Spacing transform changes physical extent of images

Describe the bug After applying the Spacing transform, the physical extent that an image covers is changing.

To Reproduce

import numpy as np
from monai.data.meta_tensor import MetaTensor
from monai.transforms import Spacing

pixdim_new = [2.0, 1.0, 0.2]
pixdim_old = [1.0, 1.0, 1.0]
affine = np.diag(pixdim_old + [1])
data = np.random.randn(1, 128, 128, 32)

img = MetaTensor(data, affine=affine).to("cpu")
res: MetaTensor = Spacing(pixdim=pixdim_new)(img)

np.testing.assert_allclose(
    np.array(img.shape[1:]) * pixdim_old,
    np.array(res.shape[1:]) * pixdim_new
)

Expected behavior Resampled image covers same physical space as original volume, in particular if it is possible because the resampling factors are integers. In fact, during upsampling (last dimension), the physical extent is too small. I might understand the behavior now if align_corners is true, in the sense that the centers of the corner pixels define the extent and not the borders. From align_corners documentation: “if set to False, they are instead considered as referring to the corner points of the input’s corner pixels, making the sampling more resolution agnostic”

Screenshots none

Environment

MONAI version: 0.9.1
Numpy version: 1.21.6
Pytorch version: 1.12.1+cpu
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 356d2d2f41b473f588899d705bbc682308cee52c

Additional context Add any other context about the problem here.

[wyli]

you are right @yanlend, I came across scipy zoom’s grid_mode parameter, that’s a better name and it’s not related to the align corners parameter… https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.zoom.html I’ll spend more time on this and probably update the docstring to match what’s implemented.

[wyli]

I add a scale_extent boolean parameter to choose the resampling coordinate conventions:

scale_extent: whether the scale is computed based on the spacing or the full extent of voxels, for example, for
    a factor of 0.5 scaling:
    option 1, "o" represents a voxel, scaling the distance between voxels::
        o--o--o
        o-----o
    option 2, each voxel has a physical extent, scaling the full voxel extent::
        | voxel 1 | voxel 2 | voxel 3 | voxel 4 |
        |      voxel 1      |      voxel 2      |
    Option 1 may reduce the number of locations that requiring interpolation. Option 2 is more resolution
    agnostic, that is, resampling coordinates depend on the scaling factor, not on the number of voxels.
    Default is False, using option 1 to compute the shape and offset.

in this case when the affine is computed using option 2, the align_corners=True should be used for SpatialResampling though, as compute_shape_offset option 2 already implements the shift/scaling. Using option 2 + align_corners=False would incorrectly compensate the coordinates twice.