Monte Carlo Techniques in Radiation Therapy: Introduction, Source Modelling, and Patient Dose Calculations (Imaging in Medical Diagnosis and Therapy)

About ten years after the first edition comes this second edition of Monte Carlo Techniques in Radiation Therapy: Introduction, Source Modelling and Patient Dose Calculations , thoroughly updated and extended with the latest topics , edited by Frank Verhaegen and Joao Seco. The book aims to provide a brief introduction to the history and basics of Monte Carlo simulation, but again has a strong focus on applications in radiotherapy. Since the first edition, Monte Carlo simulation has found many new applications, which were included in detail. The applications sections in this book cover: Modelling transport of photons, electrons, protons and ions Modelling radiation sources for external beam radiotherapy Modelling radiation sources for brachytherapy Design of radiation sources Modelling dynamic beam delivery Patient dose calculations in external beam radiotherapy Patient dose calculations in brachytherapy Use of Artificial Intelligence in Monte Carlo simulations This book is intended for both students or professionals, both novice and experienced, in medical radiotherapy physics. The book combines overviews of development, methods and references to facilitate Monte Carlo studies. Cover Half Title Title Page Copyright Page Dedication Table of Contents Preface to the First Edition Preface to the Second Edition Editors Contributors PART I: Introduction 1 History of Monte Carlo 1.1 Motivating Monte Carlo 1.2 Monte Carlo in Medical Physics 1.3 EGSx Code Systems 1.4 Application: Ion Chamber Dosimetry 1.5 Early Radiotherapy Applications 1.6 The Future of Monte Carlo Appendix: Monte Carlo and Numerical Quadrature Acknowledgment References 2 Basics of Monte Carlo Simulations 2.1 Monte Carlo Method 2.1.1 Random Number Sampling 2.1.2 Numerical Integration 2.1.3 Nonuniform Sampling Methods 2.2 Monte Carlo Transport in Radiation Therapy 2.2.1 Analog Particle Transport 2.2.2 Charged Particle Transport 2.2.2.1 Condensed History (CH) Technique 2.2.2.2 Continuous Energy Loss 2.2.2.3 Multiple Scattering 2.2.2.4 Transport Mechanics 2.2.2.5 Boundary Crossing 2.2.3 Cross Sections 2.2.3.1 Photon Interaction Coefficients 2.2.3.2 Charged Particle Stopping and Scattering Powers References 3 Variance Reduction Techniques 3.1 Introduction 3.1.1 Calculation Efficiency 3.1.2 Hardware Performance Improvements 3.1.3 Approximate Methods 3.1.4 Condensed History Electron Transport 3.2 Basic Variance Reduction Techniques 3.2.1 Uniform Particle Splitting 3.2.2 Russian Roulette 3.2.3 Range Rejection 3.2.4 Cross-Section Enhancement 3.2.5 Interaction Forcing 3.2.6 Exponential Transform 3.2.7 Woodcock Tracking 3.2.8 Correlated Sampling 3.2.9 Initial Calculation of the Primary Interaction Density 3.2.10 Quasi-Random Numbers 3.3 Advanced Variance Reduction Techniques 3.3.1 Selective Bremsstrahlung Splitting 3.3.2 Directional Bremsstrahlung Splitting 3.3.3 Macro Monte Carlo 3.3.4 History Repetition 3.3.5 Simultaneous Transport of Particle Sets 3.3.6 Continuous Boundary Crossing 3.3.7 Multiple Photon Transport 3.4 Pure Approximate Variance Reduction Techniques 3.4.1 KERMA Approximation 3.4.2 Continuous Slowing Down Approximation 3.4.3 Transport Parameter Optimization References PART II: Source Modelling 4 Monte Carlo Modelling of External Photon Beams in Radiotherapy 4.1 Introduction 4.2 Photon Beams from Clinical Linacs 