Magnetic Resonance Brain Imaging

Modeling and Data Analysis Using R

verfasst von: Dr. Jörg Polzehl, Dr. Karsten Tabelow

Verlag: Springer International Publishing

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Über dieses Buch

This book discusses the modeling and analysis of magnetic resonance imaging (MRI) data acquired from the human brain. The data processing pipelines described rely on R. The book is intended for readers from two communities: Statisticians who are interested in neuroimaging and looking for an introduction to the acquired data and typical scientific problems in the field; and neuroimaging students wanting to learn about the statistical modeling and analysis of MRI data. Offering a practical introduction to the field, the book focuses on those problems in data analysis for which implementations within R are available. It also includes fully worked examples and as such serves as a tutorial on MRI analysis with R, from which the readers can derive their own data processing scripts.

The book starts with a short introduction to MRI and then examines the process of reading and writing common neuroimaging data formats to and from the R session. The main chapters cover three common MR imaging modalities and their data modeling and analysis problems: functional MRI, diffusion MRI, and Multi-Parameter Mapping. The book concludes with extended appendices providing details of the non-parametric statistics used and the resources for R and MRI data.The book also addresses the issues of reproducibility and topics like data organization and description, as well as open data and open science. It relies solely on a dynamic report generation with knitr and uses neuroimaging data publicly available in data repositories. The PDF was created executing the R code in the chunks and then running LaTeX, which means that almost all figures, numbers, and results were generated while producing the PDF from the sources.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Images are common in our lives. They come as simple photographs or as the result of various medical, technical, or scientific experiments and are often very easy to interpret for our visual capabilities as humans. It was a real revolution when Lauterbur and Mansfield invented the use of the magnetic resonance phenomenon to generate images of the human body. It enabled in vivo images of soft tissues and stipulated a lot of neuroscientific research on structure and function of the human brain. Often statistical models and methods are needed for the understanding of the information that is contained in the images. This has become even more important as neuroimaging evolved from providing images in two dimensions to three-dimensional volumes or time series of volumes or even data in five- or six-dimensional spaces. Then visual inspection becomes difficult if not impossible and the information has to be aggregated by appropriate methods. In the following chapters, we will demonstrate how such an analysis can be performed for the three MRI imaging modalities that we work with.

Jörg Polzehl, Karsten Tabelow

Chapter 2. Magnetic Resonance Imaging in a Nutshell

Abstract

Since its invention in the early 70s by Lauterbur (Lauterbur 1973; Mansfield and Grannell 1973) and Mansfield (Mansfield 1977), for which they shared the 2003 Nobel prize in Physiology and Medicine, magnetic resonance imaging (MRI) has evolved into a versatile tool for the in vivo examination of tissue. MRI is based on the nuclear magnetic resonance phenomenon. Although MRI is based on quantum mechanical properties of the particles at the sub-atom level, the large ensemble of particles in the tissue allows for a semiclassical description that can be relatively easily accessed. We very shortly review the basic ideas of MRI. A number of special MR imaging sequences, i.e., sequences of gradient and RF excitations, have proven to be very important for the neuroscientific research, especially the functional and diffusion-weighted MRI and, recently, the multiparameter mapping. These data and their analysis will be the subject of the main chapters of this book. Here we provide a teaser on the basic acquisition principles.

Jörg Polzehl, Karsten Tabelow

Chapter 3. Medical Imaging Data Formats

Abstract

There exist a large variety of data formats used in medical imaging in general and specifically for functional magnetic resonance imaging, diffusion-weighted imaging, or multiparameter mapping. Medical imaging data typically contain the actual data and additionally some metadata. This may be the data dimensionality, the spatial extension of the imaged voxel, but also physical parameters of the image acquisition, or patient data. The way this is stored in the different data formats differs. Here, we discuss DICOM\(^\circledR \), ANALYZE, and NIfTI formats as they are widely used for storing medical imaging data or analysis results that are interchangeable between different analysis software. We demonstrate how these data can be easily accessed from within R. This is amended with a short discussion of the Brain Imaging Data Structure (BIDS) standard.

Jörg Polzehl, Karsten Tabelow

Chapter 4. Functional Magnetic Resonance ImagingFunctional magnetic resonance imaging

Abstract

Functional magnetic resonance imaging (fMRI) maps brain activity by detecting changes in image intensity related to neural activity by the blood oxygenation level-dependent (BOLD) contrast. Functional MRI data essentially consists of time series of 3D images associated with a description of the experimental conditions. The chapter outlines an analysis pipeline for functional magnetic resonance imaging (fMRI) experiments completely based on R packages. Thereby, we focus on a single-subject analysis with the full pipeline of data preprocessing, the General Linear Model, and inference with correction for the multiplicity of the statistical tests. Part of the chapter elaborates on the use of structural adaptive smoothing procedure in fMRI, which we specifically developed. We also include alternative fMRI analysis methods, i.e., others then the mass-univariate approach. The chapter concludes with a section on functional connectivity.

Jörg Polzehl, Karsten Tabelow

Chapter 5. Diffusion-Weighted Imaging

Abstract

Diffusion-weighted Magnetic Resonance Imaging (dMRI) has long proven to be a versatile tool for the in vivo microstructural investigation of the human brain (Le Bihan 2003), the spinal cord (Clark et al. 1999), or even muscle tissue (Sinha et al. 2006). In contrast to conventional weighted MRI or functional MRI discussed in the preceding Chap. 4, it is quantitative in the sense that it directly infers on physical quantities with physical units, specifically the diffusion constant. In this chapter, we will first elaborate on the physical background before presenting experimental dMRI data and describe its processing. This includes preprocessing steps, i.e., the removal of artifacts, and the actual modeling of the data to infer on interesting and relevant quantities.

Jörg Polzehl, Karsten Tabelow

Chapter 6. Multiparameter Mapping

Abstract

Unlike conventional weighted MRI, leading to \(T_1\)-, \(T_2\)-, \(T_2^\star \)-, or proton density (\(P\!D\)) weighted images in arbitrary units, quantitative MRI (qMRI) aims to estimate absolute physical metrics. One example is dMRI considered in Chap. 5. qMRI is of increasing interest in neuroscience and clinical research for its greater specificity and its sensitivity to microstructural properties of brain tissue such as axon, myelin, iron and water concentration. Furthermore, the measurement of quantitative data allows for comparison across sites, time points, and participants, and enables longitudinal studies and multicenter trials. In order to maintain its comparability, quantitative maps obtained from qMRI have to be adjusted for instrumental biases. Then, in combination with biophysical models, qMRI can enable the in vivo characterization of key microscopic brain tissue parameters which previously could only be achieved with ex vivo histology. Here, we focus on the quantities that are accessible by the multiparameter mapping (MPM) approach. We will also present an adaptive smoothing algorithm for this type of data.

Jörg Polzehl, Karsten Tabelow

Backmatter

Titel: Magnetic Resonance Brain Imaging
verfasst von: Dr. Jörg Polzehl
Dr. Karsten Tabelow
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-29184-6
Print ISBN: 978-3-030-29182-2
DOI: https://doi.org/10.1007/978-3-030-29184-6

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Über dieses Buch

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Chapter 2. Magnetic Resonance Imaging in a Nutshell

Chapter 3. Medical Imaging Data Formats

Chapter 4. Functional Magnetic Resonance ImagingFunctional magnetic resonance imaging

Chapter 5. Diffusion-Weighted Imaging

Chapter 6. Multiparameter Mapping

Backmatter

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