Carotid artery wall motion analysis from B-mode ultrasound using adaptive block matching: in silico evaluation and in vivo application.

Valid risk stratification for carotid atherosclerotic plaques represents a crucial public health issue toward preventing fatal cerebrovascular events. Although motion analysis (MA) provides useful information about arterial wall dynamics, the identification of motion-based risk markers remains a significant challenge. Considering that the ability of a motion estimator (ME) to handle changes in the appearance of motion targets has a major effect on accuracy in MA, we investigated the potential of adaptive block matching (ABM) MEs, which consider changes in image intensities over time. To assure the validity in MA, we optimized and evaluated the ABM MEs in the context of a specially designed in silico framework. ABM(FIRF2), which takes advantage of the periodicity characterizing the arterial wall motion, was the most effective ABM algorithm, yielding a 47% accuracy increase with respect to the conventional block matching. The in vivo application of ABM(FIRF2) revealed five potential risk markers: low movement amplitude of the normal part of the wall adjacent to the plaques in the radial (RMA(PWL)) and longitudinal (LMA(PWL)) directions, high radial motion amplitude of the plaque top surface (RMA(PTS)), and high relative movement, expressed in terms of radial strain (RSI(PL)) and longitudinal shear strain (LSSI(PL)), between plaque top and bottom surfaces. The in vivo results were reproduced by OF(LK(WLS)) and ABM(KF-K2), MEs previously proposed by the authors and with remarkable in silico performances, thereby reinforcing the clinical values of the markers and the potential of those MEs. Future in vivo studies will elucidate with confidence the full potential of the markers.

Automated detection of the carotid artery wall in longitudinal B-mode images using active contours initialized by the Hough transform.

In this paper, a fully automatic active-contour-based segmentation method is presented, for detecting the carotid artery wall in longitudinal B-mode ultrasound images. A Hough-transform-based methodology is used for the definition of the initial snake, followed by a gradient vector flow (GVF) snake deformation for the final contour detection. The GVF snake is based on the calculation of the image edge map and the calculation of GVF field which guides its deformation for the estimation of the real arterial wall boundaries. In twenty cases there was no significant difference between the automated segmentation and the manual diameter measurements. The sensitivity, specificity and accuracy were 0.97, 0.99 and 0.98, respectively, for both diastolic and systolic cases. In conclusion, the proposed methodology provides an accurate and reliable way to segment ultrasound images of the carotid artery.

Automated detection of the carotid artery wall in B-mode ultrasound images using active contours initialized by the Hough Transform.

Automatic segmentation of the arterial lumen from ultrasound images is an important and often challenging task in clinical diagnosis. We previously used the Hough Transform (HT) to automatically extract circles from sequences of B-mode ultrasound images of transverse sections of the carotid artery. In this paper, an active-contour-based methodology is suggested, initialized by the HT circle, in an attempt to extend previous findings and to accurately detect the arterial wall boundary. The methodology is based on the generation of a gradient vector flow field, an approach attempting to overcome conventional active contours constraints. Contour estimation is then achieved by deforming the initial curve (circle) based on the gradient vector flow field. In ten normal subjects, the specificity and accuracy of the segmentation were on average higher than 0.98, whereas the sensitivity was higher than 0.82. The methodology was also applied to four subjects with atherosclerosis, in which sensitivity, specificity and accuracy were comparable to those of normal subjects. In conclusion, the HT-initialized active contours methodology provides a reliable tool to detect the carotid artery wall in ultrasound images and can be used in clinical practice.

A mathematical model of the mechanical deformation of the carotid artery wall and its application to clinical data.

The study of arterial wall mechanics, including the study of stresses and strains experienced by the vascular wall, is pivotal in our understanding of arterial physiology. In this paper, a mathematical model is provided describing the deformation of the arterial wall in terms of 6 parameters. Actual deformation waveforms were also obtained from the analysis of B-mode ultrasound image sequences of the carotid artery using block-matching. The mathematical model was fitted to the clinical data using nonlinear least squares to determine the 6 parameters for 6 different locations along the posterior and 6 along the anterior walls, on the interface between the lumen and the intima-media complex (L-IM). On the posterior wall, 6 locations were also investigated at the interface between the intima-media complex and the adventitia (IM-A) as well as at the adventitia-surrounding tissue (A-T) boundary. The root mean square error was low for all locations indicating a good fit of the proposed model to the clinical data. The amplitude of the deformation, expressed through parameter alpha, was significantly lower in the A-T interface compared to the other two interfaces. The time when the systolic peak occurs, expressed through parameter t1, was significantly lower in the L-IM interface compared to the other two interfaces. Preliminary findings from a small group of diseased wall locations suggested that the parameters alpha, b and t1 were significantly different than healthy cases. This probably reflects alterations of arterial wall mechanics due to disease. This study showed that the proposed mathematical model is a satisfactory representation of the mechanical deformation of the carotid artery wall in the radial direction and can provide valuable information in the understanding of the mechanical behavior of the arterial wall.

Characterization of carotid atherosclerotic plaques using frequency-based texture analysis and bootstrap.

