What is 3D Ultrasound: Devices, Applications, and Algorithms (2024)?
3D Ultrasound: Devices, Applications, and Algorithms is a 2024 CRC Press-edited book covering three-dimensional ultrasound devices, clinical applications, and machine learning algorithms. Edited by Aaron Fenster, it is intended primarily for physicians, engineers, and advanced graduate students interested in 3D ultrasound imaging, image-guided procedures, medical applications, and computational methods.
Introduction
Three-dimensional ultrasound has moved beyond being a purely technical imaging concept and has become an important area of research and clinical application.
The book 3D Ultrasound: Devices, Applications, and Algorithms brings together developments in ultrasound hardware, volumetric imaging applications, and computational methods in a single specialized reference.
First published in 2024 by CRC Press, the book is edited by Aaron Fenster and forms part of the Imaging in Medical Diagnosis and Therapy series. Its stated scope is particularly relevant to readers interested in how three-dimensional ultrasound is produced, applied to medical problems, and enhanced through algorithms and machine learning.
The book is designed for physicians, engineers, and advanced graduate students. Rather than presenting 3D ultrasound simply as an extension of conventional two-dimensional scanning, it examines the technology from several interconnected perspectives: the devices that acquire volumetric data, the clinical and research applications that use those data, and algorithms that process and interpret 3D ultrasound images.
This combination makes the book particularly interesting for readers working at the intersection of medical imaging, ultrasound engineering, image-guided intervention, biomedical engineering, and medical image analysis.
Book Overview
Full Book Title
3D Ultrasound: Devices, Applications, and Algorithms
Edition
First edition
Editor
Aaron Fenster
Publisher
CRC Press, an imprint of Taylor & Francis Group
Publication Year
2024
Medical Specialty
The book focuses on three-dimensional ultrasound imaging, with coverage extending across ultrasound device technology, medical imaging applications, image-guided procedures, and computational and machine-learning approaches.
Intended Audience
The book specifically identifies physicians, engineers, and advanced graduate students as its intended readers.
The contributor list also demonstrates the multidisciplinary nature of the work, with contributors affiliated with medical imaging, biomedical engineering, medical biophysics, electrical engineering, radiation oncology, urology, computer science, and related fields.
What This Book Covers
The central strength of 3D Ultrasound: Devices, Applications, and Algorithms is its three-part organization. The book is divided into the following:
- 3D Ultrasound Devices and Methods
- 3D Ultrasound Applications
- 3D Ultrasound Algorithms
This structure takes the reader from the technology used to create volumetric ultrasound data through clinical and research applications and finally to computational approaches for analyzing those data.
1. 3D Ultrasound Devices and Methods
The opening section addresses the technological foundations of 3D ultrasound.
The first chapter examines 2D arrays, including their relationship to conventional 1D arrays and their role in volumetric imaging. It discusses electronically controlled beam steering and focusing in both azimuthal and elevational directions, as well as the challenges involved in developing densely populated 2D transducer arrays.
The chapter also discusses technologies including piezoelectric materials, multilayer transducers, single-crystal materials, capacitive micromachined ultrasonic transducers (cMUTs), piezoelectric micromachined ultrasonic transducers (pMUTs), sparse arrays, row-column arrays, and ASIC-based fully sampled arrays.
These discussions demonstrate that 3D ultrasound is not simply a software development problem. The acquisition of high-quality volumetric data depends heavily on transducer architecture, electrical impedance, signal routing, beamforming, electronics, and probe design.
Chapter 2 focuses on mechanical 3D ultrasound scanning devices, while Chapter 3 addresses freehand 3D ultrasound, including its principles and clinical applications.
Together, these chapters establish the technological background necessary for understanding how three-dimensional ultrasound volumes are acquired.
2. 3D Ultrasound Applications
The second section moves from devices to practical medical applications.
It includes a chapter on 3D ultrasound algorithms and applications for liver tumor ablation, demonstrating the role of volumetric imaging in an interventional setting.
