MRIQC for LF-MRI

1

Hello,

I want to use MRIQC for low-field MRI data (T1W and T2W) that has been BIDS-ified. I am encountering this error. Would you please guide me on how to fix it?

Node: mriqc_wf.anatMRIQC.ComputeIQMs.measures
Working directory: /…/ComputeIQMs/…bids…sub-042…ses-t0…anat…sub-042_ses-t0_acq-axi_T1w.nii.gz/measures

Node inputs:

air_msk =
artifact_msk =
head_msk =
human = True
in_bias =
in_file =
in_fwhm =
in_noinu =
in_pvms =
in_segm =
in_tpms =
mni_tpms =
rot_msk =

Traceback (most recent call last):
File “/opt/conda/lib/python3.9/site-packages/mriqc/engine/plugin.py”, line 60, in run_node
result[“result”] = node.run(updatehash=updatehash)
File “/opt/conda/lib/python3.9/site-packages/nipype/pipeline/engine/nodes.py”, line 527, in run
result = self._run_interface(execute=True)
File “/opt/conda/lib/python3.9/site-packages/nipype/pipeline/engine/nodes.py”, line 645, in _run_interface
return self._run_command(execute)
File “/opt/conda/lib/python3.9/site-packages/nipype/pipeline/engine/nodes.py”, line 771, in _run_command
raise NodeExecutionError(msg)
nipype.pipeline.engine.nodes.NodeExecutionError: Exception raised while executing Node measures.

Traceback:
Traceback (most recent call last):
File “/opt/conda/lib/python3.9/site-packages/nipype/interfaces/base/core.py”, line 397, in run
runtime = self._run_interface(runtime)
File “/opt/conda/lib/python3.9/site-packages/mriqc/interfaces/anatomical.py”, line 114, in _run_interface
raise RuntimeError(
RuntimeError: Input inhomogeneity-corrected data seem empty. MRIQC failed to process this dataset.

2

This is happening with our lab’s LF data, also. It’s not every scan, but a sizeable number of them error out with this message.

Edit to add: This issue is affecting our lab’s MRIQC processing of 3T scans as well, because it causes mriqc to fully crash for the individual it’s running on. We’re having to do some very hacky things to make MRIQC ignore the lowfield scans so we can at least get a result for the others. The ability to filter by the acq- tag would be so helpful in cases like this.