Texture analysis of B-mode ultrasound images of carotid atheromatous plaque can be valuable for the accurate diagnosis of atherosclerosis. In this paper, two frequency-based texture analysis methods based on the Fourier Power Spectrum and the Wavelet Transform were used to characterize atheromatous plaques. B-mode ultrasound images of 10 symptomatic and 9 asymptomatic plaques were interrogated. A total of 109 texture features were estimated for each plaque. The bootstrap method was used to compare the mean values of the texture features extracted from the two groups. After bootstrapping, three features were found to be significantly different between the two types of plaques: the average value of the angular distribution corresponding to the wedge centered at 90 degrees, the standard deviation at scale 1 derived from the horizontal detail image, and the standard deviation at scale 2 derived from the horizontal detail image. It is concluded that frequency-based texture analysis in combination with a powerful statistical technique, such as bootstrapping, may provide valuable information about the plaque tissue type.

Analysis and quantification of arterial wall motion from B-mode ultrasound images - comparison of block-matching and optical flow.

Motion of the carotid atheromatous plaque may be responsible for plaque rupture and cerebrovascular symptoms. B-mode ultrasound allows non-invasive recording of arterial wall and plaque motion. Our aim was to analyze quantitatively patterns of arterial wall motion with different techniques. Temporal sequences of digitized B-mode ultrasound images of the carotid arteries of 10 young healthy subjects were interrogated. Arterial wall motion was analyzed using: a/ block-matching, and b/ optical flow. The motion of selected regions of the luminal surface of the arterial wall was estimated using region tracking and block-matching. The motion of areas of the arterial wall was estimated using optical flow. Waveforms showing radial and axial displacements, as well as radial and axial velocities were produced for the selected ROIs using both techniques. Both techniques produced waveforms with peaks, corresponding to cardiac cycle events, that occurred at similar time points. To study the similarity of the waveforms obtained from the two techniques, a cross-correlation coefficient was calculated. Cross-correlation coefficients were 0.72..0.22 and 0.70..0.19 for displacements and velocities, respectively, in the radial direction. In the axial direction, the coefficients were 0.32..0.39 and 0.24..0.22 for displacements and velocities, respectively. On the basis of this relative comparison of methods, we conclude that significant observations can be made for each motion analysis technique in terms of characterization of the mechanical properties of the tissue.

Comparison of B-mode, M-mode and Hough transform methods for measurement of arterial diastolic and systolic diameters.

Measurements of arterial diameter during the cardiac cycle are increasingly used to study the mechanical properties of the arterial wall and changes associated with disease. In this paper, diastolic and systolic diameters of the carotid arteries were estimated from ultrasound imaging using the following three different procedures: a/ B-mode imaging with region tracking and block-matching, b/ M-mode imaging with automated edge detection and c/ automatic segmentation of the arterial lumen at diastole and systole using the Hough transform. Transverse images of the carotid artery were used, in which the arterial lumen has an almost circular appearance. The values for systolic and diastolic diameters estimated with the Hough transform, 0.69±0.04 and 0.61±0.06, respectively, were closer to those estimated with B-mode and motion tracking, 0.75±0.07 and 0.67±0.09. A large difference was found for a subject with an atherosclerotic vessel wall. It is concluded that the Hough transform can be efficiently used to automatically segment healthy arterial wall lumen from B-mode ultrasound images of the carotid artery, assuming a circular shape. In atherosclerotic vessel walls the assumption for circular shape may no longer be valid, and thus the use of an elliptical shape may be more appropriate.

A Communication Platform for Tele-monitoring and Tele-management of Type 1 Diabetes.

Type 1 diabetes mellitus is a chronic disease characterized by blood glucose levels out of normal range due to inability of insulin production. This dysfunction leads to many short- and long-term complications. In this paper, a system for tele-monitoring and tele-management of Type 1 diabetes patients is proposed, aiming at reducing the risk of diabetes complications and improving quality of life. The system integrates Wireless Personal Area Networks (WPAN), mobile infrastructure, and Internet technology along with commercially available and novel glucose measurement devices, advanced modeling techniques, and tools for the intelligent processing of the available diabetes patients information. The integration of the above technologies enables intensive monitoring of blood glucose levels, treatment optimisation, continuous medical care, and improvement of quality of life for Type 1 diabetes patients, without restrictions in everyday life activities.

Characterization of carotid atherosclerosis based on motion and texture features and clustering using fuzzy c-means.

Analysis of B-mode ultrasound images of the carotid atheromatous plaque includes the estimation of texture from static images and the estimation of motion from image sequences. The combination of these two types of information may be valuable for accurate diagnosis of vascular disease. The purpose of this paper was to study texture and motion patterns of carotid atherosclerosis and select the optimal combination of features that can characterize plaque. B-mode ultrasound images of 10 symptomatic and 9 asymptomatic plaques were interrogated. A total of 99 texture features were estimated using first-order statistics, second-order statistics, Laws texture energy and the fractal dimension. Only five texture features were significantly different between the two groups. In the same subjects, the motion of selected plaque regions was estimated using region tracking and block-matching and expressed through: a/maximal surface velocity (MSV), and b/maximal relative surface velocity (MRSV). MSV and MRSV were significantly lower in asymptomatic plaques suggesting more homogeneous motion patterns. Clustering using fuzzy c-means correctly classified 74% of plaques based on texture features only, and 79% of plaques based on motion features only. Classification performance reached 84% when a combination of motion and texture features was used.