Generative Artificial Intelligence for Neuroimaging
Methods and Applications
Will be published approx. on 1. October 2026
Book
Paperback/Softback
250 pages
978-0-443-45010-5 (ISBN)
Description
Generative Artificial Intelligence in Neuroimaging: Methods and Applications offers a clear and practical guide for biomedical engineers and data scientists interested in using generative AI to improve neuroimaging techniques. The book explains key generative models, such as GANs, VAEs, and diffusion models, and shows how these methods can enhance data analysis, improve image quality, and support personalized medicine. It includes real-world examples that demonstrate the successful use of AI in diagnosing diseases and developing brain-computer interfaces. The book also discusses important ethical considerations and best practices for using AI responsibly in healthcare.
Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.
Finally, the book addresses technical challenges and highlights future research opportunities in the field of AI and biomedical engineering. Whether you are an experienced professional or a new researcher, this book provides the knowledge and tools needed to advance neuroimaging and contribute to better patient care.
More details
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 235 mm
Width: 191 mm
Weight
449 gr
ISBN-13
978-0-443-45010-5 (9780443450105)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Deepika Koundal currently serves as a Senior Researcher at the University of Eastern Finland, specializing in Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision & Image Processing, and Cyber-Physical Systems. She holds a Ph.D. in Computer Science and Engineering and has received several prestigious accolades, including the MSCA Seal of Excellence from the European Commission. In 2023 and 2024, she was recognized as a top 2% researcher by Stanford University. She has over 13 years of teaching and research experience, having served in various academic roles including at NIT Hamirpur, Chitkara University, and UIET Panjab University. She has earned multiple research excellence awards from UPES and has published numerous research articles, edited notable books, and holds several patents. Furthermore, she contributes as a guest and associate editor for leading journals, including those published by IEEE and Elsevier. Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of "Visiting Professor? in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the "Research Scientist? of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.
Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.
Content
Part I: Foundations
1. Introduction to Neuroscience Imaging Modalities (fMRI, EEG, MEG, etc.) and their challenges.
2. A Primer on Generative AI: Key Concepts and Architectures
3. Data Handling and Preprocessing in Neuroimaging for Generative AI. Addressing noise, artifacts, and variability.
Part II: Methodological Advancements
4. Generative Adversarial Networks (GANs) For Neuroimaging Networks
5. Variational Autoencoders (VAEs) for Neuroimaging: Dimensionality Reduction, Data Augmentation, and Latent Space Analysis.
6. Diffusion Models for High-Fidelity Neuroimage Generation and Enhancement.
7. Flow-based Generative Models for Neuroimaging: Density Estimation and Data Augmentation.
8. Hybrid and Novel Generative Models for Neuroimaging: Exploring emerging architectures and combinations
Part III: Applications in Neuroscience
9. Generative AI for Disease Diagnosis and Prognosis (Alzheimer's, Parkinson's, Stroke, etc.)
10. Generative AI for Brain Connectivity and Network Analysis: Understanding brain organization and its alterations in disease
11. Generative AI for Simulating Brain Development and Aging: Modeling normal and pathological processes.
12. Generative AI for Cognitive Neuroscience: Investigating the neural basis of cognition.
Part IV: Challenges and Future Directions
13. Challenges and Limitations: Addressing data scarcity, interpretability, computational costs, and ethical concerns.
14. Generative Al for Neurological Diseases: A Review Toward Future Directions
1. Introduction to Neuroscience Imaging Modalities (fMRI, EEG, MEG, etc.) and their challenges.
2. A Primer on Generative AI: Key Concepts and Architectures
3. Data Handling and Preprocessing in Neuroimaging for Generative AI. Addressing noise, artifacts, and variability.
Part II: Methodological Advancements
4. Generative Adversarial Networks (GANs) For Neuroimaging Networks
5. Variational Autoencoders (VAEs) for Neuroimaging: Dimensionality Reduction, Data Augmentation, and Latent Space Analysis.
6. Diffusion Models for High-Fidelity Neuroimage Generation and Enhancement.
7. Flow-based Generative Models for Neuroimaging: Density Estimation and Data Augmentation.
8. Hybrid and Novel Generative Models for Neuroimaging: Exploring emerging architectures and combinations
Part III: Applications in Neuroscience
9. Generative AI for Disease Diagnosis and Prognosis (Alzheimer's, Parkinson's, Stroke, etc.)
10. Generative AI for Brain Connectivity and Network Analysis: Understanding brain organization and its alterations in disease
11. Generative AI for Simulating Brain Development and Aging: Modeling normal and pathological processes.
12. Generative AI for Cognitive Neuroscience: Investigating the neural basis of cognition.
Part IV: Challenges and Future Directions
13. Challenges and Limitations: Addressing data scarcity, interpretability, computational costs, and ethical concerns.
14. Generative Al for Neurological Diseases: A Review Toward Future Directions