Muutke küpsiste eelistusi

Diagnostic Radiology Physics with MATLAB®: A Problem-Solving Approach [Kõva köide]

Edited by (Karolinska University Hospital, Sweden), Edited by (Karolinska University Hospital, Sweden), Edited by (Karolinska University Hospital, Sweden)
  • Formaat: Hardback, 292 pages, kõrgus x laius: 254x178 mm, kaal: 875 g, 6 Tables, black and white; 4 Line drawings, color; 32 Line drawings, black and white; 6 Halftones, color; 22 Halftones, black and white; 10 Illustrations, color; 64 Illustrations, black and white
  • Sari: Series in Medical Physics and Biomedical Engineering
  • Ilmumisaeg: 24-Nov-2020
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0815393652
  • ISBN-13: 9780815393658
Teised raamatud teemal:
Diagnostic Radiology Physics with MATLAB®: A Problem-Solving ApproachSuurem pilt
  • Formaat: Hardback, 292 pages, kõrgus x laius: 254x178 mm, kaal: 875 g, 6 Tables, black and white; 4 Line drawings, color; 32 Line drawings, black and white; 6 Halftones, color; 22 Halftones, black and white; 10 Illustrations, color; 64 Illustrations, black and white
  • Sari: Series in Medical Physics and Biomedical Engineering
  • Ilmumisaeg: 24-Nov-2020
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0815393652
  • ISBN-13: 9780815393658
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780815393658.html
Märksõnad:

