The Ultrasound Window Into Vascular Ageing: A Technology Review by the VascAgeNet COST Action

Arteriosclerosis; Ultrasound; Vascular ageingArteriosclerosi; Ecografia; Envelliment vascularArteriosclerosis; Ecografía; Envejecimiento vascularNon-invasive ultrasound (US) imaging enables the assessment of the properties of superficial blood vessels. Various modes can be used for vascular characteristics analysis, ranging from radiofrequency (RF) data, Doppler- and standard B/M-mode imaging, to more recent ultra-high frequency and ultrafast techniques. The aim of the present work was to provide an overview of the current state-of-the-art non-invasive US technologies and corresponding vascular ageing characteristics from a technological perspective. Following an introduction about the basic concepts of the US technique, the characteristics considered in this review are clustered into: 1) vessel wall structure; 2) dynamic elastic properties, and 3) reactive vessel properties. The overview shows that ultrasound is a versatile, non-invasive, and safe imaging technique that can be adopted for obtaining information about function, structure, and reactivity in superficial arteries. The most suitable setting for a specific application must be selected according to spatial and temporal resolution requirements. The usefulness of standardization in the validation process and performance metric adoption emerges. Computer-based techniques should always be preferred to manual measures, as long as the algorithms and learning procedures are transparent and well described, and the performance leads to better results. Identification of a minimal clinically important difference is a crucial point for drawing conclusions regarding robustness of the techniques and for the translation into practice of any biomarker.This article is based upon work from COST Action CA18216 VascAgeNet, supported by COST (European Cooperation in Science and Technology, www.cost.eu). A.G. has received funding from “La Caixa” Foundation (LCF/BQ/PR22/11920008). R.E.C is supported by the National Health and Medical Research Council of Australia (reference: 2009005) and by a National Heart Foundation Future Leader Fellowship (reference: 105636). J.A. acknowledges support from the British Heart Foundation [PG/15/104/31913], the Wellcome EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z], and the Cardiovascular MedTech Co-operative at Guy's and St Thomas' NHS Foundation Trust [MIC-2016-019]

AI in Medical Imaging Informatics: Current Challenges and Future Directions

This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine

Recent advances in vascular ultrasound imaging technology and their clinical implications

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

Texture Characterization of Carotid Atherosclerotic Plaque from B-mode Ultrasound Using Gabor Filters

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

Noninvasive assessment of age-related arterial changes using the carotid stress-strain relationship in vivo: a pilot study

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

Variation of longitudinal strain along the arterial wall adjacent to the asymptomatic carotid plaque

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

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke

Computer aided diagnosis based on medical image processing and artificial intelligence methods

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

Ultrasound-image-based texture variability along the carotid artery wall in asymptomatic subjects with low and high stenosis degrees: unveiling morphological phenomena of the vulnerable tissue

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