Functional Magnetic Resonance Imaging Processing

Xingfeng Li

2013-ж. сен. · Springer Science & Business Media

Учкай маалымат

With strong numerical and computational focus, this book serves as an essential resource on the methods for functional neuroimaging analysis, diffusion weighted image analysis, and longitudinal VBM analysis. It includes four MRI image modalities analysis methods. The first covers the PWI methods, which is the basis for understanding cerebral flow in human brain. The second part, the book’s core, covers fMRI methods in three specific domains: first level analysis, second level analysis, and effective connectivity study. The third part covers the analysis of Diffusion weighted image, i.e. DTI, QBI and DSI image analysis. Finally, the book covers (longitudinal) VBM methods and its application to Alzheimer’s disease study.

Автор жөнүндө

Dr. Xingfeng Li obtained his first degree in automation control, master degree of engineer in power system control, and Ph.D. degree in pattern recognition and machine intelligence in 1996, 2001, and 2004, respectively. Since then, he has been working in various research institutions in different countries on MRI and PET image analysis. He worked as postdoc research fellow from 2004-2009 at McGill University in Canada and INSERM, Paris 6th University in France. From 2009–2013, he has been a research fellow at the University of Ulster, UK. He is currently working on applying nonlinear system identification theory for studying nonlinear dynamic brain system. He conducts extensive research work using fMRI, diffusion weighted imaging, perfusion weighted imaging, structural MRI, and PET methods to investigate human brain system. His research interests include functional medical imaging analysis, numerical analysis, statistical analysis, nonlinear system identification, and optimization algorithms. He has published dozen of papers in the journals NeuroImage, IEEE Transaction on Medical Imaging, and Medical Image Analysis. He is also a member of the editorial board of Journal of Nonlinear Dynamics.