4.2.1 Components of a Monte Carlo Model of a Linac Photon Beam 4.2.1.1 Primary Electron Beam Distribution and Photon Target 4.2.1.2 Flattening Filter 4.2.1.3 Monitor Ion Chamber Backscatter 4.2.1.4 Wedges 4.2.1.5 Multileaf Collimators and Dynamic Therapy 4.2.2 Full Linac Modelling 4.2.2.1 Phase-Space Models 4.2.2.2 Source Models 4.2.2.3 Absolute Dose Calculations (Monitor Unit Calculations) 4.2.3 Stereotactic Beams 4.2.4 Contaminant Particles 4.2.4.1 Electrons 4.2.4.2 Neutrons 4.2.5 Radiotherapy Kilovolt X-Ray Units 4.2.6 [sup(60)]Co Teletherapy Units 4.3 Summary References 5 Monte Carlo Modelling of External Electron Beams in Radiotherapy 5.1 Introduction 5.2 Electron Beams from Clinical Linacs 5.2.1 Early Work in Electron Beam Modelling 5.2.2 Monte Carlo Modelling of Complete Electron Linac Beams 5.2.2.1 First Full Linac Models 5.2.2.2 Applications of the BEAM Code 5.2.2.3 Studies for Electron Treatment Planning 5.2.2.4 Other Studies with Electron Beams 5.3 Summary References 6 Monte Carlo Techniques in Brachytherapy: Basics and Source and Detector Modelling 6.1 Introduction 6.2 MC Techniques in Brachytherapy 6.2.1 Simulations of Radiation Transport 6.2.2 Cross Sections 6.2.3 Scoring Functions 6.2.4 Approaches to Enhance Simulation Efficiency 6.3 Single-Source Dosimetry 6.3.1 TG-43 Dosimetry Parameter Reference Datasets 6.3.2 The TG-43 Dose Calculation Formalism 6.3.3 The Primary and Scatter Separation Dose Calculation Formalism, PSS 6.3.4 MC Dose Calculation Method for TG-43 and PSS 6.3.4.1 Brachytherapy Source Design 6.3.4.2 Brachytherapy Source Radiation Emissions 6.3.4.3 Simulating Phantoms for Reference-Quality or Experimental Dosimetry Measurements 6.3.4.4 Dose-Rate Constant 6.4 MC in Support of Experimental BT 6.4.1 Corrections of Detector Energy Response 6.4.2 Effects of Experimental Phantom 6.4.3 Detector/Dosimeter Response and Perturbation 6.5 Future Use of Monte Carlo Methods for Brachytherapy Source Modelling References 7 Monte Carlo Modelling of Scanned Ion Beams in Radiotherapy 7.1 Introduction 7.1.1 Scanned Ion Beam Delivery 7.2 MC Modelling of SIBD Systems 7.2.1 Description of a Pencil Beam 7.2.2 Description of Scanning Properties 7.2.3 Description of Nozzle Accessories 7.2.4 Beam Modelling Concept 7.2.5 Generic Modelling Recipe 7.2.6 Modelling Method from Nozzle Exit 7.2.7 Full-Nozzle Modelling Method 7.2.8 Calibration of the Beam Model 7.3 MC Modelling Specificities of Nuclear Models 7.3.1 Nuclear Halo for Proton Beams 7.3.2 Fragment Spectra for Carbon Ions 7.4 MC Applications 7.4.1 Treatment Planning Workflow 7.4.2 Treatment Planning Algorithms 7.4.3 Other Applications 7.5 Summary References 8 Monte Carlo Simulations for Treatment Device Design 8.1 Introduction 8.2 Target for Therapy 8.3 Target for Imaging 8.4 Electron Beam Magnetic Shield 8.5 X-Ray Standard and Extended Flattening Filters 8.6 X-Ray Leakage 8.7 Electron Secondary Scattering Foils 8.8 X-Ray Wedges 8.9 Electron Applicator Leakage 8.10 Electron Multi-Leaf Collimator 8.11 X-Ray Image Detection and Processing 8.12 Conclusion References 9 Dynamic Beam Delivery and 4D Monte Carlo 9.1 Introduction 9.2 Simulations of Dynamic Beam Delivery 9.2.1 Strategies for Simulating Time-Dependent Beam Geometries 9.2.2 Applications of MC to Model Dynamic Radiotherapy Techniques 9.2.2.1 Dynamic Wedge 9.2.2.2 MLC-Based IMRT 9.2.2.3 Tomotherapy 9.2.2.4 VMAT 9.2.2.5 Protons 9.3 Dynamic Patient Simulations 9.3.1 Patient Motion in Radiotherapy 9.3.2 Strategies for 4D