Medical image format converter: from raw Bruker ParaVision to nifti.
Switch branches/tags
Nothing to show
Clone or download
Pull request Compare This branch is even with r03ert0:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
GUI
bruker2nifti
docs
paper
screenshots
test
test_data
.coveragerc
.gitignore
.travis.yml
CONTRIBUTE.md
LICENCE.txt
MANIFEST.in
README.md
nose_coverage.txt
requirements.txt
setup.py

README.md

status Build Status

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

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 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).