import copy
from ... import SpatialTransform
from ....data.subject import Subject
[docs]
class CopyAffine(SpatialTransform):
"""Copy the spatial metadata from a reference image in the subject.
Small unexpected differences in spatial metadata across different images
of a subject can arise due to rounding errors while converting formats.
If the ``shape`` and ``orientation`` of the images are the same and their
``affine`` attributes are different but very similar, this transform can be
used to avoid errors during safety checks in other transforms and samplers.
Args:
target: Name of the image within the subject whose affine matrix will
be used.
Example:
>>> import torch
>>> import torchio as tio
>>> import numpy as np
>>> np.random.seed(0)
>>> affine = np.diag((*(np.random.rand(3) + 0.5), 1))
>>> t1 = tio.ScalarImage(tensor=torch.rand(1, 100, 100, 100), affine=affine)
>>> # Let's simulate a loss of precision
>>> # (caused for example by NIfTI storing spatial metadata in single precision)
>>> bad_affine = affine.astype(np.float16)
>>> t2 = tio.ScalarImage(tensor=torch.rand(1, 100, 100, 100), affine=bad_affine)
>>> subject = tio.Subject(t1=t1, t2=t2)
>>> resample = tio.Resample(0.5)
>>> resample(subject).shape # error as images are in different spaces
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/fernando/git/torchio/torchio/data/subject.py", line 101, in shape
self.check_consistent_attribute('shape')
File "/Users/fernando/git/torchio/torchio/data/subject.py", line 229, in check_consistent_attribute
raise RuntimeError(message)
RuntimeError: More than one shape found in subject images:
{'t1': (1, 210, 244, 221), 't2': (1, 210, 243, 221)}
>>> transform = tio.CopyAffine('t1')
>>> fixed = transform(subject)
>>> resample(fixed).shape
(1, 210, 244, 221)
.. warning:: This transform should be used with caution. Modifying the
spatial metadata of an image manually can lead to incorrect processing
of the position of anatomical structures. For example, a machine
learning algorithm might incorrectly predict that a lesion on the right
lung is on the left lung.
.. note:: For more information, see some related discussions on GitHub:
* https://github.com/fepegar/torchio/issues/354
* https://github.com/fepegar/torchio/discussions/489
* https://github.com/fepegar/torchio/pull/584
* https://github.com/fepegar/torchio/issues/430
* https://github.com/fepegar/torchio/issues/382
* https://github.com/fepegar/torchio/pull/592
""" # noqa: B950
def __init__(self, target: str, **kwargs):
super().__init__(**kwargs)
if not isinstance(target, str):
message = f'The target must be a string, but "{type(target)}" was found'
raise ValueError(message)
self.target = target
self.args_names = ['target']
def apply_transform(self, subject: Subject) -> Subject:
if self.target not in subject:
message = f'Target image "{self.target}" not found in subject'
raise RuntimeError(message)
reference = subject[self.target]
affine = copy.deepcopy(reference.affine)
for image in self.get_images(subject):
if image is reference:
continue
# We load the image to avoid complications
# https://github.com/fepegar/torchio/issues/1071#issuecomment-1511814720
image.load()
image.affine = affine
return subject