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Problem summary

Describe the problem
TorchIO spatial transforms (e.g., Crop, Pad, CropOrPad) reconstruct images internally using type(image)(tensor=..., affine=..., type=..., path=...).
If a user-defined subclass of torchio.Image has a different __init__ signature (e.g., requires extra arguments like history), these transforms raise errors and fail.

This issue affects all transforms that reconstruct images, not just Crop.
It prevents users from extending TorchIO Image to store metadata or other extra attributes inside images.

Proposed solution
Introduce an optional hook or method (new_like) that allows subclasses to return a new instance when transforms need to copy/crop/resize the image. Transforms would call this hook if it exists, otherwise fall back to the standard constructor.

Code for reproduction

python
import torchio as tio
import torch

class HistoryScalarImage(tio.ScalarImage):
    def __init__(self, tensor, affine, history, **kwargs):
        super().__init__(tensor=tensor, affine=affine, **kwargs)
        self.history = history

img = HistoryScalarImage(torch.rand(1,10,10,10), affine=torch.eye(4), history=[])
subject = tio.Subject(image=img)
transform = tio.Crop(cropping=2)

Actual outcome

transform(subject) # raises TypeError

Error messages

TypeError

Expected outcome

Transforms should either:

1. Support arbitrary Image subclasses by providing a clone / factory hook (new_like() or similar), or
2. Clearly document that only vanilla TorchIO Image subclasses are supported.

System info

Platform:   Linux-6.8.0-85-generic-x86_64-with-glibc2.39
TorchIO:    0.21.0
PyTorch:    2.1.2
SimpleITK:  2.5.2 (ITK 5.4)
NumPy:      1.26.4
Python:     3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0]