Mathias Goncalves
Mathias Goncalves
Overall this looks reasonable. Some issues with the boilerplate, though:  FSL reference shows a Python object reference, and the anatomical header has been wrapped into the SDC section. _Originally...
Addresses #24 This PR adds docker-compose file which is made up of: * a DICOM receiver * an automated `heudiconv` trigger on receiver output directory
Imports the patched FSSource interface from fMRIPrep
To not forget about these regression tests https://github.com/nipreps/niworkflows/commit/f2442f22bc43abf671819e8df295c5b67a4ced46
When working with `RegridToZooms` (aka `resample_by_spacing`), I was going from even -> odd slice lengths when doubling the zooms of an image. After a little digging, it seems to me...
Since `1.4.2`, we are now pinning many packages to a compatible release during our python install https://github.com/nipreps/niworkflows/blob/171bf7c0d1850a96c4594b4defb44086e8f838fd/setup.cfg#L27-L45 I think this is a bit too strict, and is causing some problems...
https://github.com/nipreps/niworkflows/blob/3120884336dd9c3b6211eeb538ed5f2d1085ac78/niworkflows/cli/boldref.py#L51 specifically the generated mask: - `/enhance_and_skullstrip_bold_wf/combine_masks/ref_bold_corrected_brain_mask_maths.nii.gz` and likely the report: - `/mask_reportlet/report.svg`
taking improvements from: - https://github.com/nipreps/nirodents/blob/master/nirodents/workflows/brainextraction.py - https://github.com/nipreps/nibabies/blob/master/nibabies/workflows/brain_extraction.py
I believe here's where you sample the subcortical structures from the BOLD data. Now that we have learned so much from nitransforms, you can do this step without the resampling...
This is a supplemental PR to https://github.com/nipreps/nibabies/pull/360 - since nibabies also relies on smriprep, but may be processing in T2w space, this PR generalize many of the workflows/nodes/fields to account...