Patient Simulations 9.3.2.1 Convolution-Based Methods 9.3.2.2 Dose-Mapping Methods: Center-of-Mass and Dose Interpolation 9.3.2.3 Voxel Warping Method 9.3.2.4 Energy-Mapping Methods 9.4 Combining Dynamic Beam and Patient Simulations 9.5 Novel and Future Applications of MC in Dynamic Beam Delivery 9.6 Summary and Outlook References PART III: Patient Dose Calculation 10 Photons: Clinical Considerations and Applications 10.1 Introduction 10.2 Requirements for Clinical MC Treatment Planning 10.2.1 Beam Setup Capability 10.2.2 Beam Model 10.2.3 Patient Model 10.2.4 Dose Calculation 10.2.5 Dose Evaluation Capability 10.3 Commissioning and Validation 10.3.1 Tolerances and Acceptance Criteria 10.3.2 CT Conversion 10.3.3 Beam Model and Dose Calculation 10.4 Research and Commercial MCTP Systems 10.4.1 Research MCTP Systems 10.4.2 Commercial MCTP Systems 10.5 Clinical Examples and Applications 10.5.1 MC as Treatment Planning Tool 10.5.1.1 Noise in Dose Distributions 10.5.1.2 Calculation Time 10.5.2 Comparisons of Dose Calculation Engines 10.5.2.1 Lungs 10.5.2.2 Head and Neck 10.5.3 Inclusion of Time Dependencies 10.5.4 Reevaluation of Studies 10.5.5 MC as QA Tool 10.5.6 MC in Optimization 10.6 Conclusions References 11 Patient Dose Calculation 11.1 General Introduction 11.2 Statistical Uncertainties in Patient Dose Calculation 11.2.1 Introduction 11.2.2 Dose-Scoring Geometries and Calculation of Uncertainties 11.2.2.1 Dosels 11.2.2.2 Kugels 11.2.2.3 Segmented Organs 11.2.2.4 Voxel Size Effects 11.2.2.5 Concept of Latent Variance 11.2.2.6 Batch Method 11.2.2.7 History-by-History Method 11.3 Denoising and Smoothing Methods 11.3.1 Introduction 11.3.2 Denoising Integrated Dose Tallies 11.3.2.1 DVH Denoising Methods 11.3.3 Dose Distributions Denoising Methods 11.3.3.1 Deasy Approach 11.3.3.2 Wavelet Approach 11.3.3.3 Savitzky-Golay Method 11.3.3.4 Diffusion Equation Method 11.3.3.5 IRON Method 11.3.3.6 Content Adaptive Median Hybrid Filter Method 11.4 CT to Medium Conversion Methods 11.4.1 CT Stoichiometric Conversion Methods 11.4.1.1 Calculation of CT HU Number for Stoichiometric Conversion Schemes 11.4.1.2 CT Hounsfield Units Interpolation Method 11.4.1.3 Stoichiometric Conversion Method Based on Dose-Equivalent Tissue Subsets 11.4.2 Dual-Energy X-Ray CT Imaging: Improved HU CT to Medium Conversions 11.5 Deformable Image Registration 11.5.1 Developing Dose Warping Approaches for 4D MC 11.5.2 Comparing Patient Dose Calculation between 3D and 4D 11.5.3 Intercomparison of Dose Warping Techniques for 4D Dose Distributions 11.6 Inverse Planning with MC for Improved Patient Dose Calculations in Both 3D and 4D References 12 Electrons: Clinical Considerations and Applications 12.1 Introduction: Rationale for Monte Carlo-Based Treatment Planning Systems for Electron Beams 12.1.1 Advantages of MC versus Pencil Beam Algorithm 12.2 Research and Commercial MC TPSs 12.2.1 Meeting the Challenges: The OMEGA Project 12.2.2 Methods to Speed Up the MC Calculations 12.2.3 EGSnrc-Based MC Research Package 12.2.4 Commercial MC TPSs 12.2.4.1 Elekta Monaco VMC++ 12.2.4.2 Varian Electron MC 12.2.4.3 Elekta XiO eMC 12.2.4.4 RaySearch Electron MC Algorithm 12.3 Commissioning of an MC-Based TPS 12.3.1 Measurements Required for Beam Characterization 12.3.2 Additional Beam Data Measurements for Commissioning MC-Based TPSs 12.3.3 Beam Measurements Required for In-Phantom/Patient Dose Calculation Verification 12.3.3.1 Homogeneous Phantom: Dose Profiles, MU Calculations at