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 utilise 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 makes the book suitable for readers from a variety of backgrounds and with different levels of programming experience
Section I General topics
Chapter 1 The role of programming in healthcare
3(4)
Johan Helmenkamp
Robert Bujila
Gavin Poludniowski
1.1 What Programming Can Do For You
4(1)
1.2 What Programming Can Do For Your Clinic: Change The Nature Of Routine Work
4(1)
1.3 What Programming Can Do For Your Clinic: Enable Research And Innovation
5(1)
1.4 With Great Power Comes Great Responsibility
6(1)
1.5 Conclusion
6(1)
Chapter 2 Matlab Fundamentals
7(20)
Javier Gazzarri
Cindy Solomon
2.1 Introduction
8(1)
2.2 Variables And Data Types
9(1)
2.3 Arrays And Matrix Manipulation
10(2)
2.4 More Data Types
12(1)
2.5 Conditional Operators And Logical Indexing
12(1)
2.6 Control Flow
13(3)
2.7 User-Defined Functions
16(1)
2.8 Data Analysis
17(1)
2.9 Visualization
18(2)
2.10 Handling Big Data Sets
20(1)
2.11 Classes
21(1)
2.12 Improving Code Performance
22(1)
2.13 Exercise--basic Image Processing
23(2)
2.14 Conclusion
25(2)
Chapter 3 Data Sources In Medical Imaging
27(10)
Jonas Andersson
Josef Lundman
Gavin Poludniowski
Robert Bujila
3.1 Introduction
28(2)
3.2 The Dicom Standard And File Format
30(4)
3.3 Other Data Sources
34(2)
3.4 Conclusion
36(1)
Chapter 4 Importing, Manipulating And Displaying Dicom Data In Matlab
37(16)
Piyush Khopkar
Josef Lundman
Vuu Ravichandran
4.1 Introduction
38(2)
4.2 Importing Image Data
40(2)
4.3 Writing And Anonymizing Dicom Data
42(3)
4.4 Visualization
45(7)
4.5 Conclusion
52(1)
Chapter 5 Creating Automated Workflows Using Matlab
53(12)
Johan Helmenkamp
Sven Mansson
5.1 Introduction
53(2)
5.2 Manual Calculation Of Snr
55(1)
5.3 Automating The Snr Calculation Using Matlab
56(7)
5.4 Conclusion
63(2)
Chapter 6 Integration With Other Programming Languages And Environments
65(14)
Gavin Poludniowski
Matt Whitaker
6.1 Introduction
65(1)
6.2 When To Use Other Programming Languages And Environments
66(1)
6.3 System Commands
67(2)
6.4 Integrating With Java
69(2)
6.5 Integrating With Python
71(4)
6.6 Integrating With The Net Framework
75(2)
6.7 Conclusion
77(2)
Chapter 7 Good Programming Practices
79(10)
Yanlu Wang
Piyush Khopkar
7.1 What Makes A Good Program
79(1)
7.2 Good Practices
80(7)
7.3 Conclusion
87(2)
Chapter 8 Sharing Software
89(16)
Yanlu Wang
Piyush Khopkar
8.1 Potential Of Crowd-Sourcing
90(1)
8.2 Share Code Using Matlab File Exchange
91(1)
8.3 Share Code Using Other Source-Code Hosting Sites
91(1)
8.4 Choosing The Optimal Approach: Gui Or Not?
92(1)
8.5 Building An App In Matlab
93(5)
8.6 Creating Executables With The Matlab Compiler
98(3)
8.7 Licenses
101(2)
8.8 Conclusion
103(2)
Chapter 9 Regulatory Considerations When Deploying Your Software In A Clinical Environment
105(24)
Philip S. Cosgriff
Johan Atting
9.1 Medical Device Regulations
106(15)
9.2 Health Information Privacy
121(8)
Section II Problem-Solving: Examples From The Trenches
Chapter 10 Applying Good Software Development Processes In Practice
129(12)
Tanya Kairn
10.1 Introduction
130(1)
10.2 The Trench In Question: Radiochromic Film Dosimetry
131(1)
10.3 An In-House Software Validation Checklist
132(1)
10.4 Before Writing The Code
133(4)
10.5 While Writing The Code
137(1)
10.6 After Writing The Code
138(1)
10.7 Summary Of Validation Process And Outcomes
139(1)
10.8 Regarding Certification
140(1)
10.9 Conclusion
140(1)
Chapter 11 Automating Quality Control Tests And Evaluating Atcm In Computed Tomography
141(12)
Patrik Nowik
11.1 Introduction
141(1)
11.2 Analyzing Ct Phantom Images
142(2)
11.3 Applications In Constancy Tests
144(5)
11.4 Applications In Automatic Tube Current Modulation
149(3)
11.5 Conclusions
152(1)
Chapter 12 Parsing And Analyzing Radiation Dose Structured Reports
153(10)
Robert Bujila
12.1 Introduction
153(1)
12.2 Structure Of Rdsr Objects
154(3)
12.3 Parsing Rdsr Objects
157(2)
12.4 Analyzing Parsed Rdsr Data
159(3)
12.5 Conclusions
162(1)
Chapter 13 Methods Of Determining Patient Size Surrogates Using CT Images
163(8)
Christiane Sarah Burton
13.1 Introduction
163(1)
13.2 Structure Of The Code
164(2)
13.3 Calculating Size Metrics From Ct Axial Images
166(3)
13.4 Calculating The Size-Specific Dose Esimate
169(1)
13.5 Conclusion
170(1)
Chapter 14 Reconstructing The Geometry Of X-Ray Interventions
171(12)
Artur Omar
14.1 Introduction
171(2)
14.2 Elementary Vector Algebra
173(1)
14.3 Reconstructing The Patient-Beam Alignment
174(2)
14.4 Reconstructing The Source-To-Surface Distance
176(4)
14.5 Calculating The Incident Air Kerma
180(1)
14.6 Conclusion
181(2)
Chapter 15 Simulation Of Anatomical Structure In Dm And Bt Using Perlin Noise
183(14)
Magnus Dustler
15.1 Introduction
184(2)
15.2 Generating The Noise
186(6)
15.3 Fractal Noise
192(2)
15.4 Pre-Generation
194(1)
15.5 The Final Tissue Model
195(1)
15.6 Conclusion: Generating Breast Tissue
196(1)
Chapter 16 Xrtk: A Matlab Toolkit For X-Ray Physics Calculations
197(14)
Tomi F. Nano
Ian A. Cunningham
16.1 Introduction
198(2)
16.2 Optimizing Image Quality
200(9)
16.3 Discussion
209(1)
16.4 Conclusions
209(2)
Chapter 17 "Automating Daily Qc For An Mri Scanner
211(12)
Sven Mansson
17.1 Introduction
211(1)
17.2 Automatic Analysis Of Quality Control Images
212(9)
17.3 The Main Function
221(1)
17.4 Conclusion
222(1)
Chapter 18 Image Processing At Scale By Containerizing' Matlab
223(16)
James D'Arcy
Simon J. Doran
Matthew Orton
18.1 Introduction
224(1)
18.2 Improved Dicom Support By Matlab-Java Integration
224(6)
18.3 Running Matlab In A Container
230(3)
18.4 Example Problem For Containerization
233(5)
18.5 Xnat: Orchestrating The Image Analysis Of Large Patient Cohorts
238(1)
18.6 Conclusion
238(1)
Chapter 19 Estimation Of Arterial Wall Movements
239(10)
Magnus Cinthio
John Albinsson
Tobias Erlov
Tomas Jansson
Asa Ryden Ahlgren
19.1 The Longitudinal Movement Of The Arterial Wall
240(1)
19.2 Block Matching
241(5)
19.3 Arterial Wall Movement Measurements
246(1)
19.4 Concluding Remarks
247(2)
Chapter 20 Importation And Visualization Of Ultrasound Data
249(20)
Tobias Erlov
Magnus Cinthio
Tomas Jansson
20.1 Introduction To Ultrasound Data
249(1)
20.2 Structure Of A Data File
250(2)
20.3 Read Data Into Matlab
252(1)
20.4 Generating And Visualizing B-Mode Images
253(3)
20.5 Conclusion
256(13)
Index 269
Johan Helmenkamp, M.Sc. Johan obtained his Masters degree in Medical Radiation Physics at Lund University (Sweden) in 2010. Johan is a practising medical physics expert in Radiology at the Karolinska University Hospital (Sweden) and teaches undergraduate medical physics students, radiology residents and radiographers in topics such as imaging physics and technology, image processing and radiation safety. Johan has chaired a course committee for a national programming course aimed at improving the programming skills of fellow medical physicists and finds MATLAB programming a necessity both as a teaching tool but also for his everyday physics practice in the clinic, where there is a high demand for accurate and well-presented data for decision making.

