The hMRI-toolbox

A toolbox for quantitative MRI and in vivo histology using MRI (hMRI).

Background

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,Weiskopf et al. 2021).

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 (MTsat); Weiskopf et al. 2013), followed by spatial registration in common space for statistical analysis (Draganski et al. 2011). It is embedded in the Statistical Parametric Mapping (SPM) framework, meaning that 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 cortical and 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 therefore represents a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction.

Wiki-Pages

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 and example dataset papers

For a reference on the scientific background, methods and concepts please use the hMRI-toolbox paper and cite it when publishing results compiled with the hMRI-toolbox.

A full example dataset can be obtained from one of the following links: (GitHub | OwnCloud | Mega). The description of the example dataset is also available in this paper, which should be cited when publishing results using the example dataset.

A qMRI brain imaging data structure (BIDS) compatible version of the example dataset can be downloaded from the ds-mpm folder at https://osf.io/k4bs5/.

Example acquisition protocols

Several example MRI protocols (standard MPM protocol using customised Siemens sequences, MPM protocols implemented using Siemens and Philips product sequences) as well as a setup (for Siemens 3T MRI) and usage tutorial are available on GitHub. 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.

Licence

The hMRI-toolbox is free but copyright software, distributed under the terms of the GNU General Public License 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 at www.gnu.org/copyleft/. In particular, the hMRI-toolbox is supplied as is. No formal support or maintenance is provided or implied.

Download

The latest release (as well as previous- and pre-releases) of the hMRI-toolbox Matlab code can be downloaded as a zip archive (.zip) or a Tarball (.tar.gz) from the releases page.

A compiled version of the toolbox which does not require a Matlab license is also provided and can be run using the Neurodesk framework; see here for more details.

E-Mail List

We have created an e-mail list for users of the hMRI-toolbox: HMRI-TOOLBOX@JISCMAIL.AC.UK. Registered users can login to view the message archive on the list homepage.

Workshops

Materials from the recent hMRI toolbox workshop in Bordeaux (part of the OHBM OSSIG 2026 BrainHack) can be found here.

Developers of the hMRI-toolbox

The development of the hMRI-toolbox is an international collaborative effort including the following developers and sites:

• Baris E. Ugurcan, Tobias Leutritz, Enrico Reimer, Nikolaus Weiskopf (Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany)
• Luke J. Edwards (Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; previously Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany)
• Evelyne Balteau, Christophe Phillips (University of Liège, Liège, Belgium)
• Siawoosh Mohammadi (Department of Neuroradiology, University of Lübeck, Lübeck, Germany; previously Medical Center Hamburg-Eppendorf, Hamburg, Germany)
• Martina F. Callaghan, John Ashburner (Functional Imaging Laboratory, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom)
• Karsten Tabelow (Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany)
• Ferath Kerif, Antoine Lutti (LREN, DNC - CHUV, University Lausanne, Lausanne, Switzerland)
• Bogdan Draganski (University Bern, Bern, Switzerland; previously 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)

Toolbox Reference

Please cite this key reference when you use the toolbox:

• 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., Weiskopf, N., Ziegler, G., Mohammadi, S., 2019. “hMRI – A toolbox for quantitative MRI in neuroscience and clinical research.” Neuroimage 194, 191-210. doi:10.1016/j.neuroimage.2019.01.029

Additional References

Please also cite the relevant references for the methods that are implemented in the toolbox detailed below.

