This edited book focuses on the application of AI and data analytics within the three specialisms of medical imaging: diagnostic radiography (including fluoroscopy, computed tomography, breast imaging, ultrasound, and magnetic resonance imaging), radiotherapy and oncology, and nuclear medicine and molecular imaging.
This edited book focuses on the application of AI and data analytics within medical imaging with contributions focussing on general radiography, cross-sectional imaging, and breast imaging.
Artificial Intelligence and Data Analytics in Medical Imaging leverages the expertise of key practitioners, academics, and researchers who are recognised leaders in their respective fields. The chapters cover essential topics, including imaging modalities, radiotherapy and future recommendations. The editors incorporate insights from recent publications and clinical practice, addressing how emerging technologies should be managed, implemented, and adapted in healthcare settings. Each chapter maintains a patient-centred focus while connecting to key literature in the field.
This book acts as a cornerstone for undergraduate students, but importantly signposts to other key texts within the field of medical imaging. Further, academics will also find this text useful as it aims to enrich scholarly learning, teaching, and assessment to healthcare programmes nationally and internationally.
Chapter 1 Artificial Intelligence in Medical Imaging
Chapter 2
Preprocessing of Medical Imaging Data
Chapter 3 Artificial Intelligence and
Data Analytics in Medical Imaging: Tools and Packages in Clinical Practice
Chapter 4 Balancing Trust and Reliance: Understanding the Human-AI
Interaction to Ensure Responsible Use of Innovation and Advanced Technologies
in Radiography
Chapter 5 Brain MRI Segmentation
Chapter 6 Centring the
Patient in Implementing AI for Medical Imaging
Chapter 7 Artificial
Intelligence and Data Analytics in Medical Imaging for the Diagnosis of
Endometriosis
Chapter 8 The Role of Artificial Intelligence in Forensic
Radiology
Chapter 9 Artificial Intelligence for Medical Imaging in Radiation
Therapy
Chapter 10 Artificial Intelligence for MRI-Based Diagnosis of
Prostate Cancer in Clinical Practice
Chapter 11 Artificial Intelligence in
Mammography
Chapter 12 Integrating Artificial Intelligence into Medical
Imaging Curriculum: Challenges, Applications, and Future Directions
Chapter
13 Business Analytics for Radiology: A Narrative Review
Chapter 14
Radiographers and Computer Programmers: Finding Collaborative Ways to Enhance
Clinical Outcomes
Chapter 15 Brain Age Prediction: Methods, Models, and
Applications
Christopher Hayre is an Associate Professor at Monash University, Australia. He holds an Adjunct Professor position with RMIT University, Australia and Fiji National University. He has published on a range of topics involving qualitative and quantitative papers and brought together several books in the field of medical imaging, health research, technology, and ethnography. His work has influence policy in the United Kingdom and was recently citied in the top 20 (11/20) of most prolific researchers in professional radiography journals in a ten-year period.
Rob Davidson retired from the University of Canberra (UC) in February 2020. He was honoured by UC on his retirement as an Emeritus Professor and is also an Adjunct Research Professor at Fiji National University. Prof. Davidsons research focus is on dose/image quality in planar radiography and CT and digital image processing in medical imaging. He is part of an international team looking at new imaging methods for improved detection of prostate cancer. He has been a Chief Investigator in multiple research grants; has over 70 peer reviewed publications; authored a book on mammography physic including a chapter on artificial intelligence in mammography; has authored/co-authored six book chapters; been the keynote speaker at multiple international conferences; supervised/co-supervised approximately 20 PhD and Masters by Research students; and examined multiple higher degree by research theses.
Shayne Chau is a Senior Lecturer in Diagnostic Radiography at Charles Sturt University, Adjunct Senior Lecturer at the University of Exeter, and Adjunct Staff at the University of Canberra and Vin University. Shayne is a fellow of both the Higher Education Academy (UK) and the Australian Society of Medical Imaging and Radiation Therapy. He is an internationally published researcher with over 65 peer-reviewed journal articles, multiple textbooks, and book chapters in radiography, computed tomography, neuroimaging, and person-centred care.
Xiaoming Zheng is a Senior Lecturer in Medical Physics at Charles Sturt University. He was a PACS administrator while he was working at the Department of Nuclear Medicine at the Prince of Wales Hospital Sydney from 1995 to 1998. He has been teaching Digital Image Processing and Imaging Informatics in Medical Radiation Science Course at Charles Sturt University since 1998.
Abel Zhou is an Assistant Professor at the Singapore Institute of Technology. His research focuses on X-ray scatter reduction techniques and the application of artificial intelligence in medical imaging. He also employs Monte Carlo simulations to evaluate and optimize X-ray imaging performance and patient radiation dose. In addition, he teaches courses on Artificial Intelligence in Medical Imaging, Healthcare and Radiological Informatics, and Radiation Risk Management.
Nigel Frame is a Lecturer in Radiation Therapy and Radiation Protection at Charles Sturt University. He is a Doctoral student examining the role religion has played in onco-suppression.
