nifti-qc 0.1.0

Catch silently-broken NIfTI geometry (qform/sform mismatch, bad affines, misaligned inputs) before it ruins your results.

nifti-qc

Catch silently-broken NIfTI geometry before it ruins your results.

A NIfTI file stores its world→voxel mapping twice — the quaternion qform and the affine sform. Different tools trust different ones: nibabel and most Python tooling follow the sform; some C/registration tools (e.g. greedy) read the qform. When an earlier step updates only one of them — SPM co-registration is the classic culprit — the two disagree and your image is silently mislocated in world space. No error, no crash: just a segmentation, overlay, or registration that lands in the wrong place.

nifti-qc is a tiny, dependency-light QC gate that catches that trap and a handful of related geometry/data problems, so you find them in seconds instead of in a downstream result three steps later.

This tool grew out of a real fix for exactly this bug in the MS-lesion segmentation pipeline LST-AI (CompImg/LST-AI#45), where a qform/sform mismatch mislocated lesion masks in FLAIR space.

Install

pip install nifti-qc          # once published
# or from source:
pip install -e .

Requires only nibabel and numpy.

Use it on the command line

# QC a single file
nifti-qc scan t1.nii.gz

# QC each file AND check they share a grid / world space (e.g. T1 + FLAIR)
nifti-qc scan t1.nii.gz flair.nii.gz

# machine-readable, for CI / pipelines
nifti-qc scan *.nii.gz --json

Example output:

sub-01_flair.nii.gz  [RAS]
  ERROR  qform_sform_mismatch: qform and sform disagree: up to 10.000 mm
         translation and 0.000 deg rotation apart. Tools that read the qform
         (e.g. greedy) and tools that read the sform (e.g. nibabel) will place
         this image differently.

PROBLEMS: 1 error(s), 0 warning(s) across 1 file(s)

The command exits non-zero when any error-severity problem is found, so it drops straight into a CI step or a pre-processing script as a gate:

nifti-qc scan "$T1" "$FLAIR" || { echo "fix your inputs first"; exit 1; }

Use it in CI (GitHub Action)

A composite Action ships with the repo, so a workflow can gate on NIfTI geometry with no setup — the job fails if any file has an error-severity problem:

- uses: CedricConday/nifti-qc@main
  with:
    paths: data/*.nii.gz
    # args: "--no-align"   # optional flags for `nifti-qc scan`

Use it as a library

import nifti_qc

report = nifti_qc.scan(["t1.nii.gz", "flair.nii.gz"])
if not report.ok:
    for fr in report.files:
        for f in fr.findings:
            print(fr.path, f.code, f.severity, f.message)
    for f in report.alignment:
        print("align", f.code, f.message)

Every check is also importable on its own (check_affine, check_geometry, check_data, check_alignment) and returns plain Finding dataclasses.

What it checks

Check	Severity	What it means
qform_sform_mismatch	error	qform and sform disagree — the silent-mislocation trap
no_valid_affine	error	both codes are 0; world orientation undefined
qform_unset / sform_unset	warn	only one transform is set; some tools will misread
nonpositive_voxel_size	error	a voxel dimension is ≤ 0
extreme_anisotropy	warn	very non-isotropic voxels (default ≥ 5:1)
non_orthonormal_direction	warn	affine encodes shear — usually a corrupt header
non_finite_data	error	NaN/Inf voxels
empty_volume	warn	volume is all zeros
grid_shape_mismatch	warn	inputs live on different voxel grids (resampling needed)
voxel_size_mismatch	warn	inputs have different voxel sizes
world_space_mismatch	info	inputs are not co-registered in world space

Each finding carries a machine-readable detail dict (e.g. the exact translation in mm and rotation in degrees) for programmatic use.

Why trust the numbers

Every check has a unit test that builds a synthetic NIfTI designed to trigger it and asserts on the reported magnitude — the qform/sform mismatch test constructs a 10 mm offset and asserts the tool reports 10 mm. Run them:

pip install -e ".[test]"
pytest -q

Scope

nifti-qc reports problems; it does not fix them. To repair a qform/sform mismatch you decide which transform is authoritative and harmonize to it — see the LST-AI fix linked above for one approach. Keeping detection and repair separate is deliberate: the right fix depends on which upstream tool you trust.

License

MIT © Cedric Conday