Another chapter examines 3D ultrasound in gynecologic brachytherapy, highlighting the use of volumetric ultrasound in image-guided therapy.
The section also covers 3D automated breast ultrasound, an important area in which automated volumetric acquisition can support breast imaging.
Musculoskeletal research is addressed in a dedicated chapter, followed by detailed discussions of carotid atherosclerosis, including local vessel-wall and plaque-volume evaluation.
The section also includes 3D ultrasound for prostate biopsy and carotid atherosclerosis segmentation from 3D ultrasound images.
This application-oriented organization is valuable because it demonstrates the range of problems for which volumetric ultrasound can be investigated—from oncology and image-guided intervention to breast imaging, musculoskeletal research, vascular disease, and prostate procedures.
3. 3D Ultrasound Algorithms
The third section focuses on computational methods for extracting useful information from volumetric ultrasound data.
Topics include segmentation of neonatal cerebral lateral ventricles, convolutional neural networks in ultrasound-based prostate brachytherapy, freehand 3D reconstruction in fetal ultrasound, and localization of standard planes in 3D fetal ultrasound.
The book's introductory material also identifies machine-learning applications such as automatic detection and diagnosis of disease, monitoring progression or regression of disease, and guiding or tracking tools introduced into the body.
Consequently, the algorithms section provides an important computational dimension to the book, connecting 3D ultrasound acquisition with image analysis, reconstruction, segmentation, and machine-learning methods.
Key Features
- Dedicated coverage of 3D ultrasound devices, applications, and algorithms.
- Multidisciplinary approach combining medical imaging, engineering, and computational methods.
- Discussion of 2D matrix arrays and transducer technology.
- Coverage of mechanical and freehand 3D ultrasound techniques.
- Clinical and research applications involving the liver, breast, prostate, carotid arteries, musculoskeletal system, fetal imaging, and brachytherapy.
- Dedicated discussion of machine-learning and convolutional neural network approaches.
- Coverage of image segmentation and 3D reconstruction.
- Contributions from specialists working across medical imaging, biomedical engineering, electrical engineering, radiation oncology, urology, computer science, and related disciplines.
- References throughout the technical chapters provide pathways for readers who want to explore individual topics further.
Who Should Read This Book?
Medical Students
Medical students with a particular interest in diagnostic imaging, ultrasound, biomedical engineering, or emerging medical technologies may find the book useful as an introduction to advanced 3D ultrasound concepts.
However, the book is specialized rather than a general introductory medical textbook. The stated target audience is physicians, engineers, and advanced graduate students.
Residents and Young Doctors
Residents and young physicians interested in radiology, imaging, image-guided procedures, oncology-related imaging, obstetric imaging, vascular imaging, or other areas represented in the book may benefit from its application-focused chapters.
Specialists
The clinical applications make the book potentially valuable to specialists who want to understand specific uses of volumetric ultrasound, particularly in areas such as liver tumor ablation, gynecologic brachytherapy, breast ultrasound, vascular disease, prostate biopsy, and fetal imaging.
Engineers and Biomedical Engineers
This is one of the book's strongest target groups. The detailed discussion of arrays, transducers, ASICs, beamforming, sparse-array approaches, and computational imaging makes it relevant to professionals involved in ultrasound system development.
Researchers
Researchers are likely to represent an important readership, particularly those working in ultrasound imaging, biomedical engineering, image processing, machine learning, medical image analysis, and image-guided intervention.
Exam Candidates
The book may serve as supplementary reading for students or clinicians preparing for examinations involving medical imaging.
Why This Book Is Useful
Clinical Relevance
A major advantage of the book is its connection between imaging technology and clinical applications.
Instead of restricting the discussion to transducer design, the book demonstrates how 3D ultrasound can be used in areas including tumor ablation, brachytherapy, breast imaging, musculoskeletal research, carotid atherosclerosis, prostate biopsy, and fetal imaging.
