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Adaptive Motion Compensation in Radiotherapy [Kõva köide]

Edited by (Virginia Commonwealth University, Richmond, USA)
  • Formaat: Hardback, 166 pages, kõrgus x laius: 276x219 mm, kaal: 470 g, 10 Tables, black and white; 23 Illustrations, color; 122 Illustrations, black and white
  • Sari: Imaging in Medical Diagnosis and Therapy
  • Ilmumisaeg: 14-Dec-2011
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1439821933
  • ISBN-13: 9781439821930
Teised raamatud teemal:
Adaptive Motion Compensation in RadiotherapySuurem pilt
  • Formaat: Hardback, 166 pages, kõrgus x laius: 276x219 mm, kaal: 470 g, 10 Tables, black and white; 23 Illustrations, color; 122 Illustrations, black and white
  • Sari: Imaging in Medical Diagnosis and Therapy
  • Ilmumisaeg: 14-Dec-2011
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1439821933
  • ISBN-13: 9781439821930
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781439821930.html
Märksõnad:
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 solutionadapting delivery to natural movementa 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 realignment Treatment planning issues that arise when the patient and internal target are mobile Integrated motion-adaptive systems in clinical use or at advanced stages of development System control functions essential to any therapy device operating in a near-autonomous manner with limited human interaction Necessary motion-detection methodology, repositioning techniques, and approaches to interpreting and responding to target movement data in real time

Medical 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.

Arvustused

"The book has contributions from many eminent scientists and each chapter is well written and informative. an informative and useful book that will be of particular interest to those seeking an overview of the different means of achieving adaptive motion compensation in radiotherapy that either are or may be available in the near future." Chris Bragg, Journal of Radiotherapy in Practice, 2013

