Preprocess a NIfTI image and mask

Load a CT image and its binary ROI mask, then resample both to 2 mm isotropic spacing. B-spline interpolation handles image intensities; linear interpolation followed by a 0.5 threshold keeps the mask binary. Replace the paths and choose the settings for your analysis protocol; see Resampling guidelines.

from zrad.image import Image
from zrad.preprocessing import ImageResampler, MaskResampler, RoiData

# Load the mask on the image grid so their physical geometry matches.
image = Image.from_nifti("path/to/phantom.nii.gz")
mask = Image.from_nifti_mask("path/to/mask.nii.gz", reference=image)
roi = RoiData(image=image, morphological_mask=mask)

# Spacing is in millimetres and (x, y, z) order.
resolution = (2.0, 2.0, 2.0)
roi = ImageResampler(resolution, method="bspline").apply(roi)
roi = MaskResampler(
    resolution, method="linear", partial_volume_threshold=0.5
).apply(roi)

# Confirm that the resampled image and mask share a voxel grid.
assert tuple(roi.image.shape) == tuple(roi.morphological_mask.shape)
assert tuple(roi.image.spacing) == tuple(roi.morphological_mask.spacing)

# Z-Rad creates the output folder when saving these files.
roi.image.save_as_nifti("output/image.nii.gz")
roi.morphological_mask.save_as_nifti("output/mask.nii.gz")
print(f"Spacing (x, y, z): {tuple(roi.image.spacing)} mm")

The two files in output/ are ready for subsequent ROI preparation and feature extraction; see Python image workflows.