A local angle compensation method based on kinematics constraints for non-invasive vascular axial strain computations on human carotid arteries.

Non invasive vascular elastography (NIVE) was developed to highlight atherosclerotic plaque constituents. However, NIVE motion estimates are affected by artifacts, such as an underestimation of deformations due to projected movement angles with respect to the ultrasound beam, movements of the operator or of the patient during image acquisition. The main objective of this work was to propose a local angle compensation method within small measurement windows for the axial strain based on kinematics constraints, and to introduce a filtering process on the strain time-varying curve to reduce as much as possible the impact of motion artifacts. With such preprocessing, we successfully quantified the strain behavior of near and far walls in longitudinal images of internal carotid arteries without (n=30) and with (n=21) significant atherosclerotic disease (greater than 50% stenosis). Maximum strain rates of 4.49% s(-1) for the healthy group and of 2.29% s(-1) for the atherosclerotic group were calculated on the far wall of internal carotid arteries; significant differences were found between these values (p=0.001). The minimum strain rates, also on the far wall of internal carotid arteries, of -3.68% s(-1) for the healthy group and of -1.89% s(-1) for the atherosclerotic group were significantly different as well (p=8×10(-4)). The mean systolic, diastolic and cumulated axial strains could also distinguish the two groups after normalization by the pressure gradient between acquired images. To conclude, the proposed techniques allowed to differentiate healthy and atherosclerotic carotid arteries and may help to diagnose vulnerable plaques.

Characterisation of carotid plaques with ultrasound elastography: feasibility and correlation with high-resolution magnetic resonance imaging.

OBJECTIVES: To evaluate the ability of ultrasound non-invasive vascular elastography (NIVE) strain analysis to characterise carotid plaque composition and vulnerability as determined by high-resolution magnetic resonance imaging (MRI). METHODS: Thirty-one subjects with 50 % or greater carotid stenosis underwent NIVE and high-resolution MRI of internal carotid arteries. Time-varying strain images (elastograms) of segmented plaques were generated from ultrasonic raw radiofrequency sequences. On MRI, corresponding plaques and components were segmented and quantified. Associations between strain parameters, plaque composition and symptomatology were estimated with curve-fitting regressions and Mann-Whitney tests. RESULTS: Mean stenosis and age were 72.7 % and 69.3 years, respectively. Of 31 plaques, 9 were symptomatic, 17 contained lipid and 7 were vulnerable on MRI. Strains were significantly lower in plaques containing a lipid core compared with those without lipid, with 77-100 % sensitivity and 57-79 % specificity (P < 0.032). A statistically significant quadratic fit was found between strain and lipid content (P < 0.03). Strains did not discriminate symptomatic patients or vulnerable plaques. CONCLUSIONS: Ultrasound NIVE is feasible in patients with significant carotid stenosis and can detect the presence of a lipid core with high sensitivity and moderate specificity. Studies of plaque progression with NIVE are required to identify vulnerable plaques. KEY POINTS: • Non-invasive vascular elastography (NIVE) provides additional information in vascular ultrasound • Ultrasound NIVE is feasible in patients with significant carotid stenosis • Ultrasound NIVE detects a lipid core with high sensitivity and moderate specificity • Studies on plaque progression with NIVE are required to identify vulnerable plaques.

A compensative model for the angle-dependence of motion estimates in noninvasive vascular elastography.

PURPOSE: Atherosclerosis of peripheral cerebral arteries can lead to stroke either by stenosis formation or plaque rupture. This pathology is initiated by the alteration of arterial wall mechanical properties shown to be assessable by ultrasound elastography. Recently, noninvasive vascular elastography (NIVE) was introduced for noninvasive imaging of the mechanical properties of superficial arteries as markers of vulnerable plaques. However, NIVE motion estimates are angle-dependent, with optimal scanning angle being represented by the alignment of tissue motion with ultrasound beam orientation. The objective of this study was to introduce a model that compensates for such angle-dependence in order to reduce the bias on strain estimates, namely, when investigating longitudinal vessel segments. METHODS: The model is based on the Lagrangian speckle model estimator (LSME) because the LSME assesses the 2D-deformation matrix required to compute the scanning angle. RESULTS: Experiments on vessel-mimicking phantoms indicated that such a model enables the estimation of scanning angle with less than 3-degrees error. The method was also validated in vivo in human carotid arteries where less than 4-degrees error was observed. In both cases, the compensative model estimated the inclination angles with low variability. CONCLUSION: Angle-dependence may be an important factor to consider in avoiding potentially distort clinical diagnoses. Results, reported in this article, suggest that the LSME-based compensative model might be considered as a very interesting and promising clinical tool for NIVE applications.

Performance evaluation of different implementations of the Lagrangian speckle model estimator for non-invasive vascular ultrasound elastography.

Non-invasive vascular ultrasound elastography (NIVE) was recently introduced to characterize mechanical properties of carotid arteries for stroke prevention. Using the Lagrangian speckle model estimator (LSME), the four components of the 2D deformation matrix (delta), which are the axial strain (delta(yy)) and shear (delta(yx)) and the lateral strain (delta(xx)) and shear (delta(xy)), can be computed. This paper overviews four different implementations of the LSME and addresses their reliability. These implementations include two unconstrained (L&M and L&M+) and one constrained (ITER(c)) iterative algorithms, and one optical flow-based (OF-based) algorithm. The theoretical frameworks were supported by biomechanical simulations of a pathology-free vessel wall and by one single layer vessel-mimicking phantom study. Regarding simulations, the four LSME implementations provided similar biases on axial motion parameters, except the L&M that outperformed other methods with a minimum strain bias of -3%. LSME axial motion estimates showed good consistence with theory, namely the OF-based algorithm that in a specific instance estimated delta(yy) with no relative error on the standard deviation. With regards to lateral motion parameters, ITER(c) exhibited a minimum strain bias of -8.5% when ultrasound beam and motion mostly run parallel, whereas L&M performs strain and shear estimates with less than 23% bias independently of orientations. The in vitro vessel phantom data showed LSME delta(yy) and delta(yx) maps that were qualitatively equivalent to theory, and noisy delta(xx) and delta(xy) elastograms. In summary, the authors propose to promote the OF-based LSME as an optimal choice for further applications of NIVE, because of its reliability to compute both axial strain and shear motion parameters and because it outperformed the other implementations by a factor of 30 or more in terms of processing time.