Computer aided diagnosis of coronary artery disease, myocardial infarction and carotid atherosclerosis using ultrasound images: a review

The diagnosis of Coronary Artery Disease (CAD), Myocardial Infarction (MI) and carotid atherosclerosis is of paramount importance, as these cardiovascular diseases may cause medical complications and large number of death. Ultrasound (US) is a widely used imaging modality, as it captures moving images and image features correlate well with results obtained from other imaging methods. Furthermore, US does not use ionizing radiation and it is economical when compared to other imaging modalities. However, reading US images takes time and the relationship between image and tissue composition is complex. Therefore, the diagnostic accuracy depends on both time taken to read the images and experience of the screening practitioner. Computer support tools can reduce the inter-operator variability with lower subject specific expertise, when appropriate processing methods are used. In the current review, we analysed automatic detection methods for the diagnosis of CAD, MI and carotid atherosclerosis based on thoracic and Intravascular Ultrasound (IVUS). We found that IVUS is more often used than thoracic US for CAD. But for MI and carotid atherosclerosis IVUS is still in the experimental stage. Furthermore, thoracic US is more often used than IVUS for computer aided diagnosis systems

Novel ultrasound features for the identification of the vulnerable carotid plaque

Background: The identification of the vulnerable carotid plaque is of paramount importance in order to prevent the significant stroke-related mortality and morbidity. Currently the clinical decision-making around this condition is based on the traditional ultrasound evaluation of the degree of stenosis. However, there is emerging evidence supporting that this is not sufficient for all patients. Aim of this thesis: The evaluation of novel carotid plaque features for the characterisation of plaque composition, volume and motion using 2 and 3 dimensional ultrasound technology. The ultimate goal is to identify novel sensitive imaging markers for carotid plaque characterisation and stroke-risk stratification. Methods: The Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) Study was a large prospective multicentre trial that was recently completed. A post-hoc analysis of the sonographic and clinical data from this study was performed in order to evaluate the effectiveness of novel ultrasound texture features, such as second order statics, on stroke-risk prediction. In addition, the change of specific texture features and degree of stenosis during the ACSRS follow-up time (8 years) and their importance for stroke prediction was evaluated. In order to assess the potential of 3D ultrasound carotid imaging we also developed a special methodology using a 3D broadband, linear array probe and the Q-lab software. This methodology was then applied in a clinical, cross-sectional study of patients with symptomatic and asymptomatic carotid disease. Finally we developed a carotid plaque motion analysis methodology that we tested on a feasibility study. Results: The post-hoc analysis of more than 1, 000 patients from the ACSRS database showed that there are novel ultrasound features of plaque homogeneity that can contribute to plaque characterisation and improve stroke-risk prediction. Similarly our results suggest that the change of degree of stenosis or plaque’s composition through time might have significant predictive value when combined with the above novel features. The study in 3D ultrasound prospectively assessed more than 80 people with symptomatic and asymptomatic carotid disease with both 2 and 3D carotid ultrasound without, though, revealing any significant benefit from the use of 3D imaging in terms of stroke-risk prediction. Finally, our feasibility study on plaque motion analysis showed that it is possible to objectively characterise plaque motion, using ultrasound and dedicated software without complicated reconstructions. Conclusion: The use of novel 2D ultrasound texture features in combination with traditional ones can improve the stroke-risk stratification. 3D ultrasound is a promising new approach, however, the current technology does not appear to offer a significant benefit in comparison to cheaper traditional 2D ultrasound for carotid plaque evaluation. Further research is warranted on this issue.Open Acces

