Quantifying atherosclerosis in vasculature using ultrasound imaging

Cerebrovascular disease accounts for approximately 30% of the global burden associated with cardiovascular diseases [1]. According to the World Stroke Organisation, there are approximately 13.7 million new stroke cases annually, and just under six million people will die from stroke each year [2]. The underlying cause of this disease is atherosclerosis – a vascular pathology which is characterised by thickening and hardening of blood vessel walls. When fatty substances such as cholesterol accumulate on the inner linings of an artery, they cause a progressive narrowing of the lumen referred to as a stenosis. Localisation and grading of the severity of a stenosis, is important for practitioners to assess the risk of rupture which leads to stroke. Ultrasound imaging is popular for this purpose. It is low cost, non-invasive, and permits a quick assessment of vessel geometry and stenosis by measuring the intima media thickness. Research is showing that 3D monitoring of plaque progression may provide a better indication of sites which are at risk of rupture. Various metrics have been proposed. From these, the quantification of plaques by measuring vessel wall volume (VWV) using the segmented media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, methods to segment these boundaries are required to help generate VWV measurements with high accuracy, less user interaction and increased robustness to variability in di↵erent user acquisition protocols.ii This work proposes three novel methods to address these requirements, to ultimately produce a highly accurate, fully automated segmentation algorithm which works on intensity-invariant data. The first method proposed was that of generating a novel, intensity-invariant representation of ultrasound data by creating phase-congruency maps from raw unprocessed radio-frequency ultrasound information. Experiments carried out showed that this representation retained the necessary anatomical structural information to facilitate segmentation, while concurrently being invariant to changes in amplitude from the user. The second method proposed was the novel application of Deep Convolutional Networks (DCN) to carotid ultrasound images to achieve fully automatic delineation of the MAB boundaries, in addition to the use of a novel fusion of amplitude and phase congruency data as an image source. Experiments carried out showed that the DCN produces highly accurate and automated results, and that the fusion of amplitude and phase yield superior results to either one alone. The third method proposed was a new geometrically constrained objective function for the network's Stochastic Gradient Descent optimisation, thus tuning it to the segmentation problem at hand, while also developing the network further to concurrently delineate both the MAB and LIB to produce vessel wall contours. Experiments carried out here also show that the novel geometric constraints improve the segmentation results on both MAB and LIB contours. In conclusion, the presented work provides significant novel contributions to field of Carotid Ultrasound segmentation, and with future work, this could lead to implementations which facilitate plaque progression analysis for the end�user

Radiomics and Machine Learning in the Prediction of Cardiovascular Disease

Carotid atherosclerosis is a major risk factor for ischaemic stroke which is a leading cause of death worldwide. For stroke survivors, 1 in 4 will have another stroke within five years. Carotid CT angiography (CTA) is commonly performed following an ischaemic stroke or transient ischemic attack to help guide patient management in the secondary prevention of stroke. For example, carotid endarterectomy surgery plus medical therapy or medical therapy alone. The degree of carotid stenosis is the mainstay in making this decision and uses only one aspect of anatomical information that can be obtained from a carotid CTA scan. Radiomics, sometimes called ‘texture analysis’, is the extraction of quantitative data from medical images that may not be apparent to the naked eye and has already demonstrated clinical utility in oncology for applications ranging from lesion characterisation to tumour grading and prognostication. Machine learning refers to the process of learning from experience (in this case data), rather than following pre-programmed rules. This thesis presents the findings of a proof-of-principle study to assess the value of radiomics in identifying the ‘vulnerable plaque’ and the ‘vulnerable patient’ within the context of cerebrovascular events. To evaluate the potential of radiomic features as imaging biomarkers, their reproducibility and robustness to morphological perturbations were assessed, as well as their biological associations with both PET and immunohistochemistry data. The ability of radiomic features to classify different carotid artery types, namely, culprit, non-culprit and asymptomatic carotid arteries was assessed using several machine learning classifiers. This was subsequently compared with a deep learning approach, which has greater capacity for data mining than feature-based machine learning approaches. Overall, radiomics could extract further useful information from carotid CTA scans. Culprit versus non-culprit carotid arteries in symptomatic patients and asymptomatic carotid arteries from asymptomatic patients had different radiomic profiles that could be leveraged using machine learning for better classification performance than carotid calcification or carotid PET imaging alone. Reliable and robust CT-based carotid radiomic features were identified that were associated with the degree of inflammation underlying the carotid artery. If validated with future prospective studies, this has the potential to improve personalised patient care in stroke management and advance clinical decision-making.Cambridge School of Clinical Medicine, the Medical Research Council's Doctoral Training Partnership and the Frank Edward Elmore Fun

