Learning Ultrasound Imaging

This book offers a practical approach to the world of diagnostic ultrasound. It has been structured in a reader-friendly, case-based format that makes it easy and enjoyable to learn the basics of the applications and interpretation of ultrasound. Each case includes illustrations, descriptions of the imaging findings, and technical details and serves to identify the essential imaging features of the pathology under consideration, thus assisting the reader in the diagnosis of similar cases. The book is divided into 17 short chapters that review the most important areas of ultrasound application and also document the latest advances in the use of contrast and interventional ultrasound. The authors treat every topic from a “how to do it” perspective with the aim of imparting their wide experience in use of the technique. This book forms part of the Learning Imaging series for medical students, residents, less experienced radiologists, and other medical staff.

This book offers a practical approach to the world of diagnostic ultrasound. It has been structured in a reader-friendly, case-based format that makes it easy and enjoyable to learn the basics of the applications and interpretation of ultrasound. Each case includes illustrations, descriptions of the imaging findings, and technical details and serves to identify the essential imaging features of the pathology under consideration, thus assisting the reader in the diagnosis of similar cases. The book is divided into 17 short chapters that review the most important areas of ultrasound application and also document the latest advances in the use of contrast and interventional ultrasound. The authors treat every topic from a “how to do it” perspective with the aim of imparting their wide experience in use of the technique. This book forms part of the Learning Imaging series for medical students, residents, less experienced radiologists, and other medical staff.

This report defines the essential training and skills necessary for the effective employment of diagnostic ultrasound in different health care settings. It reviews the present situation in ultrasonography and the trends in the utilization of diagnostic ultrasound in clinical practice. The report also examines worldwide practice with respect to ultrasonography training for physicians, sonographers, and other categories of medical professionals, such as midwives, nurses, and assistant physicians. It presents outlines of recommended training curricula for physicians in general, advanced, and specialized ultrasonography and discusses various other factors that must be taken into account in organizing the training process. The report describes the recommended training for allied health professionals specializing in ultrasonography and presents the basic science and instrumentation aspects of the curricula. It then discusses the level of competence that should be reached by those trained in the use of diagnostic ultrasound, as well as the recommended standards for training programs, including the requirements for instructors and training centers. Recommendations for the implementation of training programs are included. An appendix outlines the applications of diagnostic ultrasound. (Contains 36 references.) (MDM).

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics Features self-contained chapters with a thorough literature review Assesses the development of future machine learning techniques and the further application of existing techniques

Ultrasound medical imaging stands out among the other diagnostic imaging modalities for its patient-friendliness, high temporal resolution, low cost, and absence of ionizing radiation. On the other hand, it may still suffer from limited detail level, low signal-to-noise ratio, and narrow field-of-view. In the last decade, new beamforming and image reconstruction techniques have emerged which aim at improving resolution, contrast, and clutter suppression, especially in difficult-to-image patients. Nevertheless, achieving a higher image quality is of the utmost importance in diagnostic ultrasound medical imaging, and further developments are still indispensable. From this point of view, a crucial role can be played by novel beamforming techniques as well as by non-conventional image formation techniques (e.g., advanced transmission strategies, and compounding, coded, and harmonic imaging). This Special Issue includes novel contributions on both ultrasound beamforming and image formation techniques, particularly addressed at improving B-mode image quality and related diagnostic content. This indeed represents a hot topic in the ultrasound imaging community, and further active research in this field is expected, where many challenges still persist.

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.

This book constitutes the refereed joint proceedings of the First International Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 10 full papers presented at SUSI 2019 and the 10 full papers presented at PIPPI 2019 were carefully reviewed and selected. The SUSI papers cover a wide range of medical applications of B-Mode ultrasound, including cardiac (echocardiography), abdominal (liver), fetal, musculoskeletal, and lung. The PIPPI papers cover the detailed scientific study of volumetric growth, myelination and cortical microstructure, placental structure and function.

Diagnostic and Therapeutic Ultrasound has recently taken an explosive growth for better safer, economic, mobile and high quality healthcare. This technology is very appealing for medical applications because it is non-ionizing, non-invasive and it is available in most of the medical and clinical facilities. Its low cost, when compared with other medical image modalities, makes it one of the preferred tools for medical monitoring, follow-up and diagnosis. Besides the traditional fields of Cardiology and Obstetrics, where it is extensively used for long time, it has became also very useful in the diagnosis of diseases of the prostate, liver and coronaries and carotids atherosclerosis. However, Ultrasound images present poor quality, very low signal to noise ratio and a lot of artifacts. The extraction of useful information from Ultrasound data for diagnosis is a challenge task that makes this medical image modality a very active field of research. The difficulties are being overcome and novel and advanced methods are being proposed for detection, characterization and segmentation of abnormalities in several organs. In fact, Ultrasound application range is vast, covering almost all organs of the human body, including the brain where Tran-cranial Doppler Ultrasound is very important to assess the brain vasculature. This book presents some of the recent advances in Ultrasound imaging technology covering several organs and techniques in a Biomedical Engineering (BME) perspective. The focus of the book is in the algorithms, methodologies and systems developed by multidisciplinary research teams of engineers and physicians for Computer-Aided Diagnosis (CAD) purposes. Cardiovascular and Cancer, the most common life-threatening diseases in western countries, are two of the most important topics focused in the book. However, other advanced issues are also presented such as Intravascular Ultrasound, 3D US and Ultrasound in Computer-Aided Surgery (CAS). Some chapters are direct contributions from medical research groups where Ultrasound has also received great attention in the last decade. By this, new techniques based on Ultrasound were introduced in the clinical practice for diagnosis and therapeutics, mainly in hospital facilities.