In this paper recent advances in vascular ultrasound imaging technology are discussed, including three-dimensional ultrasound (3DUS), contrast-enhanced ultrasound (CEUS) and strain- (SE) and shear-wave-elastography (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.

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.

Motion extracted from the carotid artery wall provides unique information for vascular health evaluation. Carotid artery longitudinal wall motion corresponds to the multiphasic arterial wall excursion in the direction parallel to blood flow during the cardiac cycle. While this motion phenomenon has been well characterized, there is a general lack of awareness regarding its implications for vascular health assessment or even basic vascular physiology. In the last decade, novel estimation strategies and clinical investigations have greatly advanced our understanding of the bi-axial behavior of the carotid artery, necessitating an up-to-date review to summarize and classify the published literature in collaboration with technical and clinical experts in the field. Within this review, the state-of-the art methodologies for carotid wall motion estimation are described, and the observed relationships between longitudinal-motion-derived indices and vascular health are reported. The vast number of studies describing the longitudinal motion pattern in plaque-free arteries, with its putative application to cardiovascular disease prediction, point to the need for characterizing the added value and applicability of longitudinal motion beyond established biomarkers. To this aim, the main purpose of this review is to provide a strong base of theoretical knowledge, together with a curated set of practical guidelines and recommendations for longitudinal motion estimation in patients, to foster future discoveries in the field, toward the integration of longitudinal motion in basic science as well as clinical practice.

Asynchronous movement of the carotid atheromatous plaque from B-mode ultrasound has been previously reported, and associated with higher risk of stroke, but not quantitatively estimated. Based on the hypothesis that asynchronous plaque motion is associated with vulnerable plaque, in this study, synchronisation patterns of different tissue areas were estimated using cross-correlations of displacement waveforms. In 135 plaques (77 subjects), plaque radial deformation was synchronised by approximately 50% with the arterial diameter, and the mean phase shift was 0.4 s. Within the plaque, the mean phase shifts between the displacements of the top and bottom surfaces were 0.2 s and 0.3 s, in the radial and longitudinal directions, respectively, and the synchronisation about 80% in both directions. Classification of phase-shift-based features using Random Forests yielded Area-Under-the-Curve scores of 0.81, 0.79, 0.89 and 0.90 for echogenicity, symptomaticity, stenosis degree and plaque risk, respectively. Statistical analysis showed that echolucent, high-stenosis and high-risk plaques exhibited higher phase shifts between the radial displacements of their top and bottom surfaces. These findings are useful in the study of plaque kinematics.

Stroke is a leading cause of morbidity and mortality worldwide, and characterization of vulnerable carotid plaque remains the spearhead of scientific research. Plaque destabilization, the key factor that induces the series of events leading to the clinical symptoms of carotid artery disease, is a consequence of complex mechanical, structural and biochemical processes. Novel imaging and molecular markers have been studied as predictors of disease outcome with promising results. The aim of this review is to present the current state of research on the association between ultrasound-derived echogenicity indices and blood parameters indicative of carotid plaque stability and activity. Bibliographic research revealed that there are limited available data. Among the biomarkers studied, those related to oxidative stress, lipoproteins and diabetes/insulin resistance are associated with echolucent plaques, whereas adipokines are associated with echogenic plaques. Biomarkers of inflammation and coagulation have not exhibited any conclusive relationship with plaque echogenicity, and it is not possible to come to any conclusion regarding calcification-, apoptosis- and neo-angiogenesis-related parameters because of the extremely limited bibliographic data.

The estimation of cardiovascular tissue motion from ultrasound images is a task of considerable importance but has remained difficult in clinical practice, mainly due to the limitations of ultrasound imaging and the complexity of tissue motion. This paper presents a survey of methodologies, along with physiologically relevant findings, regarding the estimation of motion of the myocardium and of central and peripheral arteries. Speckle tracking and modeling, and registration are the dominant methods used to calculate tissue displacements from sequences of images. Kinematic and mechanical indices are extracted from these displacements, which can provide valuable functional information about the cardiovascular system in normal and diseased conditions. An important application of motionbased strain indices involves the estimation of elastograms of the cardiovascular tissue. Motion analysis methods can be used to estimate a number of regional mechanical phenomena representing functional tissue properties, which are more sensitive to early changes due to ageing or disease. The importance of these methods lies in their potential to quantify in vivo tissue properties and to identify novel noninvasive personalized disease markers, toward early detection and optimal management of disease, along with increased patient safety. Their clinical usefulness remains to be demonstrated in larg trials.

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risks held by CA for both patient safety and state economies. This paper investigated the unexplored potential of kinematic features in assisting the diagnostic decision for CA in the framework of a computer-aided diagnosis (CAD) tool. To this end, 15 CAD schemes were designed and were fed with a wide variety of kinematic features of the atherosclerotic plaque and the arterial wall adjacent to the plaque for 56 patients from two different hospitals. The CAD schemes were benchmarked in terms of their ability to discriminate between symptomatic and asymptomatic patients and the combination of the Fisher discriminant ratio, as a feature-selection strategy, and support vector machines, in the classification module, was revealed as the optimal motion-based CAD tool. The particular CAD tool was evaluated with several cross-validation strategies and yielded higher than 88% classification accuracy; the texture-based CAD performance in the same dataset was 80%. The incorporation of kinematic features of the arterial wall in CAD seems to have a particularly favorable impact on the performance of image-data-driven diagnosis for CA, which remains to be further elucidated in future prospective studies on large datasets.

