3D Ultrasound Techniques: A Comprehensive Guide to Diagnosis, Interventions, and Machine Learning Applications

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

3D ultrasound devices use mechanical, tracking, and array approaches for generating 3D ultrasound images. These devices have been developed to provide detailed images of the body’s internal structures, allowing for better diagnostics and treatment planning.

Applications of 3D Ultrasound

3D ultrasound has a wide range of applications in diagnostic and interventional procedures. It is used in various clinical settings, including cardiology, oncology, and orthopedics, to provide detailed images of organs and tissues. Additionally, 3D ultrasound is used in image-guided interventions and surgery, such as guiding biopsies and tumor treatments.

Machine Learning Tools in 3D Ultrasound Imaging

Machine learning tools are being developed to enhance the capabilities of 3D ultrasound imaging. These tools can automatically detect and diagnose diseases, monitor the progression and regression of diseases, and guide and track tools being introduced into the body. This cutting-edge technology has the potential to revolutionize the field of medical imaging and treatment.

This edited book, authored by Aaron Fenster, 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 is an essential resource for physicians, engineers, and advanced graduate students interested in the field of medical imaging and diagnostics.