Application
The hMRI-Toolbox is an easy-to-use open source tool for processing qMRI data, providing tools for estimation of high-quality quantitative maps from a wide diversity of acquisition protocols, embedded in the SPM framework.
Contributors
Karsten Tabelow1, Evelyne Balteau2, John Ashburner3, Martina F. Callaghan3, Bogdan Draganski4,5, Gunther Helms6, Ferath Kherif4, Tobias Leutritz5, Antoine Lutti4, Christophe Phillips2, Enrico Reimer5, Lars Ruthotto8, Maryam Seif9, Nikolaus Weiskopf5, Gabriel Ziegler10, Siawoosh Mohammadi11
Estimated cost
Free
Progress
stable release, GNU GPL v2.0
Resources
project website: https://hmri-group.github.io/hMRI-toolbox/
repository: https://github.com/hMRI-group/hMRI-toolbox
Wiki: https://github.com/hMRI-group/
Sample protocol & data: https://owncloud.gwdg.de/
E-mail list: https://www.jiscmail.ac.uk/
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.
Publications
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
Affiliations
1WIAS Berlin, Germany
2GIGA Institute, University of Liège, Liège, Belgium
3Wellcome Trust Centre for Neuroimaging, London, UK
4Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland
5Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
6Department of Medical Radiation Physics, Lund University, Lund, Sweden
7Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
8Emory University, Atlanta, USA
9University of Zurich, Switzerland
10Otto von Guericke Universitt Magdeburg, Germany
11Medical Center Hamburg-Eppendorf, Hamburg, Germany