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feat: Parameterize epi_reference_wf to better support rodents #971

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feat: Parameterize epi_reference_wf to better support rodents #971

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feat: Parameterize epi_reference_wf to better support rodents
effigies Sep 17, 2025
7e8483d
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 17, 2025
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30 changes: 27 additions & 3 deletions niworkflows/workflows/epi/refmap.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,9 @@ def init_epi_reference_wf(
omp_nthreads,
auto_bold_nss=False,
name='epi_reference_wf',
n4_iterations=(50,) * 5,
n4_shrink_factor=4,
calculate_bspline_grid=False,
):
"""
Build a workflow that generates a reference map from a set of EPI images.
Expand Down Expand Up @@ -121,7 +124,7 @@ def init_epi_reference_wf(
from ...interfaces.header import ValidateImage
from ...interfaces.images import RobustAverage
from ...interfaces.nibabel import IntensityClip
from ...utils.connections import listify
from ...utils.connections import listify, pop_file

wf = Workflow(name=name)

Expand Down Expand Up @@ -153,9 +156,9 @@ def init_epi_reference_wf(
N4BiasFieldCorrection(
dimension=3,
copy_header=True,
n_iterations=[50] * 5,
n_iterations=list(n4_iterations),
convergence_threshold=1e-7,
shrink_factor=4,
shrink_factor=n4_shrink_factor,
),
n_procs=omp_nthreads,
name='n4_avgs',
Expand Down Expand Up @@ -220,6 +223,13 @@ def _set_threads(in_list, maximum):
else:
wf.connect(inputnode, 't_masks', per_run_avgs, 't_mask')

if calculate_bspline_grid:
bspline_grid = pe.Node(niu.Function(function=_bspline_grid), name='bspline_grid')
wf.connect([
(inputnode, bspline_grid, [(('in_files', pop_file), 'in_file')]),
(bspline_grid, n4_avgs, [('out', 'args')]),
]) # fmt:skip

return wf


Expand Down Expand Up @@ -256,3 +266,17 @@ def _post_merge(in_file, in_xfms):
img = nb.load(in_file)
nb.Nifti1Image(img.dataobj, img.affine, None).to_filename(out_file)
return _advanced_clip(out_file, p_min=0.0, p_max=100.0)


def _bspline_grid(in_file):
import math

import nibabel as nb
import numpy as np

img = nb.load(in_file)
zooms = img.header.get_zooms()[:3]
extent = (np.array(img.shape[:3]) - 1) * zooms
# get mesh resolution ratio
retval = [f'{math.ceil(i / extent[np.argmin(extent)])}' for i in extent]
return f'-b [{"x".join(retval)}]'
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