Michael Hanke
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84 lines
4.4 KiB
Markdown
84 lines
4.4 KiB
Markdown
---
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title: BIDS conversion
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---
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Converting the heterogeneous, site-specific raw MRI data acquisitions into a standardized dataset is an essential precondition for the collaborative work in TRR379.
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It readies the data for processing with established pipelines, and applies a pseudonymization as a safeguard for responsible use of this personal data.
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TRR379 uses the [Brain Imaging Data Structure](bids.neuroimaging.io) (BIDS) as the standard for its datasets.
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## Conversion to BIDS
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The conversion of raw MRI data in DICOM format to a BIDS-compliant dataset is a largely automated process.
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The recommended software to be used for conversion is [Heudiconv](https://heudiconv.readthedocs.io).
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Heudiconv uses [dcm2niix](https://github.com/rordenlab/dcm2niix) as the actual DICOM→NIfTI converter.
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In our experience, `dcm2niix` is the most robust and most correct tool available for this task.
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Heudiconv does the job of mapping DICOM series to BIDS entities (ie. determine BIDS-compliant file names).
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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.
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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.
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Because TRR379 uses its own conventions, a matching heuristic needs to be provided (possibly one for each TRR379 site).
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An [implementation of such a heuristic](https://hub.trr379.de/q02/phantom-mri-bids/src/branch/main/code/heuristic-q01.py) has been created, and was tested on phantom MRI acquisitions from all sites (see below).
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Using this heuristic, MRI data from all sites can be BIDS-standardized.
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As with any automation, caution and oversight is needed for edge cases (e.g. repeated / discarded acquisitions).
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[Heudiconv tutorials](https://heudiconv.readthedocs.io/en/latest/tutorials.html) further illustrate the process and capabilities of the software.
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### Good practices
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- Use heudiconv as a containerized application. Q02 provides a [readily usable utility dataset with a
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configured container](https://hub.trr379.de/q02/heudiconv-container). See that repository for
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an example usage.
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- DICOMs as subdatasets helps with provenance, even if those DICOMs are never accessed outside
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- Heudiconv takes paths and (optionally) intended subject IDs as input
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- if paths contain identifying information, this would leak into DataLad run records
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- having a helper script / lookup table in the (private) DICOM dataset can hide this information
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### Caveats
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- https://hub.trr379.de/q02/phantom-mri-bids used dcm2niix v1.0.20240202
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- current latest is v1.0.20250506
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- potential impact discussed in https://hub.trr379.de/q02/phantom-mri-bids/issues/8
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## Demonstrators and resources
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### TRR phantom DICOMs
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Scans of MRI phantoms were carried out using the intended sequences (presumably - see caveats section below).
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These were shared with Q02 and uploaded to the TRR Hub forgejo instance:
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- https://hub.trr379.de/q01/phantom-mri-dicom-aachen
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- https://hub.trr379.de/q01/phantom-mri-dicom-frankfurt
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- https://hub.trr379.de/q01/phantom-mri-dicom-heidelberg
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- https://hub.trr379.de/q01/phantom-mri-dicom-mannheim
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Note: Aachen did a re-scan which was shared by e-mail / cloud (June 03, 2025).
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This has not been uploaded to forgejo (permissions + size).
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### TRR phantom BIDS
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- A BIDS-compliant dataset from these dicoms (3/4 sites): https://hub.trr379.de/q02/phantom-mri-bids
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- The heuristic used: https://hub.trr379.de/q02/phantom-mri-bids/src/branch/main/code/heuristic-q01.py
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- Issue tracker: https://hub.trr379.de/q02/phantom-mri-bids/issues
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Conversion of re-scanned Aachen phantom is in https://hub.trr379.de/q02/tmp-phantom-bids (separate from the above
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because input data is not available as a DataLad dataset)
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### Data consistency
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- the phantom datasets are not the same: https://hub.trr379.de/q02/phantom-mri-bids/issues/6
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- re-scan from Aachen has more sequences than the initial scan, but lacks T2w
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- heudiconv fails to parse some Heidelberg dicoms, and dcm2niix raises warnings;
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unclear whether this is data issue or software issue: https://hub.trr379.de/q02/phantom-mri-bids/issues/5
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### Conversion: technical issues
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These are open questions:
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- Technical: BIDS validator errors https://hub.trr379.de/q02/phantom-mri-bids/issues/7
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- Technical: re-run with the latest dcm2niix https://hub.trr379.de/q02/phantom-mri-bids/issues/8
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