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

• Requirements

  • Python 3 backward compatible with python 2.7
  • Libraries in requirements.txt.

• Installation

  • Install the latest stable release with pip install bruker2nifti.
  • Install the latest development version.

• Real data examples

  • Python command shell.
  • Command Line Utility (CLI).
  • Graphical User Interface (GUI).

Accessing only the GUI with no Python knowledge required

• To access the Graphical User interface and convert some data with no python knowledge required.
• GUI instructions and real data examples.

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

• API documentation.
• Wiki documentation with additional notes and examples.
• Links and list of available 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

• Copyright (c) 2017, Sebastiano Ferraris, University College London.
• Bruker2nifti is available as free open-source software under MIT License.
• To cite the code in your research please follow this link.

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).