Diagnostic Radiology Physics with MATLAB®: A Problem-Solving Approach
Book information
Description
Imaging modalities in radiology produce ever-increasing amounts of data which need to be displayed, optimized, analyzed and archived: a "big data" as well as an "image processing" problem. Computer programming skills are rarely emphasized during the education and training of medical physicists, meaning that many individuals enter the workplace without the ability to efficiently solve many real-world clinical problems. This book provides a foundation for the teaching and learning of programming for medical physicists and other professions in the field of Radiology and offers valuable content for novices and more experienced readers alike. It focuses on providing readers with practical skills on how to implement MATLAB® as an everyday tool, rather than on solving academic and abstract physics problems. Further, it recognizes that MATLAB is only one tool in a medical physicist’s toolkit and shows how it can be used as the "glue" to integrate other software and processes together. Yet, with great power comes great responsibility. The pitfalls to deploying your own software in a clinical environment are also clearly explained. This book is an ideal companion for all medical physicists and medical professionals looking to learn how to utilize MATLAB in their work. Features Encompasses a wide range of medical physics applications in diagnostic and interventional radiology Advances the skill of the reader by taking them through real-world practical examples and solutions with access to an online resource of example code The diverse examples of varying difficulty make the book suitable for readers from a variety of backgrounds and with different levels of programming experience. Cover Half Title Series Page Title Page Copyright Page Dedication Contents SECTION I: General topics CHAPTER 1: The role of programming in healthcare 1.1. WHAT PROGRAMMING CAN DO FOR YOU 1.2. WHAT PROGRAMMING CAN DO FOR YOUR CLINIC: CHANGE THE NATURE OF ROUTINE WORK 1.3. WHAT PROGRAMMING CAN DO FOR YOUR CLINIC: ENABLE RESEARCH AND INNOVATION 1.4. WITH GREAT POWER COMES GREAT RESPONSIBILITY 1.5. CONCLUSION CHAPTER 2: MATLAB fundamentals 2.1. INTRODUCTION 2.2. VARIABLES AND DATA TYPES 2.3. ARRAYS AND MATRIX MANIPULATION 2.4. MORE DATA TYPES 2.5. CONDITIONAL OPERATORS AND LOGICAL INDEXING 2.6. CONTROL FLOW 2.7. USER-DEFINED FUNCTIONS 2.8. DATA ANALYSIS 2.9. VISUALIZATION 2.10. HANDLING BIG DATA SETS 2.11. CLASSES 2.12. IMPROVING CODE PERFORMANCE 2.13. EXERCISE—BASIC IMAGE PROCESSING 2.14. CONCLUSION CHAPTER 3: Data sources in medical imaging 3.1. INTRODUCTION 3.2. THE DICOM STANDARD AND FILE FORMAT 3.3. OTHER DATA SOURCES 3.4. CONCLUSION CHAPTER 4: Importing, manipulating and displaying DICOM data in MATLAB 4.1. INTRODUCTION 4.2. IMPORTING IMAGE DATA 4.3. WRITING AND ANONYMIZING DICOM DATA 4.4. VISUALIZATION 4.5. CONCLUSION CHAPTER 5: Creating automated workfows using MATLAB 5.1. INTRODUCTION 5.2. MANUAL CALCULATION OF SNR 5.3. AUTOMATING THE SNR CALCULATION USING MATLAB 5.4. CONCLUSION CHAPTER 6: Integration with other programming languages and environments 6.1. INTRODUCTION 6.2. WHEN TO USE OTHER PROGRAMMING LANGUAGES AND ENVIRONMENTS 6.3. SYSTEM COMMANDS 6.4. INTEGRATING WITH JAVA 6.5. INTEGRATING WITH PYTHON 6.6. INTEGRATING