Various SSDs 12.3.3.2 Heterogeneous Phantoms 12.4 Issues Arising from the Clinical Implementation of MC Dose Calculations 12.4.1 Calculation Normalization, Dose Prescription, and Isodose Lines 12.4.2 Statistical Uncertainty, Smoothing, and Calculation Voxel Size 12.4.3 Dose-to-Medium versus Dose-to-Water Calculations 12.4.4 Examples of CT-Based Dose Calculations 12.4.5 Typical Calculation Times 12.5 Future Clinical Applications 12.5.1 Electron Beams without Applicator 12.5.2 MERT Using Photon MLC 12.5.3 Mixed Electron and Photon Beam Radiotherapy 12.6 Summary References 13 Protons: Clinical Considerations and Applications 13.1 Introduction 13.1.1 Short Introduction to Proton Therapy Beam Delivery 13.1.2 Proton Physics 13.1.3 Codes for Proton Monte Carlo 13.2 Simulating the Radiation Field Incident on a Patient or an Experimental Setup 13.2.1 Characterizing Proton Beams at the Treatment Head Entrance 13.2.2 Monte Carlo Modelling of the Therapy Treatment Head 13.2.3 Phase-Space Distributions and Beam Models 13.2.4 Uncertainties and Benchmarking 13.3 Simulating Dose to the Patient 13.3.1 Statistical Accuracy of Dose Modelling 13.3.2 CT Conversion 13.3.3 Absolute Dose 13.3.4 Dose to Water and Dose to Tissue 13.3.5 Impact of Nuclear Interaction Products on Patient Dose Distributions 13.3.6 Differences between Proton Monte Carlo and Analytical Dose Calculation 13.3.6.1 Differences in the Predicted Range 13.3.6.2 Differences in the Predicted Dose 13.3.7 Clinical Implementation 13.3.8 Monte Carlo-Based Treatment Planning 13.3.9 Improving Monte Carlo Efficiency 13.4 Other Proton Monte Carlo Applications 13.4.1 4D Dose Calculations 13.4.2 Simulating Proton-Induced Gamma Emission for Range Verification 13.4.3 Simulating LET Distributions for Radiobiological Considerations 13.4.4 Track Structure Simulations References 14 Monte Carlo as a QA Tool for Advanced Radiation Therapy 14.1 Introduction 14.2 Techniques and Implementation Methods 14.2.1 Overview of Monte Carlo-Based QA 14.2.2 Treatment Delivery Information 14.2.3 Direct Monte Carlo Simulation 14.2.4 Beam Intensity Map for Monte Carlo-Based QA 14.2.4.1 Beam Intensity Map Measurement 14.2.4.2 Intensity Map Reconstruction 14.2.5 Monte Carlo Dose Calculation 14.3 Clinical Applications 14.3.1 MC for TPS QA 14.3.2 MC for Patient-Specific Plan QA 14.3.3 MC for Online and Offline Treatment QA 14.4 Conclusions References 15 Monte Carlo Applications in Total Skin Electron Therapy 15.1 Introduction 15.1.1 Total Skin Electron Therapy 15.1.2 Requirement of Beam Characteristics for Delivering TSET 15.1.3 Irradiation Techniques in TSET 15.1.4 Advantages of Monte Carlo Application in TSET 15.2 Monte Carlo Simulation of Incident Beams Used in TSET 15.2.1 Generation of Incident Beams for Dose Calculation 15.2.2 Validation of Simulated Beams in TSET Delivery Geometry 15.2.3 Use of Simulated Beams for Dose Calculation in TSET 15.3 Uniformity of Dose Distributions from Dual Fields 15.3.1 Dose Distributions from Rotational Fields on an Oval Cylindrical Phantom 15.3.2 Patient Skin Dose Distributions Resulting from Rotational Dual Fields or 6 Static Dual Fields 15.4 Summary References 16 Monte Carlo Simulation in Brachytherapy Patients and Applicator Modelling 16.1 Introduction 16.2 Departure from TG-43 to MC Dose Calculations for Treatment Planning 16.2.1 Introduction 16.2.2 Absorbed Dose Differences between Water and Human Tissues 16.2.3 Shielding 16.2.4 Scattering Conditions 16.2.5 Breakdown of the Kerma