Aaron Fenster – författare

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.

This edited book is divided into three sections covering 3D ultrasound devices, 3D ultrasound applications, and machine learning tools using 3D ultrasound imaging and written with physicians, engineers, and advanced graduate students in mind.

Features:

Provides descriptions of mechanical, tracking, and array approaches for generating 3D ultrasound images Details the applications of 3D ultrasound for diagnostic application and in image-guided intervention and surgery Explores the cutting-edge use of machine learning in detection, diagnosis, monitoring, and guidance for a variety of clinical 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.

This edited book is divided into three sections covering 3D ultrasound devices, 3D ultrasound applications, and machine learning tools using 3D ultrasound imaging and written with physicians, engineers, and advanced graduate students in mind.

Features:

Provides descriptions of mechanical, tracking, and array approaches for generating 3D ultrasound images Details the applications of 3D ultrasound for diagnostic application and in image-guided intervention and surgery Explores the cutting-edge use of machine learning in detection, diagnosis, monitoring, and guidance for a variety of clinical 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.This edited book is divided into three sections covering 3D ultrasound devices, 3D ultrasound applications, and machine learning tools using 3D ultrasound imaging and written with physicians, engineers, and advanced graduate students in mind.Features:Provides descriptions of mechanical, tracking, and array approaches for generating 3D ultrasound imagesDetails the applications of 3D ultrasound for diagnostic application and in image-guided intervention and surgeryExplores the cutting-edge use of machine learning in detection, diagnosis, monitoring, and guidance for a variety of clinical 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.This edited book is divided into three sections covering 3D ultrasound devices, 3D ultrasound applications, and machine learning tools using 3D ultrasound imaging and written with physicians, engineers, and advanced graduate students in mind.Features:Provides descriptions of mechanical, tracking, and array approaches for generating 3D ultrasound imagesDetails the applications of 3D ultrasound for diagnostic application and in image-guided intervention and surgeryExplores the cutting-edge use of machine learning in detection, diagnosis, monitoring, and guidance for a variety of clinical applications

Up-to-Date Details on Using Ultrasound Imaging to Help Diagnose Various DiseasesDue to improvements in image quality and the reduced cost of advanced features, ultrasound imaging is playing a greater role in the diagnosis and image-guided intervention of a wide range of diseases. Ultrasound Imaging and Therapy highlights the latest advances in using ultrasound imaging in image-guided interventions and ultrasound-based therapy. The book presents current and emerging techniques, identifies trends in the use of ultrasound imaging, and addresses technical and computational problems that need to be solved.The book is organized into three sections. The first section covers advances in technology, including transducers (2-D, 3-D, and 4-D), beamformers, 3-D imaging systems, and blood velocity estimation systems. The second section focuses on diagnostic applications, such as elastography, quantitative techniques for therapy monitoring and diagnostic imaging, and ultrasound tomography. The final section explains the use of ultrasound in image-guided interventions for image-guided biopsy and brain imaging.

Up-to-Date Details on Using Ultrasound Imaging to Help Diagnose Various DiseasesDue to improvements in image quality and the reduced cost of advanced features, ultrasound imaging is playing a greater role in the diagnosis and image-guided intervention of a wide range of diseases. Ultrasound Imaging and Therapy highlights the latest advances in usin

Up-to-Date Details on Using Ultrasound Imaging to Help Diagnose Various DiseasesDue to improvements in image quality and the reduced cost of advanced features, ultrasound imaging is playing a greater role in the diagnosis and image-guided intervention of a wide range of diseases. Ultrasound Imaging and Therapy highlights the latest advances in usin

Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images.

Prostatic adenocarcinoma (CAP) is the second most common malignancy with an estimated 190,000 new cases in the USA in 2010 (Source: American Cancer Society), and is the most frequently diagnosed cancer among men. If CAP is caught early, men have a high, five-year survival rate. Unfortunately there is no standardized ima- based screening protocol for early detection of CAP (unlike for breast cancers). In the USA high levels of prostate-specific antigen (PSA) warrant a trans-rectal ultrasound (TRUS) biopsy to enable histologic confirmation of presence or absence of CAP. With recent rapid developments in multi-parametric radiological imaging te- niques (spectroscopy, dynamic contrast enhanced MR imaging, PET, RF ultrasound), some of these functional and metabolic imaging modalities are allowing for definition of high resolution, multi-modal signatures for prostate cancer in vivo. Distinct com- tational and technological challenges for multi-modal data registration and classifi- tion still remain in leveraging this multi-parametric data for directing therapy and optimizing biopsy. Additionally, with the recent advent of whole slide digital sc- ners, digitized histopathology has become amenable to computerized image analysis. While it is known that outcome of prostate cancer (prognosis) is highly correlated with Gleason grade, pathologists often have difficulty in distinguishing between interme- ate Gleason grades from histopathology. Development of computerized image analysis methods for automated Gleason grading and predicting outcome on histopathology have to confront the significant computational challenges associated with working these very large digitized images.