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After smoothing, the image is divided by the smoothed mask to correct… #321

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merged 2 commits into from
Sep 28, 2023
Merged

After smoothing, the image is divided by the smoothed mask to correct… #321

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e281458
After smoothing, the image is divided by the smoothed mask to correct…
Gab-D-G Sep 28, 2023
a9b06de
smoothing takes the mask as input
Gab-D-G Sep 28, 2023
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3 changes: 2 additions & 1 deletion rabies/analysis_pkg/diagnosis_pkg/analysis_QC.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,10 +57,11 @@ def get_maps(prior, prior_list, corr_variable, mask_file, smoothing=False, non_p
maps.append(corr_map)

if smoothing:
mask_img = sitk.ReadImage(mask_file)
import nibabel as nb
affine = nb.load(mask_file).affine[:3,:3]
for i in range(len(maps)):
maps[i] = sitk.GetArrayFromImage(smooth_image(recover_3D(mask_file,maps[i]), affine, 0.3))[volume_indices]
maps[i] = sitk.GetArrayFromImage(smooth_image(recover_3D(mask_file,maps[i]), affine, 0.3, mask_img))[volume_indices]

return maps

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3 changes: 2 additions & 1 deletion rabies/confound_correction_pkg/confound_correction.py
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Expand Up @@ -490,7 +490,8 @@ def _run_interface(self, runtime):
'''
import nibabel as nb
affine = nb.load(bold_file).affine[:3,:3] # still not sure how to match nibabel's affine reliably
timeseries_img = smooth_image(timeseries_img, affine, cr_opts.smoothing_filter)
mask_img = sitk.ReadImage(brain_mask_file, sitk.sitkFloat32)
timeseries_img = smooth_image(timeseries_img, affine, cr_opts.smoothing_filter, mask_img)

cleaned_path = cr_out+'/'+filename_split[0]+'_cleaned.nii.gz'
sitk.WriteImage(timeseries_img, cleaned_path)
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18 changes: 17 additions & 1 deletion rabies/confound_correction_pkg/utils.py
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Expand Up @@ -447,7 +447,7 @@ def phase_randomized_regressors(confounds_array, frame_mask, TR):
return randomized_confounds_array


def smooth_image(img, affine, fwhm):
def smooth_image(img, affine, fwhm, mask_img):
# apply nilearn's Gaussian smoothing on a SITK image
from nilearn.image.image import _smooth_array
from rabies.utils import copyInfo_4DImage, copyInfo_3DImage
Expand All @@ -466,12 +466,28 @@ def smooth_image(img, affine, fwhm):
array_3d = sitk.GetArrayFromImage(img)
arr = array_3d.transpose(2,1,0) # re-orient the array to match the affine
smoothed_arr = _smooth_array(arr, affine, fwhm=fwhm, ensure_finite=True, copy=True)

# smoothing creates leakage around mask boundaries
# correct for edge effects by dividing by the smoothed mask like FSL https://johnmuschelli.com/fslr/reference/fslsmooth.html
mask_arr = sitk.GetArrayFromImage(mask_img).transpose(2,1,0) # re-orient the array to match the affine
smoothed_mask = _smooth_array(mask_arr, affine, fwhm=fwhm, ensure_finite=True, copy=True)
smoothed_mask = smoothed_mask.transpose(2,1,0) # recover SITK orientation

# recover SITK img
if dim==4:
smoothed_arr = smoothed_arr.transpose(3,2,1,0)
smoothed_arr /= smoothed_mask # correct for edge effect
smoothed_arr *= sitk.GetArrayFromImage(mask_img) # re-apply mask
smoothed_arr[np.isnan(smoothed_arr)] = 0

smoothed_img = copyInfo_4DImage(sitk.GetImageFromArray(
smoothed_arr, isVector=False), img, img)
elif dim==3:
smoothed_arr = smoothed_arr.transpose(2,1,0)
smoothed_arr /= smoothed_mask # correct for edge effect
smoothed_arr *= sitk.GetArrayFromImage(mask_img) # re-apply mask
smoothed_arr[np.isnan(smoothed_arr)] = 0

smoothed_img = copyInfo_3DImage(sitk.GetImageFromArray(
smoothed_arr, isVector=False), img)

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