Technical Depth
The technology chapters provide substantial attention to the engineering challenges behind 3D ultrasound. For example, the discussion of 2D arrays covers element size, impedance, signal routing, beamforming, sparse arrays, and locally integrated ASICs.
This makes the book particularly valuable for readers who want to understand not merely what 3D ultrasound does but also how the underlying imaging systems are constructed.
Computational Imaging and Machine Learning
The dedicated algorithms section is another important feature. Segmentation, reconstruction, standard-plane localization, and convolutional neural networks demonstrate how computational methods can complement volumetric ultrasound acquisition.
Learning Efficiency
The three-section structure gives the book a logical progression:
Devices and methods → applications → algorithms
For readers already familiar with ultrasound, this organization can provide a useful way to move from hardware principles to medical applications and then to computational analysis.
Evidence and References
The technical chapters contain bibliographic references to research literature covering topics such as ultrasound arrays, cMUTs, sparse-array design, beamforming, and 3D imaging.
The book's publisher information also emphasizes that the material is intended as professional supplementary information and that clinical decisions should be made using appropriate professional judgment and current guidance.
Table of Contents Overview
The book contains 14 chapters divided into three sections.
Section I — 3D Ultrasound Devices and Methods
- 2D Arrays — Jesse Yen and Robert Wodnicki
- Mechanical 3D Ultrasound Scanning Devices — Aaron Fenster
- Freehand 3D Ultrasound: Principle and Clinical Applications — Elvis C. S. Chen, Leah A. Groves, Hareem Nisar, Patrick K. Carnahan, Joeana Cambranis-Romero, and Terry M. Peters
Section II — 3D Ultrasound Applications
- 3D Ultrasound Algorithms and Applications for Liver Tumor Ablation — Derek J. Gillies and Shuwei Xing
- The Use of 3D Ultrasound in Gynecologic Brachytherapy — Jessica Robin Rodgers
- 3D Automated Breast Ultrasound — Claire Keun Sun Park
- Applications of 3D Ultrasound in Musculoskeletal Research — Carla du Toit and colleagues
- Local Vessel Wall and Plaque Volume Evaluation of Three-Dimensional Carotid Ultrasound Images for Sensitive Assessment of the Effect of Therapies on Atherosclerosis — Bernard Chiu, Xueli Chen, and Yuan Zhao
- 3D Ultrasound for Biopsy of the Prostate — Jake Pensa, Rory Geoghegan, and Shyam Natarajan
- Carotid Atherosclerosis Segmentation from 3D Ultrasound Images — Ran Zhou
Section III — 3D Ultrasound Algorithms
- Segmentation of Neonatal Cerebral Lateral Ventricles from 3D Ultrasound Images — Zachary Szentimrey and Eranga Ukwatta
- A Review on the Applications of Convolutional Neural Networks in Ultrasound-Based Prostate Brachytherapy — Jing Wang, Tonghe Wang, Tian Liu, and Xiaofeng Yang
- Freehand 3D Reconstruction in Fetal Ultrasound — Mingyuan Luo, Xin Yang, and Dong Ni
- Localizing Standard Plane in 3D Fetal Ultrasound — Yuhao Huang, Yuxin Zou, Houran Dou, Xiaoqiong Huang, Xin Yang, and Dong Ni.
Strengths of the Book
The strongest academic feature of 3D Ultrasound: Devices, Applications, and Algorithms is its multidisciplinary scope.
Three-dimensional ultrasound requires expertise extending beyond conventional clinical sonography. The technology depends on transducer engineering, electronic systems, beamforming, image acquisition, reconstruction, image analysis, and increasingly machine-learning methods. The book reflects this multidisciplinary reality.
Another strength is its balance between foundational technology and medical applications. Readers can move from discussions of 2D arrays and scanning mechanisms to real clinical and research problems involving oncology, vascular disease, breast imaging, prostate procedures, musculoskeletal research, and fetal ultrasound.
