HeuDiConv¶
a heuristic-centric DICOM converter
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).
Installation¶
See our installation page on heudiconv.readthedocs.io .
HOWTO 101¶
In a nutshell – heudiconv operates using a heuristic which, given metadata from DICOMs, would decide how to name
resultant (from conversion using dcm2niix) files. Heuristic convertall could actually be used with no real
heuristic and by simply establish your own conversion mapping through editing produced mapping files.
In most use-cases of retrospective study data conversion, you would need to create your custom heuristic following
existing heuristics as examples and/or
referring to “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¶
HeuDiConv sources are managed with Git on GitHub. Please file issues and suggest changes via Pull Requests.
HeuDiConv requires installation of dcm2niix and optionally DataLad.
For development, you will need a non-shallow clone (so there is a
recent released tag) of the aforementioned repository. Once you have cloned the repository,
you can then install all the necessary development requirements using pip install -r
dev-requirements.txt. Testing is done using pytest. Releases are packaged using Intuit
auto. Workflow for releases and preparation of Docker images is in
.github/workflows/release.yml.