Medical image format converter: from raw Bruker ParaVision to nifti.
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README.md

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Bruker2nifti

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 Python tool provided with a Graphical User Interface and a Command Line Utility to access the conversion method.

Getting Started

Accessing only the GUI with no Python knowledge required

gui_example

API documentation, additional notes, examples and list of Bruker converter

Code Testing and Continuous Integration

  • Unit testing with nosetest. Type nosetests in a terminal at the cloned repository.
  • Tests are based on the open dataset in the folder test_data.
  • Bruker2nifti_qa provides more Bruker raw data for further experiments (thanks to Mikaël Naveau).
  • Coverage percentage. Created with:
nosetests --with-coverage --cover-package=bruker2nifti &> nose_coverage.txt

Use

nosetests --with-coverage --cover-package=bruker2nifti --cover-erase --cover-html
open cover/index.html 

to see the HTML output.

  • Current deployment version undergoes continuous integration on travis-ci.

Support and contributions

Please see the contribution guideline for bugs report, feature requests and code style.

Copyright, Licence and How to Cite

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}
}

Acknowledgements

  • 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).