portokalh / bruker2nifti

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

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.

Please note that the stable release is compatible only with Python 2. The development release is Python 2 and Python 3 compatible.

Getting Started

• Requirements

  • 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 is implemented with nosetest. After installing the latest development version, type nosetests to run the tests.
Some of the tests are based on an open dataset Bruker images downloadable with the repo, in the folder test_data. Bruker2nifti_qa provides more Bruker raw data for further experiments.

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 and Licence

Copyright (c) 2017, Sebastiano Ferraris. Bruker2nifti is available as free open-source software under MIT License.

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).