a heuristic-centric DICOM converter
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).
See our installation page on heudiconv.readthedocs.io .
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 <https://github .com/nipy/heudiconv/blob/master/heudiconv/heuristics/convertall.py> 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-PATHswith 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.
Please use Zenodo record for your specific version of HeuDiConv. We also support gathering all relevant citations via DueCredit.
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.