Search

In this paper recent advances in vascular ultrasound imaging technology are discussed, including threedimensional ultrasound (3DUS), contrast-enhanced ultrasound (CEUS) and strain- (SE) and shear-waveelastography (SWE). 3DUS imaging allows visualisation of the actual 3D anatomy and more recently of flow, and assessment of geometrical, morphological and mechanical features in the carotid artery and the aorta. CEUS involves the use of microbubble contrast agents to estimate sensitive blood flow and neovascularisation (formation of new microvessels). Recent developments include the implementation of computerised tools for automated analysis and quantification of CEUS images, and the possibility to measure blood flow velocity in the aorta. SE, which yields anatomical maps of tissue strain, is increasingly being used to investigate the vulnerability of the carotid plaque, but is also promising for the coronary artery and the aorta. SWE relies on the generation of a shear wave by remote acoustic palpation and its acquisition by ultrafast imaging, and is useful for measuring arterial stiffness. Such advances in vascular ultrasound technology, with appropriate validation in clinical trials, could positively change current management of patients with vascular disease, and improve stratification of cardiovascular risk.

The motion, and resulting strain, of the arterial wall nearby an atheromatous plaque is believed to be affected by the plaque and therefore may carry important information about disease status. Strain of the arterial wall in the longitudinal direction, i.e. along its length, has recently gained attention as determinant of cardiovascular risk. In this work, longitudinal strain (LS) was estimated along the wall proximal to asymptomatic carotid plaque, in an attempt to highlight the significance of arterial wall kinematics in atherosclerotic subjects without cerebrovascular events. Twenty five atheromatous carotid arteries of elderly male adults (59-81 y.o., 50-100% stenosis) were imaged in longitudinal sections, and videos of B-mode images were analysed using a previously developed method based on optical flow. The analysis consisted in the estimation of the longitudinal positions of three regions of interest (ROIs) on the posterior wall. LS was subsequently defined as the normalised difference of the longitudinal positions between two ROIs, and was assessed at two locations along the wall. A longitudinal strain index (LSI) was then calculated as the average of the amplitudes of the LS curves over 2-3 cardiac cycles. LSIs at location 1 (closer to the plaque) were significantly higher than at location 2 (4.74 +/- 1.66 vs. 2.54 +/- 1.08, p-value= 1.25E-06). This indicates that the wall closer to the plaque undergoes higher strain than the wall farther from it, which may be due to the effect of the presence and motion of the nearby lesion; an effect, which seems to be extended to the area around it. These findings show promise toward better understanding the complex mechanical phenomena taking place not only within the plaque but also in the neighbouring tissues.

The feasibility of noninvasively measuring regional carotid artery stiffness by way of the stress-strain relationship was recently demonstrated in young normal adults in vivo. In this paper, similar methods were used to assess the stress-strain curve and derive the Young’s moduli in young and older subjects to evaluate the sensitivity of this approach in assessing age-related wall changes. Two types of recordings were performed on the common carotid arteries of 3 young (23-24 years) and 2 older (37 and 43 years) subjects: (a) RF signals acquired at 505-642 Hz, and (b) the pulse pressure signal using applanation tonometry. Subsequently, (a) arterial strain was calculated from the diameter waveform obtained using the radial displacements estimated by a 1D cross-correlation technique on the RF signals, and (b) arterial stress was estimated using the pressure (tonometry) signal, the diameter, and the wall thickness measured on the B-mode. The strain and stress signals were combined to produce the stress-strain curve. Using bilinear curve fitting, the Young’s modulus of the elastin-collagen fibers (E-2), as well as the moduli for elastin (E-1) and collagen (E-3), separately, were estimated, under the assumption of a linear elastic two-parallel spring model. E-1 and E-2 were significantly larger in the older subjects, indicating stiffer tissues probably due to reduced elastin, which is in agreement with the related literature. No differences in E-3 were noted between young and older subjects. The method holds promise for characterizing age-related arterial changes and can provide useful insight into the complex phenomena involved in arterial biomechanics.

Valid identification of the vulnerable asymptomatic carotid atherosclerosis remains a crucial clinical issue. In this study, texture differences were estimated along the atherosclerotic arterial wall, namely at the plaque, the wall adjacent to it and the plaque shoulder, i.e. the boundary between wall and plaque, in an attempt to reveal morphological phenomena, representative of the high stenosis (considered vulnerable) cases. A total of 25 arteries were interrogated, 11 with low (50-69%) and 14 with high (70-100%) degrees of stenosis. The two groups had similar ages. Texture features were estimated from B-mode ultrasound images, and included four second-order statistical parameters (contrast, correlation, energy and homogeneity), each calculated at four different image directions (0 degrees, 45 degrees, 90 degrees, 135 degrees), yielding a total of 16 features. Texture differences between (a) wall and plaque and (b) wall and plaque shoulder were quantified as the differences in texture feature values for each tissue area normalised by the texture feature value of the wall, which was considered as reference, as illustrated in the following equation: dTF(i) = (TFi,W - TFi,P/S)/TFi,W, where dTF(i) the estimated texture difference, TFi,W the texture of the wall, and TFi,P/S the texture of the plaque (P) or the shoulder (S). Significant differences in texture variability of wall vs. shoulder were observed between high and low stenosis cases for 3 features at diastole and 7 features at systole. No differences were observed for wall vs plaque, although wall texture was significantly different than plaque texture, in absolute values. These findings suggest that texture variability along the atherosclerotic wall, which is indicative of tissue discontinuities, and proneness to rupture, can be quantitatively described with texture indices and reveal valuable morphological phenomena of the vulnerable tissue. (C) 2015 The Authors. Published by Elsevier B.V.

Asynchronous breathing movements may be observed in the presence of pulmonary disease, such as chronic obstructive pulmonary disease (COPD). This study was undertaken in an attempt to propose a reliable methodology to quantify this asynchrony. Five methods for estimating phase differences between two signals, based on the phase angle of the Fourier Transform (PhDFT), paradoxical motion (PhDPM), the Lissajous figure (PhDLF), maximal linear correlation (PhDP) and least-squares filtering (PhDLS), were compared. Frequency-modulated signals, simulating compartmental chest wall volumes, were used to evaluate the methods. Breathing asynchrony was quantified in two ways; by estimating (a) a single PhD value for the entire recording and (b) time-varying PhDs, representing non-stationarities of human breathing. PhDPM and PhDLS had the lowest average errors (4%), and PhDLS had a slightly higher error. PhDFT had zero error when estimating a single PhD value but a considerable error when estimating time-varying PhDs. PhDP presented the highest errors in all cases. An application of this methodology is proposed in real compartmental chest wall volume signals of normal and COPD subjects. Preliminary results indicate that the methodology is promising in quantifying differences in asynchronous breathing between thoracic volumes of COPD patients and healthy controls.

Texture analysis of B-mode ultrasound images of carotid atheromatous plaque can be valuable for the accurate diagnosis of atherosclerosis. In this paper, Gabor filters were used to characterize the texture of carotid artery atherosclerotic tissue. B-mode ultrasound images of 10 symptomatic and 9 asymptomatic plaques were interrogated. A total of 40 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, the mean value and the standard deviation of the energy estimated using the Gabor filters was found to be significantly different between symptomatic and asymptomatic plaques in the first scale of analysis and for all orientations. In addition, a number of texture features that correspond to larger resolution scales were found to be significantly different between the two types of plaques. It is concluded that Gabor-filter-based texture analysis in combination with a powerful statistical technique, such as bootstrapping, may provide valuable information about the plaque tissue type.

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atherotnatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84%, were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis. (c) 2006 Elsevier B.V. All rights reserved.