Series Preface vii
Preface ix
About the Editor xi
List of Contributors
xiii
Introduction xv
Martin J. Murphy
1 Real-Time Tumor Localization
1(12)
Ruijiang Li
Laura I. Cervino
Steve B. Jiang
1.1 Introduction
1(1)
1.2 Tumor Localization Systems
1(5)
Radiographic Imaging
Electromagnetic Transponder
Fast MRI
Ultrasound
Positron Emission
Respiratory Monitoring Devices
1.3 Tumor Localization Methods
6(3)
Direct Methods
Indirect Methods
Hybrid Methods
1.4 Summary
9(4)
References
9(4)
2 Theoretical Aspects of Target Detection and Tracking
13(8)
Gregory C. Sharp
Rui Li
Nagarajan Kandasamy
2.1 The Likelihood Ratio Test
13(1)
2.2 Sensor Modeling
14(1)
2.3 Implications of Detection Theory
15(1)
2.4 Image-Based Tracking
15(1)
2.5 Template Selection and Motion Enhancement
15(1)
2.6 Matching Cost Functions
16(2)
Popular Matching Cost Functions
Robust Matching Cost Functions
2.7 Tracking and Prediction
18(3)
Single Object Tracking
Multiobject Tracking
References
20(1)
3 Respiratory Gating
21(8)
Geoffrey D. Hugo
Martin J. Murphy
3.1 Overview
21(1)
3.2 Historical Development
21(1)
3.3 Planning and Delivering a Gated Treatment
22(1)
3.4 Candidate Patients and Treatment Sites
22(1)
3.5 Candidate Breathing Behaviors and Respiratory Maneuvers
22(2)
Assessing Regularity and Stability of Respiration
Breath Hold
Feedback-Guided Free Breathing
Motion Restriction
3.6 Simulating and Planning a Gated Treatment
24(1)
3.7 Delivering a Gated Treatment
25(1)
3.8 Gating Quality Assurance and Control
26(1)
Commissioning a Gating System
Routine Quality Assurance
3.9 Limitations and Future Developments
27(2)
References
27(2)
4 The CyberKnife® Image-Guided Radiosurgery System
29(4)
Martin J. Murphy
4.1 Adaptation to Nonperiodic Movement
29(1)
4.2 Adaptation to Periodic (Respiratory) Motion
30(3)
References
31(2)
5 Fundamentals of Tracking with a Linac Multileaf Collimator
33(6)
Dualta McQuaid
Steve Webb
5.1 Introduction
33(1)
5.2 Intrafraction Breathing Motion
34(1)
5.3 The Early Work on Tracking One-Dimensional Motion
34(1)
5.4 The Fatal Flaw of Motion Deconvolution Attempts
35(1)
5.5 Tracking Two-Dimensional Motion
35(1)
5.6 Tracking Delivery Design by Direct Aperture Optimization in 4D
35(1)
5.7 Adaptive Therapy
36(1)
5.8 Conclusions
36(3)
Acknowledgments
36(1)
References
37(2)
6 Couch-Based Target Alignment
39(8)
Kathleen T. Malinowski
Warren D. D'Souza
6.1 Introduction
39(1)
6.2 Couch Shifts for Patient Alignment
39(2)
6.3 Transient and Low-Frequency Couch-Based Target Alignment
41(1)
6.4 Dynamic Couch-Based Target Motion Compensation
41(2)
6.5 Quality Assurance
43(1)
6.6 The Future of Couch-Based Motion Correction in Radiation Therapy
44(3)
References
45(2)
7 Robotic LINAC Tracking Based on Correlation and Prediction
47(18)
Floris Ernst
Achim Schweikard
7.1 Introduction
47(1)
7.2 Correlation
48(6)
Basic Correlation Methods
Advanced Correlation Methods
Validation Experiment
7.3 Prediction of Respiratory and Pulsatory Motion
54(5)
The MULIN Family of Algorithms
The SVRpred Algorithm
Validation Experiments
7.4 Discussion
59(1)
7.5 Outlook
60(5)
Fusion of Prediction and Correlation
Using Surrogates to Improve Prediction Quality
Additional Resources
61(1)
References
61(4)
8 Treatment Planning for Motion Adaptation in Radiation Therapy
65(12)
Alexander Schlaefer
8.1 Introduction
65(1)
8.2 Treatment Planning in Radiation Therapy
65(1)
8.3 Image Processing in Treatment Planning
66(3)
8.4 Planning for Motion-Compensated Treatment
69(3)
8.5 Intratreatment Motion Adaption during Treatment Planning
72(5)
References
73(4)
9 Treatment Planning for Motion Management via DMLC Tracking
77(16)
Lech Papiez
Dharanipathy Rangaraj
9.1 Introduction
77(1)
Dynamic Multileaf Collimator Intensity-Modulated Delivery
9.2 DMLC Tracking Leaf-Sequencing Evolution
78(1)
Basic Governing Equations
Summary of Algorithm Properties
9.3 DMLC Control Algorithms for 1D Moving and Deforming Targets
79(2)
Optimal Solutions Developed for a Rigid Moving Target Based on Data Collected Before Treatment
Optimal Solutions Developed for a Moving, Deforming Target with Motion Data Based on Prior Measurements
Self-Corrected Delivery: Dose Delivery and Motion Model Errors
9.4 DMLC Control Algorithms for 3D Moving Targets
81(3)
Synchronized MLC for Tongue and Groove
Synchronized MLC for Targets Moving in 3D
Real-Time Synchronized MLC for Targets Moving in 3D
9.5 Toward Motion-Optimized IMRT
84(2)
Organs at Risk Sparing: DMLC Control Algorithms with Constant Linear Accelerator Dose Rate
Organs at Risk Sparing: DMLC Control Algorithms with Variable Linear Accelerator Dose Rate
9.6 Motion Management in VMAT
86(2)
9.7 Summary
88(5)
Acknowledgment
88(1)
Appendix 9.A Derivation of the Basic Equations for MLC Tracking of Moving Targets
88(2)
Appendix 9.B Interdependence of Delivery Parameters of VMAT
90(1)
References
90(3)
10 Real-Time Motion Adaptation in Tomotherapy® Using a Binary MLC
93(14)
Weiguo Lu
Mingli Chen
Carl J. Mauer
Gustavo H. Olivera
10.1 Introduction
93(1)
10.2 Binary MLC and TomoTherapy® Treatment System
93(1)
10.3 Real-Time Motion Adaptation Strategies
94(3)
Motion-Adaptive Delivery
Motion-Adaptive Optimization
10.4 Simulations
97(2)
Synthetic Data
Clinical Data
10.5 System Integration and Experiments
99(2)
10.6 Discussion
101(2)
10.7 Conclusions
103(4)
References
103(4)
11 Combination of a LINAC with 1.5 T MRI for Real-Time Image Guided Radiotherapy
107(8)
Jan J.W. Lagendijk
Bas W. Raaymakers
Marco van Vulpen
11.1 Introduction
107(1)
11.2 Design Magnetic Resonance Linac
107(1)
11.3 Status
108(1)
11.4 Dosimetry and Treatment Planning
108(3)
11.5 Clinical Impact
111(4)
References
112(3)
12 The ViewRay™ System
115(14)
Daniel A. Low
Richard Stark
James F. Dempsey
12.1 Introduction
115(1)
12.2 Historical Perspective
115(4)
Cobalt and Conformal Therapy
Technical Developments
IMRT Changes the Game
Patient and Tumor Positioning
IGRT Begins for Radiotherapy: CT "Snapshots"
MRI versus CT Imaging for Intrafraction Organ Motion
12.3 Gamma-Ray IMRT
119(2)
Gamma-Ray IMRT Treatment Plans
12.4 Radiotherapy and MRI
121(8)
Magnetic Field Selection
The ViewRay™ System
References
126(3)
13 Fault Detection in Image-Based Tracking
129(6)
Gregory C. Sharp
Rui Li
Nagarajan Kandasamy
13.1 The Hokkaido RTRT System
129(1)
13.2 Tracking, Prediction, and Online Monitoring
130(1)
13.3 Human Factors
130(1)
13.4 Dependable Systems
131(1)
13.5 Theory of Reliable Systems---Hardware
131(2)
Failure Detection
Failure Recovery
13.6 Theory of Reliable Systems---Software
133(1)
Fault Detection
Failure Recovery
13.7 System Verification and Validation
134(1)
References
134(1)
Index 135
Dr. Martin J. Murphy received his Ph.D. in physics from the University of Chicago in 1980. Following postdoctoral fellowships in nuclear physics at the University of California/Berkeley and the University of Washington and a stint as a research scientist in gamma-ray astronomy at the Lockheed Palo Alto Research Laboratories, he entered the field of radiation therapy research and development in 1992 as Director of System Development of the CyberKnife at Accuray Incorporated. In 1995, he joined the Department of Radiation Oncology at Stanford University as a senior research scientist to continue development of the CyberKnifes image guidance and target tracking capabilities. In 2003, Dr Murphy joined the Department of Radiation Oncology at Virginia Commonwealth University, where he is presently engaged in several research programs involving medical image registration, CT reconstruction, and real-time motion-adaptive control systems.