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Magnetic resonance brain imaging = modelling and data analysis using R /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Magnetic resonance brain imaging/ by Jorg Polzehl, Karsten Tabelow.
其他題名:
modelling and data analysis using R /
作者:
Polzehl, Jorg.
其他作者:
Tabelow, Karsten.
出版者:
Cham :Springer International Publishing : : 2023.,
面頁冊數:
xxi, 258 p. :ill., digital ; : 24 cm.;
Contained By:
Springer Nature eBook
標題:
Signal, Speech and Image Processing. -
電子資源:
https://doi.org/10.1007/978-3-031-38949-8
ISBN:
9783031389498
Magnetic resonance brain imaging = modelling and data analysis using R /
Polzehl, Jorg.
Magnetic resonance brain imaging
modelling and data analysis using R /[electronic resource] :by Jorg Polzehl, Karsten Tabelow. - Second edition. - Cham :Springer International Publishing :2023. - xxi, 258 p. :ill., digital ;24 cm. - Use R,2197-5744. - Use R..
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.
ISBN: 9783031389498
Standard No.: 10.1007/978-3-031-38949-8doiSubjects--Topical Terms:
1414819
Signal, Speech and Image Processing.
LC Class. No.: RC386.6.M34
Dewey Class. No.: 616.8047548
Magnetic resonance brain imaging = modelling and data analysis using R /
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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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