative improvements were also visualized for in vivo results on a healthy murine model after Bayesian regularization. Radial strain elastographic signal-to-noise ratio (SNRe) increased from 3.83 to 4.76 dB, while longitudinal strain SNRe increased from 2.29 to 4.58 dB with regularization.

GPU Accelerated Multilevel Lagrangian Carotid Strain Imaging.

A multilevel Lagrangian carotid strain imaging algorithm is analyzed to identify computational bottlenecks for implementation on a graphics processing unit (GPU). Displacement tracking including regularization was found to be the most computationally expensive aspect of this strain imaging algorithm taking about 2.2 h for an entire cardiac cycle. This intensive displacement tracking was essential to obtain Lagrangian strain tensors. However, most of the computational techniques used for displacement tracking are parallelizable, and hence GPU implementation is expected to be beneficial. A new scheme for subsample displacement estimation referred to as a multilevel global peak finder was also developed since the Nelder-Mead simplex optimization technique used in the CPU implementation was not suitable for GPU implementation. GPU optimizations to minimize thread divergence and utilization of shared and texture memories were also implemented. This enables efficient use of the GPU computational hardware and memory bandwidth. Overall, an application speedup of was obtained enabling the algorithm to finish in about 50 s for a cardiac cycle. Last, comparison of GPU and CPU implementations demonstrated no significant difference in the quality of displacement vector and strain tensor estimation with the two implementations up to a 5% interframe deformation. Hence, a GPU implementation is feasible for clinical adoption and opens opportunity for other computationally intensive techniques.

The Preservation of Cognition 1 Year After Carotid Endarterectomy in Patients With Prior Cognitive Decline.

BACKGROUND: Vascular cognitive decline is critically important in the course of atherosclerosis and stroke. OBJECTIVE: To explore the hypothesis that carotid endarterectomy (CEA) by removing an unstable plaque may slow the course of vascular cognitive decline in both symptomatic and asymptomatic patients. METHODS: Patients with clinically significant (>60%) carotid stenosis were studied preop and 1 yr post-CEA for clinical symptoms, vascular cognitive decline, instability of carotid plaque-presence of microemboli, brain white matter changes, and medical risk factors. RESULTS: Forty-six percent were classically symptomatic. All patients showed vascular cognitive decline at presentation which correlated with degree of plaque instability. Significant white matter hyperintensity changes (48.7%) and cerebral emboli (25%) were also seen at baseline in both classically symptomatic and asymptomatic. One year after CEA, both groups showed no decline in cognitive function and significant improvement in 2 tests (P = .028 and P = .013). Brain white matter hyperintensities were unchanged. Microemboli were reduced but remained present (17.86%). Improvement was predicted by the presence of hypertension (P = .001), or less advanced cognitive decline preoperatively (P = .009). CONCLUSION: This study demonstrates the importance of vascular cognitive decline in atherosclerotic disease. This is a function of the degree of instability of the atherosclerotic plaque more than the presence of stroke symptoms. It further suggests that atherosclerotic vascular cognitive decline need not be inevitable, and may be modified by treating hypertension and removal of the unstable plaque. This highlights the need for continued research on the cognitive effects of cerebrovascular disease and the synergistic benefits of intensive medical and surgical therapy.

Carotid atherosclerotic plaque instability and cognition determined by ultrasound-measured plaque strain in asymptomatic patients with significant stenosis.

OBJECTIVE This article describes the use of ultrasound measurements of physical strain within carotid atherosclerotic plaques as a measure of instability and the potential for vascular cognitive decline, microemboli, and white matter changes. METHODS Asymptomatic patients with significant (> 60%) carotid artery stenosis were studied for dynamic measures of plaque instability, presence of microemboli, white matter changes, and vascular cognitive decline in comparison with normative controls and premorbid state. RESULTS Although classically asymptomatic, these patients showed vascular cognitive decline. The degree of strain instability measured within the atherosclerotic plaque directly predicted vascular cognitive decline in these patients thought previously to be asymptomatic according to classic criteria. Furthermore, 26% of patients showed microemboli, and patients had twice as much white matter hyperintensity as controls. CONCLUSIONS These data show that physical measures of plaque instability are possible through interpretation of ultrasound strain data during pulsation, which may be more clinically relevant than solely measuring degree of stenosis. The data also highlight the importance of understanding that the definition of symptoms should not be limited to motor, speech, and vision function but underscore the role of vascular cognitive decline in the pathophysiology of carotid atherosclerotic disease. Clinical trial registration no.: NCT02476396 (clinicaltrials.gov).

Transcranial Doppler and Microemboli Detection: Relationships to Symptomatic Status and Histopathology Findings.

The purpose of this study was to determine the relationship between symptomatic status, transcranial Doppler (TCD) microemboli presence and plaque histopathology findings. TCD was performed on 60 patients (37 symptomatic, 23 asymptomatic) before undergoing clinically indicated carotid endarterectomy. The frequency of microemboli signals was not significantly different between symptomatic and asymptomatic subject groups (p = 0.88) and there were no differences observed in the macroscopic or histopathology scoring of these plaques (p-values all > 0.05). The presence of microemboli was associated with an ulceration score (regardless of symptomatic or asymptomatic status, p = 0.034), with a one-level increase in ulceration rating associated with an odds ratio of 5.86 (95% [CI] 1.55, 43.4). These findings suggest that both symptomatic and asymptomatic patients may have plaque with similar features of instability and ability to create emboli. Thus, identifying new ways to measure plaque instability may provide important information for optimizing treatment to prevent future stroke.