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Author Notes:

K. Tabelow: karsten.tabelow@wias-berlin.de

E. Balteau: e.balteau@uliege.be

S. Mohammadi: s.mohammadi@uke.de

Evelyne Balteau, Karsten Tabelow and Siawoosh Mohammadi contributed equally to this paper.

The authors thank Sanne Kikkert at ETH Zurich for helpful comments on the manuscript.

We thank Rebecca Samson (UCL London), Giacomo Luccichenti (FSL Rome), and Paul Summers (IRCCS Bologna) for assisting with Philips MRI protocols.

Subjects:

Research Funding:

Complete funding list available in full text.

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neuroimaging
  • Radiology, Nuclear Medicine & Medical Imaging
  • Neurosciences & Neurology
  • Quantitative MRI
  • In vivo histology
  • Microstructure
  • Multi-parameter mapping
  • Relaxometry
  • SPM toolbox
  • HUMAN BRAIN
  • IN-VIVO
  • MAGNETIZATION-TRANSFER
  • FLIP-ANGLE
  • PROBABILISTIC ATLAS
  • TRANSMIT FIELD
  • CEREBRAL-CORTEX
  • WATER-CONTENT
  • STEADY-STATE
  • G-RATIO

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

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Tools:

Journal Title:

NeuroImage

Volume:

Volume 194

Publisher:

, Pages 191-210

Type of Work:

Article | Final Publisher PDF

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 R 1 and R 2⋆ , 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.

Copyright information:

© 2019 The Authors

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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