Approximation for Absorbed Doses 16.2.6 Magnitude of Clinical Impact of Moving from TG-43 to MC Dose Evaluations 16.3 MC Dose Calculation Tools 16.3.1 Monte Carlo Dose Calculation Tool for Prostate Implants 16.3.2 PTRAN_CT 16.3.3 BrachyDose 16.3.4 ALGEBRA 16.3.5 egs_brachy 16.4 Role of Imaging in in Vivo Cross-Section Assignment for MC Tissue Inhomogeneity Corrections 16.4.1 Computed Tomography 16.4.2 Dual-Energy CT 16.4.3 Magnetic Resonance Imaging 16.4.4 Ultrasound 16.5 Dose Specification in Terms of D[sub(w,m)] or D[sub(m,m)] 16.6 Future Use of MC Methods for Brachytherapy Patient and Applicator Modelling 16.6.1 Treatment Planning 16.6.2 Radiobiological Evaluations References 17 Artificial Intelligence and Monte Carlo Simulation 17.1 AI and Dose Estimation from MC Simulations 17.2 AI for Dose Computation Denoising 17.3 AI for Imaging Detector and Source Modelling 17.4 CBCT Imaging 17.5 Discussion 17.6 Conclusion and Outlook References Index About ten years after the first edition comes this second edition of Monte Carlo Techniques in Radiation Therapy: Introduction, Source Modelling, and Patient Dose Calculations, thoroughly updated and extended with the latest topics, edited by Frank Verhaegen and Joao Seco. This book aims to provide a brief introduction to the history and basics of Monte Carlo simulation, but again has a strong focus on applications in radiotherapy. Since the first edition, Monte Carlo simulation has found many new applications, which are included in detail.The applications sections in this book cover the following: Modelling transport of photons, electrons, protons, and ions Modelling radiation sources for external beam radiotherapy Modelling radiation sources for brachytherapy Design of radiation sources Modelling dynamic beam delivery Patient dose calculations in external beam radiotherapy Patient dose calculations in brachytherapy Use of artificial intelligence in Monte Carlo simulations This book is intended for both students and professionals, both novice and experienced, in medical radiotherapy physics. It combines overviews of development, methods, and references to facilitate Monte Carlo studies. About ten years after the first edition comes this second edition of **__Monte Carlo Techniques in Radiation Therapy: Introduction, Source Modelling and Patient Dose Calculations__**, thoroughly updated and extended with the latest topics__,__ edited by Frank Verhaegen and Joao Seco. The book aims to provide a brief introduction to the history and basics of Monte Carlo simulation, but again has a strong focus on applications in radiotherapy. Since the first edition, Monte Carlo simulation has found many new applications, which were included in detail. The applications sections in this book cover: * Modelling transport of photons, electrons, protons and ions * Modelling radiation sources for external beam radiotherapy * Modelling radiation sources for brachytherapy * Design of radiation sources * Modelling dynamic beam delivery * Patient dose calculations in external beam radiotherapy * Patient dose calculations in brachytherapy * Use of Artificial Intelligence in Monte Carlo simulations Thoroughly updated throughout, this second edition of Monte Carlo Techniques in Radiation Therapy: Applications to Dosimetry, Imaging, Preclinical radiotherapy, edited by Joao Seco and Frank Verhaegen, explores the use of Monte Carlo methods for modelling various features of international and external radiation sources.