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Diagnostic Ultrasound Imaging provides a comprehensive introduction to and a state-of-the-art review of the essential science and signal processing principles of diagnostic ultrasound. The progressive organization of the material serves beginners in medical ultrasound science and graduate students as well as design engineers, medical physicists, researchers, clinical collaborators, and the curious. This it the most comprehensive and extensive work available on the core science and workings of advanced digital imaging systems, exploring the subject in a unified, consistent and interrelated manner. From its antecedents to the modern day use and prospects for the future, this it the most up-to-date text on the subject. Diagnostic Ultrasound Imaging provides in-depth overviews on the following major aspects of diagnostic ultrasound: absorption in tissues; acoustical and electrical measurements; beamforming, focusing, and imaging; bioeffects and ultrasound safety; digital imaging systems and terminology; Doppler and Doppler imaging; nonlinear propagation, beams and harmonic imaging; scattering and propagation through realistic tissues; and tissue characterization. * Based on the author's over thirty-five years of experience in developing laboratory methodology and standards and conducting research in ultrasound. * Conveys the fundamentals of diagnostic ultrasound as well as state-of-the-art reviews of major topics from a historical perspective. Matlab MATLAB problems and examples included. * MATLAB problems and examples included

Traces affective and aesthetic dimensions of medical imaging technologies Introduced in Phnom Penh around 1990, at the twilight of socialism and after two decades of conflict and upheaval, ultrasound took root in humanitarian and then privatized medicine. Services have since multiplied, promising diagnostic information and better prenatal and general health care. In Fixing the Image Jenna Grant draws on years of ethnographic and archival research to theorize the force and appeal of medical imaging in the urban landscape of Phnom Penh. Set within long genealogies of technology as tool of postcolonial modernity, and vision as central to skilled diagnosis in medicine and Theravada Buddhism, ultrasound offers stabilizing knowledge and elicits desire and pleasure, particularly for pregnant women. Grant offers the concept of \"fixing\"-which invokes repair, stabilization, and a dose of something to which one is addicted-to illuminate how ultrasound is entangled with practices of care and neglect across different domains. Fixing the Image thus provides a method for studying technological practice in terms of specific materialities and capacities of technologies-in this case, image production and the permeability of the body-illuminating how images are a material form of engagement between patients, between patients and their doctors, and between patients and their bodies.

Ultrasound Elastography for Biomedical Applications and MedicineIvan Z. Nenadic, Matthew W. Urban, James F. Greenleaf, Mayo Clinic Ultrasound Research Laboratory, Mayo Clinic College of Medicine, USAJean-Luc Gennisson, Miguel Bernal, Mickael Tanter, Institut Langevin – Ondes et Images, ESPCI ParisTech CNRS, FranceCovers all major developments and techniques of Ultrasound Elastography and biomedical applicationsThe field of ultrasound elastography has developed various techniques with the potential to diagnose and track the progression of diseases such as breast and thyroid cancer, liver and kidney fibrosis, congestive heart failure, and atherosclerosis. Having emerged in the last decade, ultrasound elastography is a medical imaging modality that can noninvasively measure and map the elastic and viscous properties of soft tissues.Ultrasound Elastography for Biomedical Applications and Medicine covers the basic physics of ultrasound wave propagation and the interaction of ultrasound with various media. The book introduces tissue elastography, covers the history of the field, details the various methods that have been developed by research groups across the world, and describes its novel applications, particularly in shear wave elastography.Key features:Covers all major developments and techniques of ultrasound elastography and biomedical applications.Contributions from the pioneers of the field secure the most complete coverage of ultrasound elastography available.The book is essential reading for researchers and engineers working in ultrasound and elastography, as well as biomedical engineering students and those working in the field of biomechanics.