Purpose: Clozapine is an atypical neuroleptic agent, effective in treating drug-resistant schizophrenia. The aim of this work was to investigate overall sleep architecture and sleep spindle morphology characteristics, before and after combination treatment with clozapine, in patients with drug-resistant schizophrenia who underwent polysomnography. Methods: Standard polysomnographic techniques were used. To quantify the sleep spindle morphology, a modeling technique was used that quantifies time-varying patterns in both the spindle envelope and the intraspindle frequency. Results: After combination treatment with clozapine, the patients showed clinical improvement. In addition, their overall sleep architecture and, more importantly, parameters that quantify the time-varying sleep spindle morphology were affected. Specifically, the results showed increased stage 2 sleep, reduced slow-wave sleep, increased rapid eye movement sleep, increased total sleep time, decreased wake time after sleep onset, as well as effects on spindle amplitude and intraspindle frequency parameters. However, the above changes in overall sleep architecture were statistically nonsignificant trends. Conclusions: The findings concerning statistically significant effects on spindle amplitude and intraspindle frequency parameters may imply changes in cortical sleep EEG generation mechanisms, as well as changes in thalamic pacing mechanisms or in thalamo-cortical network dynamics involved in sleep EEG generation, as a result of combination treatment with clozapine. Significance: Sleep spindle parameters may serve as metrics for the eventual development of effective EEG biomarkers to investigate treatment effects and pathophysiological mechanisms in schizophrenia.

Optimal treatment of chronic total occlusions (CTOs) remains one of the major challenges in interventional cardiology. A number of factors, including both patient clinical conditions and technical procedural considerations, have been identified to affect percutaneous coronary intervention (PCI) success and long-term outcomes, in large multicenter cohorts as well as smaller patient groups. As opposed to patient-centered factors, technical factors can be managed and as a result, a lot of research aims at improving stent technology and imaging guidance, toward enhancing PCI efficiency, in regards to patient safety.

Carotid atherosclerosis is the main cause of fatal cerebral ischemic events, thereby posing a major burden for public health and state economies. We propose a web-based platform named CAROTID to address the need for optimal management of patients with carotid atherosclerosis in a twofold sense: (a) objective selection of patients who need carotid-revascularization (i.e., high-risk patients), using a multifaceted description of the disease consisting of ultrasound imaging, biochemical and clinical markers, and (b) effective storage and retrieval of patient data to facilitate frequent follow-ups and direct comparisons with related cases. These two services are achieved by two interconnected modules, namely the computer-aided diagnosis (CAD) tool and the intelligent archival system, in a unified, remotely accessible system. We present the design of the platform and we describe three main usage scenarios to demonstrate the CAROTID utilization in clinical practice. Additionally, the platform was evaluated in a real clinical environment in terms of CAD performance, end-user satisfaction and time spent on different functionalities. CAROTID classification of high- and low-risk cases was 87%; the corresponding stenosis-degree-based classification would have been 61%. Questionnaire-based user satisfaction showed encouraging results in terms of ease-of-use, clinical usefulness and patient data protection. Times for different CAROTID functionalities were generally short; as an example, the time spent for generating the diagnostic decision was 5min in case of 4-s ultrasound video. Large datasets and future evaluation sessions in multiple medical institutions are still necessary to reveal with confidence the full potential of the platform.

Automatic segmentation of the arterial lumen from ultrasound images is an important task in clinical diagnosis. Carotid artery recognition, the first task in lumen segmentation, should be performed in a fully automated, fast, and reliable way to further facilitate the low-level task of arterial delineation. In this paper, a user-independent, real-time algorithm is introduced for carotid artery localization in longitudinal B-mode ultrasound images. The proposed technique acts directly on the raw image, and exploits basic statistics along with anatomical knowledge. The method's evaluation and parameter value optimization were performed on a threefold cross validation basis. In addition, the introduced algorithm was systematically compared with another algorithm for common carotid artery recognition in B-mode scans, separately for multi-frame and single-frame data. The data sets used included 2,149 images from 100 subjects taken from three different institutions and covering a wide range of possible lumen and surrounding tissue representations. Using the optimized values, the carotid artery was recognized in all the processed images in both multi-frame and single-frame data. Thus, the introduced technique will further reinforce automatic segmentation in longitudinal B-mode ultrasound images.

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.

Stroke is a serious and frequent cerebrovascular disease with an enormous socioeconomic burden worldwide. Stroke prevention includes treatment of carotid atherosclerosis, the most common underlying cause of stroke, according to a specific diagnostic algorithm. However, this diagnostic algorithm has proved insufficient for a large number of mostly asymptomatic subjects, which poses a significant research challenge of identifying novel personalized risk markers for the disease. This paper illustrates the potential of carotid ultrasound image analysis toward this direction, with ultrasound imaging being a low-cost and noninvasive imaging modality and ultrasound-image-based features revealing valuable information on plaque composition and stability. A concise report of state-of-the-art studies in the field is provided and a perspective for clinical scenario for optimal management of atherosclerotic patients is described. Challenges and necessary future steps toward the realization of this scenario are discussed in an attempt to urge and orient future research, and mainly include systematic applications to sufficiently large patient samples, appropriately designed longitudinal studies, confirmation with histological results, and clinical trials.

Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OF (HS)), weighted least-squares optical flow (OF (LK(WLS))), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF (LK(WLS)) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OF (HS) (11.57 pixels). Due to its superior performance, OF (LK(WLS)) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OF (LK(WLS)) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects.