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.
Highlights
Quantitative MRI finds increasing interest in neuroscience and clinical research because it is more sensitive, and more specific, to microstructural properties of brain tissue such as axon, myelin, iron and water concentration than conventional weighted MRI (Cercignani et al, 2018; Assaf and Basser, 2005; Draganski et al, 2011; Lorio et al, 2014, 2016a; Stüber et al, 2014; Callaghan et al, 2015a)
The value will depend on multiple physical tissue properties such as the longitudinal and transverse relaxation times, T1 and T2, or the proton density, PD (Helms et al, 2009, 2010). quantitative magnetic resonance imaging (qMRI) accounts for these varied effects in order to increase the specificity of the estimated metrics and eventually quantify specific physical tissue properties (Cercignani et al, 2018; Lutti et al, 2010; Weiskopf et al, 2013)
Review of studies related to the MPM acquisition protocol using predecessors of the histology using MRI (hMRI)-toolbox to make inference on myelin (My), iron (Fe), or the volume (Vol) measured by voxel-based morphometry (VBM)
Summary
Quantitative MRI (qMRI) finds increasing interest in neuroscience and clinical research because it is more sensitive, and more specific, to microstructural properties of brain tissue such as axon, myelin, iron and water concentration than conventional weighted MRI (Cercignani et al, 2018; Assaf and Basser, 2005; Draganski et al, 2011; Lorio et al, 2014, 2016a; Stüber et al, 2014; Callaghan et al, 2015a). A particular instantiation of qMRI developed at 3T, the MPM approach, spans data acquisition, modelling and bias correction of three multi-echo spoiled gradient echo volumes to generate R⋆2 , R1, PD, as well as semi-quantitative MT saturation maps This framework enables timeefficient whole brain mapping with high isotropic resolution of 800 μm in 27 min (Callaghan et al, 2015b) or reduced MPM protocol (no MT saturation) at 1 mm isotropic resolution in 14 min at 3T (Papp et al, 2016) and has even enabled the acquisition of ultra-high-resolution quantitative maps with 400 μm resolution at 7T (Trampel et al, 2017). The spatial processing part of the toolbox can be applied to any set of rotationally-invariant qMRI maps, including a number of diffusion MRI parameters and all common qMRI metrics
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