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Macmillan Higher Education

AVAILABLE FORMATS

Hardcover - 9789400773011

30 September 2013

$199.99

In stock


Ebook - 9789400773028

14 September 2013

$159.99

In stock


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...

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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.

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Mathematical equations with computer program algorithm implementation

Wide range of methods for magnetic resonance image analysis

Dealing with different image modalities

State of the art algorithms for data analysis

Preface
MRI perfusion weighted imaging analysis
Perfusion imaging
Gamma-variate fitting
AIF selection
Dispersion effects in DSC-MRI
-Summary of the PWI algorithm
First level fMRI data analysis for activation detection
fMRI experimental design
fMRI data pre-processing
Activation detection: model free and model based methods
Models for hemodynamic response function and drift
General linear model (GLM) for activation detect
Hypothesis test and threshold correction
Summary of algorithm for 1st level fMRI data analysis
2nd level fMRI data analysis using mixed model
Mixed model for fMRI data analysis
Numerical analysis for mixed effect models
Iterative trust region method for ML estimation
Exception trust region algorithm for second level fMRI data analysis
Degree of freedom (DF) estimation
fMRI data analysis future directions
Second level fMRI data processing algorithm summary
fMRI effective connectivity study
Nonlinear system identification method for fMRI effective connectivity analysis
Model selections for effective connectivity study
Robust method for second level analysis
Effective connectivity for resting-state fMRI data
Limitations for fMRI effective connectivity in this study
Summary of the algorithm for fMRI effective connectivity study.Diffusion weighted imaging analysis
Basic principle of diffusion MRI and DTI data analysis
Fiber tracking
High angular resolution diffusion imaging (HARDI) analysis
Adaptive Q-ball imaging regularization
Diffusion spectrum imaging
Summary and future directions
Summary of DTI, QBI and DSI image analysis methods
Voxel based morphometry and its application to Alzheimer’s disease study
Background for voxel based morphometry analysis
Enhanced VBM
Longitudinal VBM and its application to AD study
Effective connectivity for longitudinal data analysis
Other type of sMRI data analysis
Summary of (longitudinal) VBM analysis methods
Appendixes
Question answers and hints.
From the reviews:
 “This informative new book reviews the methods for functional brain imaging analysis. Written and edited by a researcher in the field, this book … is a welcome contribution to the field. … The targeted audience includes individuals with a background in computer programming, numerical analysis, statistics, and medical imaging analysis. … Each chapter ends with timely and relevant citations of the scientific literature.” (Michael Joel Schrift, Doody’s Book Reviews, March, 2014)
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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,...

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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.

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