A toolbox for quantitative MRI and in vivo histology using MRI (hMRI)
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 (Weiskopf et al., 2015).
The hMRI-toolbox is an easy-to-use open-source and flexible tool, for qMRI data handling and processing.
It 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) (Weiskopf et al., 2013),
followed by spatial registration in common space for statistical analysis (Draganski et al., 2011).
Embedded in the Statistical Parametric Mapping (SPM) framework,
it can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps,
and it benefits from the extensive range of established SPM tools
for high-accuracy spatial registration and statistical inferences.
The qMRI maps generated by the toolbox can be used
for quantitative parameter analysis and accurate delineation of subcortical brain structures.
They 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 (Mohammadi et al., 2015).
The hMRI toolbox is therefore the first step towards in vivo histology using MRI (hMRI) and is being
extended further in this direction.
The hMRI-toolbox is free but copyright software, distributed under the terms of the GNU General Public Licence as published by the Free Software Foundation (either version 2, as given in file LICENSE, or at your option, any later version). Further details on "copyleft" can be found here. In particular, the hMRI-toolbox is supplied as is. No formal support or maintenance is provided or implied.
The latest release (and previous releases and pre-releases) of the hMRI-toolbox Matlab code can be downloaded from the
The latest release is v0.2.0 (2018/12/21).
Online documentation is available as a Wiki.
It provides guidelines and instructions for installation and usage of the hMRI-toolbox.
These pages are work-in-progress and updated on a regular basis.
hMRI-toolbox Paper & Poster
For a reference on the scientific background, methods and concepts please use this paper (Tabelow et al., 2019; also available as a pre-print paper) and cite it when publishing results compiled with the hMRI-toolbox.
A conference poster is also available here.
Sample Dataset and MRI Acquisition Protocols
A sample dataset is provided as reference and material for exploring the possibilities and functionalities of the hMRI-toolbox.
It is publicly available for download here (Callaghan et al., 2019).
Several example MRI protocols (standard MPM protocol using customised Siemens sequences, as well MPM protocol implemented using Siemens and Philips product sequences)
as well as a setup (for Siemens 3T MRI) and usage tutorial are available at the same URL.
Please be aware that we provide these protocols and information without any warranty.
They must be considered work-in-progress, possibly non-optimal protocols and information.
We have created an e-mail list for users of the hMRI-toolbox. Registered users can login to view the message archive at the list home page.
Developers of the hMRI-toolbox
The development of the hMRI-toolbox is an international collaborative effort including the following sites and developers:
- Tobias Leutritz, Enrico Reimer, Nikolaus Weiskopf (Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany)
- Evelyne Balteau, Christophe Phillips (University of Liege, Liege, Belgium)
- Siawoosh Mohammadi (Medical Center Hamburg-Eppendorf, Hamburg, Germany)
- Martina F Callaghan, John Ashburner (University College London, London, United Kingdom)
- Karsten Tabelow (Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany)
- Bogdan Draganski, Ferath Kerif, Antoine Lutti (LREN, DNC - CHUV, University Lausanne, Lausanne, Switzerland)
- Maryam Seif (University of Zurich, Zurich, Switzerland)
- Gunther Helms (Department of Medical Radiation Physics, Lund University, Lund, Sweden)
- Lars Ruthotto (Emory University, Atlanta, GA, United States)
- Gabriel Ziegler (Otto-von-Guericke-University Magdeburg, Magdeburg, Germany)
Draganski, B., Ashburner, J., Hutton, C., Kherif, F., Frackowiak, R.S.J., Helms, G., Weiskopf, N., 2011. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage 55, 1423-1434. https://doi.org/10.1016/j.neuroimage.2011.01.052
Mohammadi, S., Carey, D., Dick, F., Diedrichsen, J., Sereno, M.I., Reisert, M., Callaghan, M.F., Weiskopf, N., 2015. Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers. Front Neurosci 9, 441. https://doi.org/10.3389/fnins.2015.00441
Weiskopf, N., Mohammadi, S., Lutti, A., Callaghan, M.F., 2015. Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Curr. Opin. Neurol. 28, 313-322. https://doi.org/10.1097/WCO.0000000000000222
Weiskopf, N., Suckling, J., Williams, G., Correia, M.M., Inkster, B., Tait, R., Ooi, C., Bullmore, E.T., Lutti, A., 2013. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front. Neurosci. 7, 95. https://doi.org/10.3389/fnins.2013.00095
Tabelow, K., Balteau, E., Ashburner, J., Callaghan, M. F., Draganski, B., Helms, G., Kherif, F., Leutritz, T., Lutti, A., Phillips, C., Reimer, E., Ruthotto, L., Seif, M., Weiskop, N., Ziegler, G., Mohammadi, S., 2019. hMRI – A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 194, 191-210. https://doi.org/10.1016/j.neuroimage.2019.01.029
Callaghan, M. F., Lutti, A., Ashburner, J., Balteau, E., Corbin, N., Draganski, B., Helms, G., Kherif, F., Leutritz, T., Mohammadi, S., Phillips, C., Reimer, E., Ruthotto, L., Seif, M., Tabelow, K., Ziegler, G., Weiskop, N., 2019. Example dataset for the hMRI toolbox. Data in Brief 25, 104132. https://doi.org/10.1016/j.dib.2019.104132
Acknowledgments and Funding
- EB received funding from the European Structural and Investment Fund / European Regional Development Fund & the Belgian Walloon Government, project BIOMED-HUB (programme 2014-2020).
- NW received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement No 616905. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the grant agreement No 681094, and is supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 15.0137.
- SM received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 658589.
- NW and SM received funding from the BMBF (01EW1711A and B) in the framework of ERA-NET NEURON. BD is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant Nr 32003B_159780) and Foundation Synapsis. LREN is very grateful to the Roger De Spoelberch and Partridge Foundations for their generous financial support.
- MFC is supported by the MRC and Spinal Research Charity through the ERA-NET Neuron joint call (MR/R000050/1).
- The Wellcome Centre for Human Neuroimaging is supported by core funding from the Wellcome [203147/Z/16/Z].
- CP is supported by the F.R.S.-FNRS, Belgium.
- The hMRI-toolbox project is supported by the Max Planck Society.