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

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

Ultrasonic Pulse Wave Imaging for in vivo Assessment of Vascular Wall Dynamics and Characterization of Arterial Pathologies

Arterial diseases such as hypertension, carotid stenosis, and abdominal aortic aneurysm (AAA) may progress silently without symptoms and contribute to acute cardiovascular events such as heart attack, stroke, and aneurysm rupture, which are consistently among the leading causes of death worldwide. The arterial pulse wave, regarded as one of the fundamental vital signs of clinical medicine, originates from the heart and propagates throughout the arterial tree as a pressure, flow velocity, and wall displacement wave, giving rise to the natural pulsation of the arteries. The dynamic properties of the pulse wave are intimately related to the physical state of the cardiovascular system. Thus, the assessment of the arterial wall dynamics driven by the pulse wave may provide valuable insights into vascular mechanical properties for the early detection and characterization of arterial pathologies. The focus of this dissertation was to develop and clinically implement Pulse Wave Imaging (PWI), an ultrasound elasticity imaging-based method for the visualization and spatio-temporal mapping of the pulse wave propagation at any accessible arterial location. Motion estimation algorithms based on cross-correlation of the ultrasound radio-frequency (RF) signals were used to track the arterial walls and capture the pulse wave-induced displacements over the cardiac cycle. PWI facilitates the image-guided measurement of clinically relevant pulse wave features such as propagation speed (pulse wave velocity, or PWV), uniformity, and morphology as well as derivation of the pulse pressure waveform. A parametric study investigating the performance of PWI in two canine aortas ex vivo and 10 normal, healthy human arteries in vivo established the optimal image acquisition and signal processing parameters for reliable measurement of the PWV and wave propagation uniformity. Using this framework, three separate clinical feasibility studies were conducted in patients diagnosed with hypertension, AAA, and carotid stenosis. In a pilot study comparing hypertensive and aneurysmal abdominal aortas with normal controls, the AAA group exhibited significantly higher PWV and lower wave propagation uniformity. A “teetering” motion upon pulse wave arrival was detected in the smaller aneurysms ( 5.5 cm in diameter). While no significant difference in PWV or propagation uniformity was observed between normal and hypertensive aortas, qualitative differences in the pulse wave morphology along the imaged aortic segment may be an indicator of increased wave reflection caused by elevated blood pressure and/or arterial stiffness. Pulse Wave Ultrasound Manometry (PWUM) was introduced as an extension of the PWI method for the derivation of the pulse pressure (PP) waveform in large central arteries. A feasibility study in 5 normotensive, 9 pre-hypertensive, and 5 hypertensive subjects indicated that a significantly higher PP in the hypertensive group was detected in the abdominal aorta by PWUM but not in the peripheral arteries by alternative devices (i.e. a radial applanation tonometer and the brachial sphygmomanometer cuff). A relatively strong positive correlation between aortic PP and both radial and brachial PP was observed in the hypertensive group but not in the normal and pre-hypertensive groups, confirming the notion that PP variation throughout the arterial tree may not be uniform in relatively compliant arteries. The application of PWI in 10 stenotic carotid arteries identified phenomenon such as wave convergence, elevated PWV, and decreased cumulative displacement around and/or within regions of atherosclerotic plaque. Intra-plaque mapping of the PWV and cumulative strain demonstrated the potential to quantitatively differentiate stable (i.e. calcified) and vulnerable (i.e. lipid) plaque components. The lack of correlation between quantitative measurements (PWV, modulus, displacement, and strain) and expected plaque stiffness illuminates to need to consider several physiological and imaging-related factors such as turbulent flow, wave reflection, imaging location, and the applicability of established theoretical models in vivo. PWI presents a highly translational method for visualization of the arterial pulse wave and the image-guided measurement of several clinically relevant pulse wave features. The aforementioned findings collectively demonstrated the potential of PWI to detect, diagnose, and characterize vascular disease based on qualitative and quantitative information about arterial wall dynamics under pathological conditions

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio