BIDS conversion

Converting the heterogeneous, site-specific raw MRI data acquisitions into a standardized dataset is an essential precondition for the collaborative work in TRR379. It readies the data for processing with established pipelines, and applies a pseudonymization as a safeguard for responsible use of this personal data.

TRR379 uses the Brain Imaging Data Structure (BIDS) as the standard for its datasets.

Conversion to BIDS

The conversion of raw MRI data in DICOM format to a BIDS-compliant dataset is a largely automated process. The recommended software to be used for conversion is Heudiconv.

Heudiconv uses dcm2niix as the actual DICOM→NIfTI converter. In our experience, dcm2niix is the most robust and most correct tool available for this task.

Heudiconv does the job of mapping DICOM series to BIDS entities (ie. determine BIDS-compliant file names). A key heudiconv concept is a heuristic: a Python program (function) which looks at the DICOM series properties and matches it with a file naming pattern. A heuristic typically relies on DICOM series naming (set at the scanner console), but it can also use other properties such as number of images or acquisition parameters.

Because TRR379 uses its own conventions, a matching heuristic needs to be provided (possibly one for each TRR379 site). An implementation of such a heuristic has been created, and was tested on phantom MRI acquisitions from all sites (see below). Using this heuristic, MRI data from all sites can be BIDS-standardized. As with any automation, caution and oversight is needed for edge cases (e.g. repeated / discarded acquisitions).

Heudiconv tutorials further illustrate the process and capabilities of the software.

Good practices

  • Use heudiconv as a containerized application. Q02 provides a readily usable utility dataset with a configured container. See that repository for an example usage.
  • DICOMs as subdatasets helps with provenance, even if those DICOMs are never accessed outside
  • Heudiconv takes paths and (optionally) intended subject IDs as input
    • if paths contain identifying information, this would leak into DataLad run records
    • having a helper script / lookup table in the (private) DICOM dataset can hide this information

Caveats

Demonstrators and resources

TRR phantom DICOMs

Scans of MRI phantoms were carried out using the intended sequences (presumably - see caveats section below). These were shared with Q02 and uploaded to the TRR Hub forgejo instance:

Note: Aachen did a re-scan which was shared by e-mail / cloud (June 03, 2025). This has not been uploaded to forgejo (permissions + size).

TRR phantom BIDS

Conversion of re-scanned Aachen phantom is in https://hub.trr379.de/q02/tmp-phantom-bids (separate from the above because input data is not available as a DataLad dataset)

Data consistency

Conversion: technical issues

These are open questions: