============= HeuDiConv

a heuristic-centric DICOM converter

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About

heudiconv is a flexible DICOM converter for organizing brain imaging data into structured directory layouts.

• it allows flexible directory layouts and naming schemes through customizable heuristics implementations
• it only converts the necessary DICOMs, not everything in a directory
• you can keep links to DICOM files in the participant layout
• using dcm2niix under the hood, it's fast
• it can track the provenance of the conversion from DICOM to NIfTI in W3C PROV format
• it provides assistance in converting to BIDS <http://bids.neuroimaging.io/>_.
• it integrates with DataLad <https://www.datalad.org/>_ to place converted and original data under git/git-annex version control, while automatically annotating files with sensitive information (e.g., non-defaced anatomicals, etc)

How to cite

Please use Zenodo record <https://doi.org/10.5281/zenodo.1012598>_ for your specific version of HeuDiConv. We also support gathering all relevant citations via DueCredit <http://duecredit.org>_.

How to contribute

HeuDiConv sources are managed with Git on GitHub <https://github.com/nipy/heudiconv/>_. Please file issues and suggest changes via Pull Requests.

HeuDiConv requires installation of dcm2niix <https://github.com/rordenlab/dcm2niix/>_ and optionally DataLad <https://datalad.org>_.

For development you will need a non-shallow clone (so there is a recent released tag) of the aforementioned repository. You can then install all necessary development requirements using pip install -r dev-requirements.txt. Testing is done using pytest <https://docs.pytest.org/>_. Releases are packaged using Intuit auto. Workflow for releases and preparation of Docker images is in .github/workflows/release.yml.