The use of ultrasound has revolutionized the way in which many acute injuries and conditions are managed in emergency department and critical care areas in hospitals. Emergency departments nationwide are outfitted with ultrasound equipment, allowing acute conditions to be diagnosed within critical seconds. This book is a practical and concise introduction to bedside emergency ultrasound for all critical care physicians. It covers the full spectrum of conditions diagnosed via this modality, both for guiding invasive procedures as well as diagnosis in critical-care settings. It introduces the major applications for emergency ultrasound by using focused diagnostic questions and teaching the image-acquisition skills needed to answer these questions. Images of positive and negative findings for each application (FAST, ECHO, etc.) are presented, as well as scanning tips for improved image quality. Each section also contains a review of the literature supporting each application.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Objectives Modern advances in bioprosthetic valve tissue technology have resulted in the development of newer generation bioprosthetic aortic valves such as the Edwards Lifesciences® Inspiris ResiliaTM (Inspiris) and the Medtronic® Avalus™ Bioprosthesis (Avalus). These claim to exhibit improved hemodynamic sustainability, and prevention from structural valve deterioration, translating into long-term durability; in comparison to bioprosthetic valves previously used in surgical aortic valve replacement. While long term data on these newer generation valves does not yet exist to compare durability between these two groups, it is possible to compare hemodynamic profiles with the use of doppler echocardiography post-operatively. Our study sought to evaluate the effect of aortic valve type implanted on both max peak gradient (maxPG), and mean peak gradient (meanPG) across the aortic valve post-operatively, taking account of valve size used intraoperatively; through the use of a prospective, non-randomized, interventional study. Methods Patients who received a biologic aortic valve replacement between July 2017 and May 2021 in our centre were studied by echocardiography in the early post-operative period. Patients who received a sutureless aortic valve, and those who died prior to post-operative echocardiogram were excluded. The remaining population comprised 106 patients (Avalus = 23, Inspiris = 9). Other valves (n=74) including Edwards Lifesciences Perimount Magna Ease (n=14), LinaNova Aortic pericardial heart valve (n=22), Medtronic Mosaic Bioprosthesis (n=22), Medtronic Mosaic Ultra Bioprosthesis (n=6), St Jude Trifecta (n=9) and St Jude Epic bioprosthesis (n=1) were grouped and analysed collectively as older generation aortic valves. Results Edwards Lifesciences® Inspiris ResiliaTM were associated with statistically significant lower meanPGs in comparison to the meanPGs seen after implantation of Medtronic® Avalus™ Bioprosthesis of the same size (p= 0.001). In addition to this, despite the small sample size analysed, a statistically significant difference was seen in post-operative meanPGs between the Inspiris and the previous generation bioprosthetic aortic valves (p=0.007), taking the contributory effect of valve size on pressure gradient into account. In contrast, there appeared to be no statistically significant difference in hemodynamics post Avalus in comparison to the older generation bioprosthetic aortic valves. Conclusions Limitations of this study included the potential effect of other confounding factors such as ejection fraction and patient’s body surface area on post-operative doppler gradients, that were not accounted for in this study. Despite this, and the small sample size analysed, the results seen from Inspiris bioprosthesis appear promising in this small pilot study; showing favourable flow-gradient patterns at time of early post-operative transthoracic echocardiogram, in comparison to the flow-gradient patterns seen post-operatively following implantation of previous generation aortic valves.

IntroductionThe management of acute exacerbation of COPD (AECOPD) is complicated by the lack of a specific biomarker related to clinical course and readmission/treatment failure risk. As AECOPD are characterized by an acute worsening of lung hyperinflation and increased respiratory work, which can lead to diaphragm weakness and/or fatigue, we hypothesized that the serial monitoring of diaphragm function during an AECOPD could provide clinically relevant information on the clinical status of patients and their treatment failure risk.MethodsPatients with AECOPD requiring hospitalization in our center were prospectively recruited. Diaphragm thickening fraction (reported as the ratio of tidal to maximal thickening fractions of the diaphragm – TF%max) was measured using ultrasonography within 24h of admission and within 24h of discharge. The difference in TF%max value between admission and discharge was reported as ΔTF. In addition to clinical and demographic characteristics, National Early Warning Score (NEWS), COPD Assessment Test (CAT) and blood gases were retrieved at the time of admission. Treatment failure was defined as a readmission to the emergency department/hospital <30 days after discharge.Results18 patients were recruited [mean (±standard deviation) age 74±7 years, FEV139±17%, residual volume 153±69% and CAT score 26±6]. Mean TF%max decreased from 54±20% on admission to 43±19% at discharge (p=0.06). Mean ΔTF was -10±50%. 5 patients (28%) were readmitted within 30 days. In these patients, TF%max at the time of discharge and the change in TF%max during hospitalization were significantly different than in those without readmission (65±7 vs 35±16%, p=0.001 and 30±66 vs -27±35%, p=0.02, respectively) (figure 1). ΔTF was significantly correlated to length of hospital stay (rho=0.49, p=0.04), but TF%max, NEWS, CAT score and pCO2 measured on admission were not (all p>0.05).Abstract P43 Figure 1ConclusionsTF%max, measured using ultrasonography, is responsive to clinical evolution during episodes of AECOPD, and may be able to predict the risk of early readmission. Further data is required to better delineate the role of diaphragm ultrasound in this setting and to identify clinically relevant threshold values associated with negative outcomes.

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we propose an analytic framework for COVID-19 assessment able to consume ultrasound images captured by linear and convex probes. We analyze the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. The proposed explainable framework, called COVID-Net L2C-ULTRA, employs an efficient deep columnar anti-aliased convolutional neural network designed via a machine-driven design exploration strategy. Our experimental results confirm that the proposed extended linear-convex ultrasound augmentation learning significantly increases performance, with a gain of 3.9% in test accuracy and 3.2% in AUC, 10.9% in recall, and 4.4% in precision. The proposed method also demonstrates a much more effective utilization of linear probe images through a 5.1% performance improvement in recall when such images are added to the training dataset, while all other methods show a decrease in recall when trained on the combined linear-convex dataset. We further verify the validity of the model by assessing what the network considers to be the critical regions of an image with our contribution clinician.