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Neuroimage. 2019 Jan 21. pii: S1053-8119(19)30029-1. doi: 10.1016/j.neuroimage.2019.01.029. [Epub ahead of print]

hMRI - A toolbox for quantitative MRI in neuroscience and clinical research.

Author information

1
WIAS Berlin, Germany. Electronic address: karsten.tabelow@wias-berlin.de.
2
GIGA Institute, University of Liège, Liège, Belgium. Electronic address: e.balteau@uliege.be.
3
Wellcome Centre for Human Neuroimaging, London, UK.
4
Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
5
Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
6
Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland.
7
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
8
GIGA Institute, University of Liège, Liège, Belgium.
9
Emory University, Atlanta, USA.
10
University of Zurich, Switzerland.
11
Institute for Cognitive Neurology and Dementia Research, University of Magdeburg, Germany.
12
Medical Center Hamburg-Eppendorf, Hamburg, Germany. Electronic address: s.mohammadi@uke.de.

Abstract

Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.

KEYWORDS:

In-vivo histology; Microstructure; Multi-parameter mapping; Quantitative MRI; Relaxometry; SPM toolbox

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