WITH THE .NET FRAMEWORK 6.7. CONCLUSION CHAPTER 7: Good programming practices 7.1. WHAT MAKES A GOOD PROGRAM 7.2. GOOD PRACTICES 7.3. CONCLUSION CHAPTER 8: Sharing software 8.1. POTENTIAL OF CROWD-SOURCING 8.2. SHARE CODE USING MATLAB FILE EXCHANGE 8.3. SHARE CODE USING OTHER SOURCE-CODE HOSTING SITES 8.4. CHOOSING THE OPTIMAL APPROACH: GUI OR NOT? 8.5. BUILDING AN APP IN MATLAB 8.6. CREATING EXECUTABLES WITH THE MATLAB COMPILER 8.7. LICENSES 8.8. CONCLUSION CHAPTER 9: Regulatory considerations when deploying your software in a clinical environment 9.1. MEDICAL DEVICE REGULATIONS 9.2. HEALTH INFORMATION PRIVACY SECTION II: Problem-solving: examples from the trenches CHAPTER 10: Applying good software development processes in practice 10.1. INTRODUCTION 10.2. THE TRENCH IN QUESTION: RADIOCHROMIC FILM DOSIMETRY 10.3. AN IN-HOUSE SOFTWARE VALIDATION CHECKLIST 10.4. BEFORE WRITING THE CODE 10.5. WHILE WRITING THE CODE 10.6. AFTER WRITING THE CODE 10.7. SUMMARY OF VALIDATION PROCESS AND OUTCOMES 10.8. REGARDING CERTIFICATION 10.9. CONCLUSION CHAPTER 11: Automating quality control tests and evaluating ATCM in computed tomography 11.1. INTRODUCTION 11.2. ANALYZING CT PHANTOM IMAGES 11.3. APPLICATIONS IN CONSTANCY TESTS 11.4. APPLICATIONS IN AUTOMATIC TUBE CURRENT MODULATION 11.5. CONCLUSIONS CHAPTER 12: Parsing and analyzing Radiation Dose Structured Reports 12.1. INTRODUCTION 12.2. STRUCTURE OF RDSR OBJECTS 12.3. PARSING RDSR OBJECTS 12.4. ANALYZING PARSED RDSR DATA 12.5. CONCLUSIONS CHAPTER 13: Methods of determining patient size surrogates using CT images 13.1. INTRODUCTION 13.2. STRUCTURE OF THE CODE 13.3. CALCULATING SIZE METRICS FROM CT AXIAL IMAGES 13.4. CALCULATING THE SIZE-SPECIFIC DOSE ESIMATE 13.5. CONCLUSION CHAPTER 14: Reconstructing the geometry of x-ray interventions 14.1. INTRODUCTION 14.2. ELEMENTARY VECTOR ALGEBRA 14.3. RECONSTRUCTING THE PATIENT-BEAM ALIGNMENT 14.4. RECONSTRUCTING THE SOURCE-TO-SURFACE DISTANCE 14.5. CALCULATING THE INCIDENT AIR KERMA 14.6. CONCLUSION CHAPTER 15: Mammography and breast tomosynthesis simulation using Perlin noise 15.1. INTRODUCTION 15.2. GENERATING THE NOISE 15.3. FRACTAL NOISE 15.4. PRE-GENERATION 15.5. THE FINAL TISSUE MODEL 15.6. CONCLUSION: GENERATING BREAST TISSUE CHAPTER 16: xrTk: a MATLAB toolkit for x-ray physics calculations 16.1. INTRODUCTION 16.2. OPTIMIZING IMAGE QUALITY 16.3. DISCUSSION 16.4. CONCLUSIONS CHAPTER 17: Automating daily QC for an MRI scanner 17.1. INTRODUCTION 17.2. AUTOMATIC ANALYSIS OF QUALITY CONTROL IMAGES 17.3. THE MAIN FUNCTION 17.4. CONCLUSION CHAPTER 18: Image processing at scale by containerizing’ MATLAB 18.1. INTRODUCTION 18.2. IMPROVED DICOM SUPPORT BY MATLAB-JAVA INTEGRATION 18.3. RUNNING MATLAB IN A CONTAINER 18.4. EXAMPLE PROBLEM FOR CONTAINERIZATION 18.5. XNAT: ORCHESTRATING THE IMAGE ANALYSIS OF LARGE PATIENT COHORTS 18.6. CONCLUSION CHAPTER 19: Estimation of arterial wall movements 19.1. THE LONGITUDINAL MOVEMENT OF THE ARTERIAL WALL 19.2. BLOCK MATCHING 19.3. ARTERIAL WALL MOVEMENT MEASUREMENTS 19.4. CONCLUDING REMARKS CHAPTER 20: Importation and visualization of ultrasound data 20.1. INTRODUCTION TO ULTRASOUND DATA 20.2. STRUCTURE OF A DATA FILE 20.3. READ DATA INTO MATLAB 20.4. GENERATING AND VISUALIZING B-MODE IMAGES 20.5. CONCLUSION Index
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