Martin J. Murphy – författare

External-beam radiotherapy has long been challenged by the simple fact that patients can (and do) move during the delivery of radiation. Recent advances in imaging and beam delivery technologies have made the solution-adapting delivery to natural movement-a practical reality. Adaptive Motion Compensation in Radiotherapy provides the first detailed

External-beam radiotherapy has long been challenged by the simple fact that patients can (and do) move during the delivery of radiation. Recent advances in imaging and beam delivery technologies have made the solution—adapting delivery to natural movement—a practical reality. Adaptive Motion Compensation in Radiotherapy provides the first detailed treatment of online interventional techniques for motion compensation radiotherapy. This authoritative book discusses: Each of the contributing elements of a motion-adaptive system, including target detection and tracking, beam adaptation, and patient realignmentTreatment planning issues that arise when the patient and internal target are mobileIntegrated motion-adaptive systems in clinical use or at advanced stages of developmentSystem control functions essential to any therapy device operating in a near-autonomous manner with limited human interactionNecessary motion-detection methodology, repositioning techniques, and approaches to interpreting and responding to target movement data in real timeMedical therapy with external beams of radiation began as a two-dimensional technology in a three-dimensional world. However, in all but a limited number of scenarios, movement introduces the fourth dimension of time to the treatment problem. Motion-adaptive radiation therapy represents a truly four-dimensional solution to an inherently four-dimensional problem. From these chapters, readers will gain not only an understanding of the technical aspects and capabilities of motion adaptation but also practical clinical insights into planning and carrying out various types of motion-adaptive radiotherapy treatment.

External-beam radiotherapy has long been challenged by the simple fact that patients can (and do) move during the delivery of radiation. Recent advances in imaging and beam delivery technologies have made the solution—adapting delivery to natural movement—a practical reality. Adaptive Motion Compensation in Radiotherapy provides the first detailed treatment of online interventional techniques for motion compensation radiotherapy. This authoritative book discusses: Each of the contributing elements of a motion-adaptive system, including target detection and tracking, beam adaptation, and patient realignmentTreatment planning issues that arise when the patient and internal target are mobileIntegrated motion-adaptive systems in clinical use or at advanced stages of developmentSystem control functions essential to any therapy device operating in a near-autonomous manner with limited human interactionNecessary motion-detection methodology, repositioning techniques, and approaches to interpreting and responding to target movement data in real timeMedical therapy with external beams of radiation began as a two-dimensional technology in a three-dimensional world. However, in all but a limited number of scenarios, movement introduces the fourth dimension of time to the treatment problem. Motion-adaptive radiation therapy represents a truly four-dimensional solution to an inherently four-dimensional problem. From these chapters, readers will gain not only an understanding of the technical aspects and capabilities of motion adaptation but also practical clinical insights into planning and carrying out various types of motion-adaptive radiotherapy treatment.

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members ofapplied machine learning communities.

This book, now in an extensively revised and updated second edition, provides a comprehensive overview of both machine learning and deep learning and their role in oncology, medical physics, and radiology. Readers will find thorough coverage of basic theory, methods, and demonstrative applications in these fields. An introductory section explains machine and deep learning, reviews learning methods, discusses performance evaluation, and examines software tools and data protection. Detailed individual sections are then devoted to the use of machine and deep learning for medical image analysis, treatment planning and delivery, and outcomes modeling and decision support. Resources for varying applications are provided in each chapter, and software code is embedded as appropriate for illustrative purposes. The book will be invaluable for students and residents in medical physics, radiology, and oncology and will also appeal to more experienced practitioners and researchers and members ofapplied machine learning communities.

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.