The inclusion of dedicated algorithm chapters is also significant. It shows how modern 3D ultrasound increasingly depends on computational approaches for reconstruction, segmentation, plane localization, and automated analysis.
Finally, the contributor list demonstrates broad institutional and disciplinary participation, with authors associated with universities and research institutions in Canada, the United States, China, Hong Kong, and the United Kingdom.
Limitations
The book's specialization is both a strength and a potential limitation.
It is not presented as a general ultrasound textbook covering the entire field of clinical ultrasonography. Its focus is specifically on three-dimensional ultrasound devices, applications, and algorithms.
Readers seeking a basic introduction to ultrasound physics, routine sonographic examination techniques, or comprehensive coverage of conventional 2D clinical ultrasound may therefore need other educational resources.
The book is also technically oriented. Some chapters, particularly those dealing with arrays, transducers, beamforming, ASICs, reconstruction, and machine learning, are likely to be most useful to readers with an appropriate technical or scientific background.
Importantly, the book does not provide a stated board-examination framework, examination question bank, or standardized exam-preparation section in the listed contents.
Comparison With Similar Books
It occupies a specialized position: its focus is not simply clinical ultrasound interpretation but the combination of 3D ultrasound hardware, medical applications, and computational algorithms.
3D Ultrasound: Devices, Applications, and Algorithms (2024)
FAQs
It is a specialized 2024 book covering three-dimensional ultrasound technology, medical applications, and computational algorithms. Its three major sections address devices and methods, applications, and algorithms.
The book is edited by Aaron Fenster, a researcher and academic associated with the Robarts Research Institute and Western University.
The first edition was published in 2024 by CRC Press, an imprint of Taylor & Francis Group.
The book covers a broad range of 3D ultrasound applications, including liver tumor ablation, gynecologic brachytherapy, automated breast ultrasound, musculoskeletal research, carotid atherosclerosis, prostate biopsy, neonatal cerebral imaging, and fetal ultrasound.
Yes. The book's overview identifies machine-learning applications in 3D ultrasound, and the third section includes a chapter reviewing convolutional neural networks in ultrasound-based prostate brachytherapy.
It can be useful for medical students with an interest in medical imaging or advanced ultrasound technology. However, the stated intended audience is physicians, engineers, and advanced graduate students, so some technical chapters may be more appropriate for readers with prior knowledge.
Yes. The device and methods section addresses 2D arrays, mechanical scanning, and freehand 3D ultrasound, while the algorithms section covers segmentation, reconstruction, convolutional neural networks, and standard-plane localization.
The listed contents do not identify it as a dedicated board-examination preparation book. Its primary purpose is specialized education concerning 3D ultrasound devices, applications, and algorithms rather than exam preparation.
Conclusion
3D Ultrasound: Devices, Applications, and Algorithms provides a specialized look at an increasingly important area of medical imaging.
Its major educational value comes from bringing together three elements that are often studied separately: the devices used to acquire volumetric ultrasound data, the medical applications that use those data, and the algorithms used to reconstruct, segment, analyze, and interpret them.
The book's organization—from devices and methods through clinical applications and finally algorithms—provides a coherent framework for understanding modern 3D ultrasound.
Its chapters span diverse areas including liver tumor ablation, gynecologic brachytherapy, automated breast ultrasound, musculoskeletal research, carotid atherosclerosis, prostate biopsy, neonatal brain imaging, and fetal ultrasound.
For physicians and researchers, the clinical application chapters offer insight into where volumetric ultrasound is being used. For engineers and biomedical imaging researchers, the sections on arrays, transducers, beamforming, electronics, reconstruction, and machine learning provide a deeper technical perspective.
The book is therefore best viewed as a specialized multidisciplinary reference on 3D ultrasound, rather than a general clinical ultrasound textbook or examination guide. Readers interested in the technological and computational evolution of ultrasound imaging may find its combination of engineering concepts, medical applications, and algorithms particularly valuable.
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