Robert Bujila, Ph.D. Robert earned his Masters degree in Medical Radiation Physics at Stockholm University (Sweden) in 2010. After receiving his Masters degree in Medical Physics, Robert worked at the Karolinska University Hospital between 2010 and 2019 with a focus on Computed Tomography. While working as a clinical Medical Physicist, Robert received his PhD in Physics from the Royal Institute of Technology in Stockholm, Sweden with a project related to the optimization of image quality and radiation dose in CT. From the beginning of Robert's career, he has been involved in many projects that require programming where MATLAB has been an essential tool. As such, Robert understands the potential that good programming skills can provide and has been an advocate for the increased utilization of programming to forward the Medical Physics profession. During the course of editing this book, Robert has moved on from the Karolinska University Hospital to take an active role in the development of Computed Tomography systems in the private sector.

Gavin Poludniowski, Ph.D. Gavin Poludniowski graduated with a PhD in Theoretical Physics from the University of Manchester (UK) in 2003. He gained his Master's degree in Medical Physics the next year from the University of Leeds, before completing the Basic Clinical Training scheme at the Regional Medical Physics Department in the North East of England. After research stints at the Royal Marsden Hospital and the University of Surrey, he moved to Sweden, where he is currently employed at the Karolinska University Hospital. Gavin has had a varied career, but for a long time a strong focus has been on research in medical imaging. His software such as "SpekCalc" and "SpekPy" have proved popular and have been used in many hospitals and research institutions around the world. He first used MATLAB before "Y2K" and still makes use of it in his work today. Gavin has also contributed previously to a book published by CRC Press (Handbook of X-ray Imaging: Physics and Technology edited by Paolo Russo).