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RandomBiasField

MRI bias field artifact

RandomBiasField

Bases: RandomTransform, IntensityTransform

Add random MRI bias field artifact.

MRI magnetic field inhomogeneity creates intensity variations of very low frequency across the whole image.

The bias field is modeled as a linear combination of polynomial basis functions, as in K. Van Leemput et al., 1999, Automated model-based tissue classification of MR images of the brain.

It was implemented in NiftyNet by Carole Sudre and used in Sudre et al., 2017, Longitudinal segmentation of age-related white matter hyperintensities .

Parameters:

Name Type Description Default
coefficients float | tuple[float, float]

Maximum magnitude \(n\) of polynomial coefficients. If a tuple \((a, b)\) is specified, then \(n \sim \mathcal{U}(a, b)\).

 0.5
order int

Order of the basis polynomial functions.

 3
**kwargs

See Transform for additional keyword arguments.

 {}

__call__(data) 

__call__(data: Subject) -> Subject

__call__(data: ImageT) -> ImageT

__call__(data: torch.Tensor) -> torch.Tensor

__call__(data: np.ndarray) -> np.ndarray

__call__(data: sitk.Image) -> sitk.Image

__call__(data: dict[str, object]) -> dict[str, object]

__call__(data: nib.Nifti1Image) -> nib.Nifti1Image

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data TypeTransformInput

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.

arguments_are_dict()

Check if main arguments are dict.

Return True if the type of all attributes specified in the args_names have dict type.