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3D ultrasound techniques have been increasingly used in diagnosis, minimally invasive image-guided interventions, and intra-operative surgical use. Today, most ultrasound system manufacturers provide 3D imaging capability as part of the systems. This availability has stimulated researchers to develop various machine learning tools to automatically detect and diagnose diseases, such as cancer, monitor the progression and regression of diseases, such as carotid atherosclerosis, guide and track tools being introduced into the body, such as brachytherapy and biopsy needles.
3D ultrasound devices have evolved significantly over the years, offering various approaches for generating 3D ultrasound images. These include mechanical, tracking, and array approaches. Each of these methods has its own advantages and limitations, making them suitable for different clinical applications.
3D ultrasound has a wide range of applications in medicine, from diagnostic use to image-guided interventions and surgery. It provides detailed images of internal organs and structures, allowing for accurate diagnoses and precise treatment planning. In addition, 3D ultrasound can be used to monitor the progression of diseases and track the effectiveness of treatments.
Machine learning is revolutionizing the field of 3D ultrasound imaging by enabling the development of tools that can automatically detect and diagnose diseases, monitor disease progression, and guide interventions. These cutting-edge technologies are being used in a variety of clinical applications, including cancer detection, cardiovascular disease monitoring, and image-guided surgery.
For physicians, engineers, and advanced graduate students, this edited book provides a comprehensive overview of 3D ultrasound techniques, devices, and applications, as well as the latest advancements in machine learning tools using 3D ultrasound imaging. It serves as a valuable resource for those looking to understand the current state of the field and its future directions.
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