Bruker2nifti is an open source medical image format converter from raw Bruker ParaVision to NifTi, without any intermediate step through the DICOM standard formats.
Bruker2nifti is a pip-installable pure Python tool provided with a Graphical User Interface and a Command Line Utility to access the conversion method.
Since the release of ParaVision360v1.1, a NifTi format converter is natively embedded and would provide the long sought standard. Please consider this option before starting with bruker2nifti.
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Requirements
- Python 3 backward compatible with python 2.7
- Libraries in requirements.txt.
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Installation
- Install the latest stable release with
pip install bruker2nifti
. - Install the latest development version with
pip install -e .
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- Install the latest stable release with
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Real data examples
- To access the Graphical User interface and convert some data with no python knowledge required.
- GUI instructions and real data examples.
- API documentation.
- Wiki documentation with additional notes and examples.
- Links and list of available Bruker converter.
- Testing and Continuous integration with Pytest and Travis CI
- Local testing and coverage with pytest and coveragerc
- Tests are based on the benchmark dataset Bruker2nifti_qa (thanks to Mikaël Naveau)
Please see the contribution guideline for bugs report, feature requests and code style.
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Copyright (c) 2017, Sebastiano Ferraris, University College London.
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Bruker2nifti is provided as it is and copyrighted under MIT License.
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To cite the code in your research please cite:
- S. Ferraris, D. I. Shakir, J. Van Der Merwe, W. Gsell, J. Deprest, T. Vercauteren (2017), Bruker2nifti: Magnetic Resonance Images converter from Bruker ParaVision to Nifti format, Journal of Open Source Software, 2(16), 354, doi:10.21105/joss.00354
BibTeX entry:
@article{ferraris2017bruker2nifti,
title={{Bruker2nifti: Magnetic Resonance Images converter from Bruker ParaVision to Nifti format}},
author={Ferraris, Sebastiano and Shakir, Ismail Dzhoshkun and Van Der Merwe, Johannes and Gsell, Willy and Deprest, Jan and Vercauteren, Tom},
journal={Journal Of Open Source Software},
volume={2},
number={16},
pages={354},
year={2017},
publisher={Journal Of Open Source Software}
}
- This repository is developed within the GIFT-surg research project.
- Funding sources and authors list can be found in the JOSS submission paper.
- Thanks to Bernard Siow (Centre for Advanced Biomedical Imaging, University College London), Chris Rorden (McCausland Center for Brain Imaging, University of South Carolina) and Matthew Brett (Berkeley Brain Imaging Center).