Quantitative map creation

• Helms, Gunther, Henning Dathe, and Peter Dechent. 2008. “Quantitative FLASH MRI at 3T Using a Rational Approximation of the Ernst Equation.” Magnetic Resonance in Medicine 59(3):667–72. doi:10.1002/mrm.21542
• Helms, Gunther, Henning Dathe, Kai Kallenberg, and Peter Dechent. 2008. “High-Resolution Maps of Magnetization Transfer with Inherent Correction for RF Inhomogeneity and T1 Relaxation Obtained from 3D FLASH MRI.” Magnetic Resonance in Medicine 60(6):1396–1407. doi:10.1002/mrm.21732
• Weiskopf, Nikolaus, Martina F. Callaghan, Oliver Josephs, Antoine Lutti, and Siawoosh Mohammadi. 2014. “Estimating the Apparent Transverse Relaxation Time (R2*) from Images with Different Contrasts (ESTATICS) Reduces Motion Artifacts.” Frontiers in Neuroscience 8(September):1–10. doi:10.3389/fnins.2014.00278
• Edwards, LJ Pine, KJ, Lipp, I et al. 2026. “Analytical Dual Flip Angle R1 Calculation Outside the Small-Angle Regime.” Magnetic Resonance in Medicine 95.4:2420–2428. doi:10.1002/mrm.70174
• 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. doi:10.3389/fnins.2013.00095
• Leutritz, Tobias et al. 2020. “Multiparameter Mapping of Relaxation (R1, R2*), Proton Density and Magnetization Transfer Saturation at 3 T: A Multicenter Dual-Vendor Reproducibility and Repeatability Study.” Human Brain Mapping 41(15):4232–47. doi:10.1002/hbm.25122

Artefact correction

• Lutti, Antoine et al. 2012. “Robust and Fast Whole Brain Mapping of the RF Transmit Field B1 at 7T.” PloS one 7(3):e32379. doi:10.1371/journal.pone.0032379
• Lutti, Antoine, Chloe Hutton, Jürgen Finsterbusch, Gunther Helms, and Nikolaus Weiskopf. 2010. “Optimization and Validation of Methods for Mapping of the Radiofrequency Transmit Field at 3T.” Magnetic Resonance in Medicine 64(1):229–38. doi:10.1002/mrm.22421
• Weiskopf, Nikolaus et al. 2011. “Unified Segmentation Based Correction of R1 Brain Maps for RF Transmit Field Inhomogeneities (UNICORT).” NeuroImage 54(3):2116–24. doi:10.1016/j.neuroimage.2010.10.023
• Papp, Daniel, Martina F. Callaghan, Heiko Meyer, Craig Buckley, and Nikolaus Weiskopf. 2016. “Correction of Inter-Scan Motion Artifacts in Quantitative R1 Mapping by Accounting for Receive Coil Sensitivity Effects.” Magnetic Resonance in Medicine 76(5):1478–85. doi:10.1002/mrm.26058
• Balbastre Y, Aghaeifar A, Corbin N, Brudfors M, Ashburner J, Callaghan MF. 2022. “Correcting inter-scan motion artifacts in quantitative R1 mapping at 7T.” Magn Reson Med. 88:280-291. doi:10.1002/mrm.29216
• Corbin, Nadège and Martina F. Callaghan. 2021. “Imperfect Spoiling in Variable Flip Angle T1 Mapping at 7T: Quantifying and Minimizing Impact.” Magnetic Resonance in Medicine 86(2):693–708. doi:10.1002/mrm.28720

Group analysis / statistical methods

• 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. doi:10.1016/j.neuroimage.2011.01.052
• Mohammadi, Siawoosh et al. 2022. “Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.” Neuroimage. doi:10.1016/j.neuroimage.2022.119529
• Lutti, A., Corbin, N., Ashburner, J., Ziegler, G., Draganski, B., Phillips, C., Kherif, F., Callaghan, M. F., & Di Domenicantonio, G. 2022. “Restoring statistical validity in group analyses of motion-corrupted MRI data.” Human Brain Mapping, 43(6), 1973–1983. doi:10.1002/hbm.25767
• Corbin, Nadège, Rita Oliveira, Quentin Raynaud, Giulia Di Domenicantonio, Bogdan Draganski, Ferath Kherif, Martina F. Callaghan, Antoine Lutti. 2023. “Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans.” Journal of Neuroscience Methods. doi:10.1016/j.jneumeth.2023.109950

