============= 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 and ignores everything else in a directory.
• You can keep links to DICOM files in the participant layout.
• Using dcm2niix <https://github.com/rordenlab/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).

Heudiconv can be inserted into your workflow to provide automatic conversion as part of a data acquisition pipeline, as seen in the figure below:

.. image:: figs/environment.png

Installation

See our installation page <https://heudiconv.readthedocs.io/en/latest/installation.html>_ on heudiconv.readthedocs.io .

HOWTO 101

In a nutshell -- heudiconv is given a file tree of DICOMs, and it produces a restructured file tree of NifTI files (conversion handled by dcm2niix_) with accompanying metadata files. The input and output structure is as flexible as your data, which is accomplished by using a Python file called a heuristic that knows how to read your input structure and decides how to name the resultant files. You can run your conversion automatically (which will produce a .heudiconv directory storing the used parameters), or generate the default parameters, edit them to customize file naming, and continue conversion via an additional invocation of heudiconv:

.. image:: figs/workflow.png

heudiconv comes with existing heuristics <https://github.com/nipy/heudiconv/tree/master/heudiconv/heuristics>_ which can be used as is, or as examples. For instance, the Heuristic convertall <https://github.com/nipy/heudiconv/blob/master/heudiconv/heuristics/convertall.py>_ extracts standard metadata from all matching DICOMs. heudiconv creates mapping files, <something>.edit.text which lets researchers simply establish their own conversion mapping.

In most use-cases of retrospective study data conversion, you would need to create your custom heuristic following the examples and the "Heuristic" section <https://heudiconv.readthedocs.io/en/latest/heuristics.html>_ in the documentation. Note that ReproIn heuristic <https://github.com/nipy/heudiconv/blob/master/heudiconv/heuristics/reproin.py>_ is generic and powerful enough to be adopted virtually for any study: For prospective studies, you would just need to name your sequences following the ReproIn convention <https://github.com/nipy/heudiconv/blob/master/heudiconv/heuristics/reproin.py#L26>, and for retrospective conversions, you often would be able to create a new versatile heuristic by simply providing remappings into ReproIn as shown in this issue (documentation is coming) <https://github.com/ReproNim/reproin/issues/18#issuecomment-834598084>.

Having decided on a heuristic, you could use the command line::

heudiconv -f HEURISTIC-FILE-OR-NAME -o OUTPUT-PATH --files INPUT-PATHs

with various additional options (see heudiconv --help or "CLI Reference" in documentation <https://heudiconv.readthedocs.io/en/latest/commandline.html>__) to tune its behavior to convert your data.

For detailed examples and guides, please check out ReproIn conversion invocation examples <https://github.com/ReproNim/reproin/#conversion>_ and the user tutorials <https://heudiconv.readthedocs.io/en/latest/tutorials.html>_ in the documentation.

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

For a detailed into, see our contributing guide <CONTRIBUTING.rst>_.

Our releases are packaged using Intuit auto, with the corresponding workflow including Docker image preparation being found in .github/workflows/release.yml.

3-rd party heuristics

• https://github.com/courtois-neuromod/ds_prep/blob/main/mri/convert/heuristics_unf.py

Support

All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/nipy/heudiconv/issues.

If you have a problem or would like to ask a question about how to use heudiconv, please submit a question to NeuroStars.org <http://neurostars.org/tags/heudiconv>_ with a heudiconv tag. NeuroStars.org is a platform similar to StackOverflow but dedicated to neuroinformatics.

All previous heudiconv questions are available here: http://neurostars.org/tags/heudiconv/ .