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https://github.com/nipy/heudiconv

Flexible DICOM conversion into structured directory layouts
https://github.com/nipy/heudiconv

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Flexible DICOM conversion into structured directory layouts

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README 

        
=============

**HeuDiConv**

=============



`a heuristic-centric DICOM converter`



.. image:: https://joss.theoj.org/papers/10.21105/joss.05839/status.svg

   :target: https://doi.org/10.21105/joss.05839

   :alt: JOSS Paper



.. image:: https://img.shields.io/badge/docker-nipy/heudiconv:latest-brightgreen.svg?logo=docker&style=flat

  :target: https://hub.docker.com/r/nipy/heudiconv/tags/

  :alt: Our Docker image



.. image:: https://github.com/nipy/heudiconv/actions/workflows/test.yml/badge.svg?event=push

  :target: https://github.com/nipy/heudiconv/actions/workflows/test.yml

  :alt: GitHub Actions (test)



.. image:: https://codecov.io/gh/nipy/heudiconv/branch/master/graph/badge.svg

  :target: https://codecov.io/gh/nipy/heudiconv

  :alt: CodeCoverage



.. image:: https://readthedocs.org/projects/heudiconv/badge/?version=latest

  :target: http://heudiconv.readthedocs.io/en/latest/?badge=latest

  :alt: Readthedocs



.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1012598.svg

  :target: https://doi.org/10.5281/zenodo.1012598

  :alt: Zenodo (latest)



.. image:: https://repology.org/badge/version-for-repo/debian_unstable/heudiconv.svg?header=Debian%20Unstable

   :target: https://repology.org/project/heudiconv/versions

   :alt: Debian Unstable



.. image:: https://repology.org/badge/version-for-repo/gentoo_ovl_science/python:heudiconv.svg?header=Gentoo%20%28%3A%3Ascience%29

   :target: https://repology.org/project/python:heudiconv/versions

   :alt: Gentoo (::science)



.. image:: https://repology.org/badge/version-for-repo/pypi/python:heudiconv.svg?header=PyPI

   :target: https://repology.org/project/python:heudiconv/versions

   :alt: PyPI



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 `_ 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 `_.

- It integrates with `DataLad `_ 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 `_

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 `_ which can be used as is, or as examples.

For instance, the Heuristic `convertall `_ extracts standard metadata from all matching DICOMs.

``heudiconv`` creates mapping files, ``.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 `_ in the documentation.

**Note** that `ReproIn heuristic `_ 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 `_, 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) `_.



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

`"Usage" in documentation `__) to tune its behavior to

convert your data.



For detailed examples and guides, please check out `ReproIn conversion invocation examples `_

and the `user tutorials `_ in the documentation.



How to cite

-----------



Please use `Zenodo record `_ for

your specific version of HeuDiConv.  We also support gathering

all relevant citations via `DueCredit `_.



How to contribute

-----------------



For a detailed into, see our `contributing guide `_.



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 `_ 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/