Denoising

• Veraart et al., 2016. “Denoising of diffusion MRI using random matrix theory.” Neuroimage 142, p 394–406 doi:10.1016/j.neuroimage.2016.08.016
• Does MD, Olesen JL, Harkins KD, et al. 2019. “Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry.” Magn Reson Med. 2019; 81:3503–3514. doi:10.1002/mrm.27658
• Bazin P-L, Alkemade A, van der Zwaag W, Caan M, Mulder M and Forstmann BU, 2019. “Denoising High-Field Multi-Dimensional MRI With Local Complex PCA.” Front. Neurosci. 13:1066. doi:10.3389/fnins.2019.01066
• Herthum H, Hetzer S. 2024. “Tensor denoising of quantitative multi-parameter mapping.” Magn Reson Med. 92:145–157. doi:10.1002/mrm.30050

Applications

• 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. doi:10.3389/fnins.2015.00441
• Callaghan, Martina F. et al. 2014. “Widespread Age-Related Differences in the Human Brain Microstructure Revealed by Quantitative Magnetic Resonance Imaging.” Neurobiology of Aging 35:1862–72. doi:10.1016/j.neurobiolaging.2014.02.008
• Farner, L., Scheuren, P.S., Sharifi, K. et al. 2026. “Anti-Nogo-A NG101 treatment induces changes in spinal cord micro- and macrostructure following spinal cord injury.” Nat Commun 17, 4197. doi:10.1038/s41467-026-71412-0

Reviews

• 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. doi:10.1097/WCO.0000000000000222
• N. Weiskopf, L. Edwards, G. Helms, S. Mohammadi, and E. Kirilina. 2021. “Quantitative Magnetic Resonance Imaging of Brain Anatomy: Towards in-Vivo Histology.” Nature Reviews Physics. doi:10.1038/s42254-021-00326-1

Datasets

• 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., Weiskopf, N., 2019. “Example dataset for the hMRI toolbox.” Data in Brief 25, 104132. doi:10.1016/j.dib.2019.104132
• Karakuzu, A., Whitaker, K. J., de Hollander, G., Lazari, A., Phillips, C., Khan, A., & Gau, R. 2023. “qMRI-BIDS example dataset.” doi:10.17605/OSF.IO/K4BS5

Acknowledgments and Funding

• E.B. received funding from the European Structural and Investment Fund / European Regional Development Fund & the Belgian Walloon Government, project BIOMED-HUB (programme 2014-2020).
• N.W. 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.
• S.M. received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 658589.
• N.W. and S.M. received funding from the BMBF (01EW1711A and B) in the framework of ERA-NET NEURON.
• S.M. has received funding from the European Union by ERC grant (Acronym: MRStain, Grant agreement ID: 101089218, DOI: 10.3030/101089218). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
• S.M. supported by the German Research Foundation (DFG Priority Program 2041 “Computational Connectomics”, [MO 2397/5-1, MO 2397/5-2], by the Emmy Noether Stipend: MO 2397/4-1; MO 2397/4-2).
• B.D. is supported by the Swiss National Science Foundation (project grant no. 213595, 32003B_135679, 32003B_159780, 324730_192755 and CRSK-3_190185), InnoSuisse Flagship Swiss brAInHealth project, ERA_NET NEURON JTC2020: iSEE and JTC2023-ELSA: BrainTree projects.
• M.F.C.’s research was funded in whole or in part by the Discovery Research Platform for Naturalistic Neuroimaging funded by the Wellcome [226793/Z/22/Z].
• A.L. is supported by the Swiss National Science Foundation (project grant Nr CR00I5-235940).
• The Wellcome Centre for Human Neuroimaging is supported by core funding from the Wellcome [203147/Z/16/Z].
• C.P. is supported by the F.R.S.-FNRS, Belgium.
• F.K. is funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101095384 (PHASE IV AI), by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871643 (MORPHEMIC), and by Collaborative Research on Science and Society 2026 (CROSS 2026) UNIL-EPFL.
• The hMRI Toolbox project is supported by the Max Planck Society.