The updated version of the EFSUMB guidelines on the application of non-hepatic contrast-enhanced ultrasound (CEUS) deals with the use of microbubble ultrasound contrast outside the liver in the many established and emerging applications.

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The EFSUMB guidelines and recommendations for the clinical practice of contrast-enhanced ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Long Version)

The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey.

Contrast-enhanced ultrasound (CEUS) is increasingly being used to identify patients with carotid plaques that are vulnerable to rupture, so-called vulnerable atherosclerotic plaques, by assessment of intraplaque neovascularization. A complete overview of the strengths and limitations of carotid CEUS is currently not available. The aim of this systematic review was to provide a complete overview of existing publications on the role of CEUS in assessment of carotid intraplaque neovascularization. The systematic review of the literature yielded 52 studies including a total of 4660 patients (mean age: 66 y, 71% male) who underwent CEUS for the assessment of intraplaque neovascularization. The majority of the patients (76%) were asymptomatic and had no history of transient ischemic attack (TIA) or stroke. The assessment of intraplaque neovascularization was mostly performed using a visual scoring system; several studies used time-intensity curves or dedicated quantification software to optimize analysis. In 17 studies CEUS was performed in patients before carotid surgery (endarterectomy), allowing a comparison of pre-operative CEUS findings with histologic analysis of the tissue sample that is removed from the carotid artery. In a total of 576 patients, the CEUS findings were compared with histopathological analysis of the plaque after surgery. In 16 of the 17 studies, contrast enhancement was found to correlate with the presence and degree of intraplaque neovascularization on histology. Plaques with a larger amount of contrast enhancement had significantly increased density of microvessels in the corresponding region on histology. In conclusion, CEUS is a readily available imaging modality for the assessment of patients with carotid atherosclerosis, providing information on atherosclerotic plaques, such as ulceration and intraplaque neovascularization, which may be clinically relevant. The ultimate clinical goal is the early identification of carotid atherosclerosis to start early preventive therapy and prevent clinical complications such as TIA and stroke.

Contrast-Enhanced Ultrasound to Assess Carotid Intraplaque Neovascularization.

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Vulnerability and instability in carotid artery plaque has been assessed based on strain variations using noninvasive ultrasound imaging. We previously demonstrated that carotid plaques with higher strain indices in a region of interest (ROI) correlated to patients with lower cognition, probably due to cerebrovascular emboli arising from these unstable plaques. This work attempts to characterize the strain distribution throughout the entire plaque region instead of being restricted to a single localized ROI. Multiple ROIs are selected within the entire plaque region, based on thresholds determined by the maximum and average strains in the entire plaque, enabling generation of additional relevant strain indices. Ultrasound strain imaging of carotid plaques, was performed on 60 human patients using an 18L6 transducer coupled to a Siemens Acuson S2000 system to acquire radiofrequency data over several cardiac cycles. Patients also underwent a battery of neuropsychological tests under a protocol based on National Institute of Neurological Disorders and Stroke and Canadian Stroke Network guidelines. Correlation of strain indices with composite cognitive index of executive function revealed a negative association relating high strain to poor cognition. Patients grouped into high and low cognition groups were then classified using these additional strain indices. One of our newer indices, namely the average L  -  1 norm with plaque (AL1NWP) presented with significantly improved correlation with executive function when compared to our previously reported maximum accumulated strain indices. An optimal combination of three of the new indices generated classifiers of patient cognition with an area under the curve (AUC) of 0.880, 0.921 and 0.905 for all (n  =  60), symptomatic (n  =  33) and asymptomatic patients (n  =  27) whereas classifiers using maximum accumulated strain indices alone provided AUC values of 0.817, 0.815 and 0.813 respectively.

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Quantification of carotid artery plaque stability with multiple region of interest based ultrasound strain indices and relationship with cognition.

Spatio-temporal Quantification of Carotid Plaque Neovascularization on Contrast Enhanced Ultrasound: Correlation with Visual Grading and Histopathology

W. Dai

Papers

Spatio-temporal Quantification of Carotid Plaque Neovascularization on Contrast Enhanced Ultrasound: Correlation with Visual Grading and Histopathology