Uses of ultrasound in stroke prevention

Ultrasound methods are useful in stroke prevention in several ways. Measurement of carotid plaque burden, as either total plaque area (TPA) or total plaque volume (TPV) are strong predictors of cardiovascular risk: much stronger than intima-media thickness, which does not represent true atherosclerosis, but a biologically and genetically distinct phenotype. Measurement of plaque burden is also useful for the study of genetics, and of new risk factors such as toxic products of the intestinal microbiome. Carotid plaque burden is highly correlated with and as predictive of risk as coronary calcium scores, but is less costly and does not require radiation. Furthermore, because carotid plaques change in time over a period of months, they can be used for a new approach to vascular prevention: “Treating arteries instead of treating risk factors”. In high-risk patients with asymptomatic carotid stenosis (ACS), this approach, implemented in 2003 in our clinics, was associated with a \u3e80% reduction of stroke and myocardial infarction over 2 years. “Treating arteries without measuring plaque would be like treating hypertension without measuring blood pressure”. Ultrasound methods can also be used to assess plaque vulnerability, by detecting echolucency, ulceration and plaque inhomogeneity on assessment of plaque texture. Transcranial Doppler (TCD) embolus detection is useful for risk stratification in patients with ACS; patients with two or more microemboli in an hour of monitoring have a 1-year risk of 15.6%, vs. 1% without microemboli, so this very clearly distinguishes which patients with ACS could benefit from intervention. TCD saline studies are more sensitive than trans-esophageal echocardiography for detection of patent foramen ovale, and more predictive of recurrent stroke. These methods should be more widely used, to reduce the increasing burden of stroke in our aging populations

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

Cyclodextrin Nanosponges: A Promising Approach for Modulating Drug Delivery

Nanotechnology showed great promise and impact on administration of therapeutic agents owing to its advantages over contemporary delivery systems. Nanoscale carriers like nanosponges represent a novel category of hyper cross-linked polymer structures with nanosized cavities which can be filled with variety of active moieties (hydrophilic as well as hydrophobic). These nanocarriers can circulate around the body until they found the specific target site and adhere on the surface and release the active moiety in a predictable and controlled manner, resulting in more effective delivery of a given dosage. Nanosponge technology helps to reduce drug associated side effects, improve stability, increase elegance and improve the flexibility of formulations, administered orally, parenterally and topically. Among nanosponges, cyclodextrin-based nanosponges (CDNS) are smart versatile carriers studied widely for drug delivery applications. Statistically, it have presented that approximately 40% of active moieties marketed currently and about 90% of active moieties in their preliminary phase confront problems regarding to solubility. In the past decade, the number of studies describing CDNS has dramatically increased. In the present chapter, scientists working in arena of nanotechnology can get an idea of fabrication, characterization and therapeutic utilities of nanosponges

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

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

In vivo quantification of metabolic activity in aortic aneurysms using PET

Objective: To investigate the role of hybrid 18F-FDG PET/CT as a potential risk-stratification tool of aneurysm expansion by measuring metabolic activity on PET and textural analysis on CT in abdominal aortic aneurysm (AAA). Histological markers of AAA wall inflammatory cell infiltrate and enzymatic degradation have been associated with increased 18F-Fluorodeoxyglucose (18F-FDG)-Positron Emission Tomography /Computed Tomography (PET/CT) uptake. Methods: Fifty patients with asymptomatic infrarenal AAA enrolled under surveillance at one of our institutions underwent 18F-FDG-PET/CT. Seventeen subjects were investigated for increased glucolysis in the AAA wall and optimal circulation imaging time for 18F-FDG. In 25 subjects the relationship between aneurysm metabolic activity and expansion was explored. Forty subjects had AAA CT textural analysis (CTTA) parameters performed on the CT component of PET/CT and were studied in relation to aneurysm expansion. Twenty-four subjects had circulating biomarkers analysed. Whole vessel assessment, region of interest analysis and the role of correcting for background blood pool activity were explored. Results: Thirteen of seventeen subjects investigated for increased 18F-FDG uptake had an AAA wall SUVmax > 2.5. In 17 subjects assessed for optimal circulation imaging time for 18F-FDG, no significant advantage in imaging at 3h over 1h after 18F-FDG injection was observed. 18F-FDG uptake correlated inversely with future AAA expansion in the preliminary group of 25 patients and in 40 subjects who also had CTTA. In subjects who had CTTA, coarse texture showed an inverse association with 18F-FDG uptake and medium coarse texture correlated with future AAA expansion. In 24 AAA patients who had serum biomarker assays, significantly higher levels of high sensitivity matrix metalloproteinase-9 (hsMMP-9) and hsMMP-2 compared to healthy controls were found. There was no correlation between AAA 18F-FDG uptake and levels of hsMMP-9, hsMMP-2, hs-interferon-γ and hs-C-reactive protein. Conclusions: In-vivo 18F-FDG PET/CT data indicated that small AAA show increased glucose metabolism. Relationships between AAA 18F-FDG uptake, CTTA and future expansion were identified. AAA18F-FDG PET/CT shows potential to identify subjects at risk of significant expansion. AAA metabolism may not relate to serum levels of certain inflammatory biomarkers