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ENH: Add smooth input to RegridToZooms #549

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Merged
merged 5 commits into from
Aug 13, 2020
Merged

ENH: Add smooth input to RegridToZooms #549

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15d1aa0
ENH: Add ``smooth`` input to ``RegridToZooms``
oesteban Aug 12, 2020
cf6d6a0
Update niworkflows/interfaces/images.py
oesteban Aug 12, 2020
f803bdd
fix: address code review comments
oesteban Aug 13, 2020
5a2fdd1
enh: eliminate casting of smoothed data
oesteban Aug 13, 2020
f52c7fb
Update niworkflows/utils/images.py
oesteban Aug 13, 2020
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8 changes: 8 additions & 0 deletions niworkflows/interfaces/images.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,13 @@ class _RegridToZoomsInputSpec(BaseInterfaceInputSpec):
usedefault=True,
desc="clip the data array within the original image's range",
)
smooth = traits.Either(
traits.Bool(),
traits.Float(),
default=False,
usedefault=True,
desc="apply gaussian smoothing before resampling"
)


class _RegridToZoomsOutputSpec(TraitedSpec):
Expand All @@ -65,6 +72,7 @@ def _run_interface(self, runtime):
self.inputs.zooms,
order=self.inputs.order,
clip=self.inputs.clip,
smooth=self.inputs.smooth,
).to_filename(self._results["out_file"])
return runtime

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13 changes: 9 additions & 4 deletions niworkflows/utils/images.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,7 +148,7 @@ def dseg_label(in_seg, label, newpath=None):
return out_file


def resample_by_spacing(in_file, zooms, order=3, clip=True):
def resample_by_spacing(in_file, zooms, order=3, clip=True, smooth=False):
"""Regrid the input image to match the new zooms."""
from pathlib import Path
import numpy as np
Expand All @@ -163,8 +163,6 @@ def resample_by_spacing(in_file, zooms, order=3, clip=True):
qform, qcode = in_file.get_qform(coded=True)

hdr = in_file.header.copy()
dtype = hdr.get_data_dtype()
data = np.asanyarray(in_file.dataobj)
zooms = np.array(zooms)

# Calculate the factors to normalize voxel size to the specific zooms
Expand Down Expand Up @@ -195,11 +193,18 @@ def resample_by_spacing(in_file, zooms, order=3, clip=True):
new_card.dot(np.vstack((new_grid, np.ones((1, new_grid.shape[1])))))
)

if smooth:
from scipy.ndimage import gaussian_filter
if smooth is True:
smooth = np.maximum(0, (pre_zooms / zooms - 1) / 2)
data = gaussian_filter(in_file.get_fdata(), smooth)
else:
data = np.asarray(in_file.dataobj)

# Resample data in the new grid
resampled = map_coordinates(
data,
ijk[:3, :],
output=dtype,
order=order,
mode="constant",
cval=0,
Expand Down