Magnetic Resonance Brain Imaging : Modelling and Data Analysis Using R
Second Edition 2023
Book Details
Format
Paperback / Softback
Book Series
Use R!
ISBN-10
3031389484
ISBN-13
9783031389481
Edition
Second Edition 2023
Publisher
Springer International Publishing AG
Imprint
Springer International Publishing AG
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Oct 13th, 2023
Print length
258 Pages
Product Classification:
Medical imaging: nuclear magnetic resonance (NMR / MRI)Nuclear magnetic resonance (NMR / MRI)Probability & statisticsProbability and statisticsElectronics engineeringMathematical & statistical softwareMathematical and statistical softwareDigital signal processing (DSP)Signal processingImage processing
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This book discusses modelling and analysis of Magnetic Resonance Imaging (MRI) data of the human brain.
This book discusses modelling and analysis of Magnetic Resonance Imaging (MRI) data of the human brain. For the data processing pipelines we rely on R, the software environment for statistical computing and graphics. The book is intended for readers from two communities: Statisticians, who are interested in neuroimaging and look for an introduction to the acquired data and typical scientific problems in the field and neuroimaging students, who want to learn about the statistical modeling and analysis of MRI data. Being a practical introduction, the book focuses on those problems in data analysis for which implementations within R are available. By providing full worked-out examples the book thus serves as a tutorial for MRI analysis with R, from which the reader can derive its own data processing scripts.
The book starts with a short introduction into MRI. The next chapter considers the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters then cover four common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, Multi-Parameter Mapping and Inversion Recovery MRI. The book concludes with extended Appendices on details of the utilize non-parametric statistics and on resources for R and MRI data.
The book also addresses the issues of reproducibility and topics like data organization and description, open data and open science. It completely relies on a dynamic report generation with knitr: The books R-code and intermediate results are available for reproducibility of the examples.
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