qMRLab is an open-source software for quantitative MR image analysis.
The main goal is to provide the community with an intuitive tool for data fitting, plotting, simulation and protocol optimization for a myriad of different quantitative models. The modularity of the implementation makes it easy to add any additional modules and we encourage everyone to contribute their favorite recipe for qMR!
For documentation, visit the Documentation website.
If you are a developer, please visit the Wiki page.
Please report any bug or suggestions in GitHub.
qMRLab is a fork from the initial project qMTLab.
- Karakuzu et al. The qMRLab workflow: From acquisition to publication. ISMRM 2019
- Duval et al. Quantitative MRI made easy with qMRLab. ISMRM 2018
- Cabana et al. Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization. Concepts in Magn Reson 2015
- Soustelle et al. Correlations of quantitative MRI metrics with myelin basic protein (MBP) staining in a murine model of demyelination. NMR in Biomed 2019
- Kim et al. Rapid framework for quantitative magnetization transfer imaging with interslice magnetization transfer and dictionary‐driven fitting approaches. Mag Res Med 2019
- Boudreau et al. All you need is a browser: eliminating barriers to MRI education with open-source interactive tutorials. Junior Fellows Symposium: Africa Challenge, ISMRM 2019
- Romero and Sinha. Magnetization Transfer Saturation Imaging of Human Calf Muscle: Reproducibility and Sensitivity to Regional and Sex Differences. J Magn Reson Imaging 2019
- Michálek et al. Fast and accurate compensation of signal offset for T2 mapping. Magn Reson Mater Phy 2019
- Barbieri et al. Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach. arXiv:1811.11477
- Varma et al. Low duty-cycle pulsed irradiation reduces magnetization transfer and increases the inhomogeneous magnetization transfer effect. J of Mag Res 2018
- Campbell et al. Promise and pitfalls of g-ratio estimation with MRI. NeuroImage 2018
- Boudreau and Pike. Sensitivity regularization of the Cramér‐Rao lower bound to minimize B1 nonuniformity effects in quantitative magnetization transfer imaging. Mag Res Med 2018
- Boudreau M. Relaxometry Series: MP2RAGE T1 Mapping. qMRLab.org 2019
- Boudreau M. Relaxometry Series: Variable Flip Angle T1 Mapping. qMRLab.org 2018
- Boudreau M. Relaxometry Series: Inversion Recovery T1 Mapping. qMRLab.org 2018
- Karakuzu et al. Quantitative MR Study Group Competition, second place, ISMRM 2019
- Boudreau et al. Junior Fellows Symposium Challenge, Africa challenge winner, ISMRM 2019
If you use qMRLab in you work, please cite:
Cabana, J.-F., Gu, Y., Boudreau, M., Levesque, I. R., Atchia, Y., Sled, J. G., Narayanan, S., Arnold, D. L., Pike, G. B., Cohen-Adad, J., Duval, T., Vuong, M.-T. and Stikov, N. (2016), Quantitative magnetization transfer imaging made easy with qMTLab: Software for data simulation, analysis, and visualization. Concepts Magn. Reson.. doi: 10.1002/cmr.a.21357
The MIT License (MIT)
Copyright (c) 2016 NeuroPoly, Ecole Polytechnique, Universite de Montreal
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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