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.
Artificial Intelligence and Data Analytics in Medical Imaging leverages the expertise of key practitioners, academics, and researchers who are recognized leaders in their respective fields. The chapters cover essential topics including imaging modalities, treatment planning, ethics, 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-centered focus while connecting to key literature in the field.
This book acts as a cornerstone for undergraduate students, but importantly ‘signpost’ 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 programs nationally and internationally.
Table of Contents:
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
About the Author :
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. Davidson’s 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.
