Skip to content

RandomSpike

MRI k-space spike artifacts

RandomSpike

Bases: RandomTransform, IntensityTransform, FourierTransform

Add random MRI spike artifacts.

Also known as Herringbone artifact , crisscross artifact or corduroy artifact, it creates stripes in different directions in image space due to spikes in k-space.

Parameters:

Name Type Description Default
num_spikes int | tuple[int, int]

Number of spikes \(n\) present in k-space. If a tuple \((a, b)\) is provided, then \(n \sim \mathcal{U}(a, b) \cap \mathbb{N}\). If only one value \(d\) is provided, \(n \sim \mathcal{U}(0, d) \cap \mathbb{N}\). Larger values generate more distorted images.

 (1, 1)
intensity float | tuple[float, float]

Ratio \(r\) between the spike intensity and the maximum of the spectrum. If a tuple \((a, b)\) is provided, then \(r \sim \mathcal{U}(a, b)\). If only one value \(d\) is provided, \(r \sim \mathcal{U}(-d, d)\). Larger values generate more distorted images.

 (1, 3)
**kwargs

See Transform for additional keyword arguments.

 {}
Note

The execution time of this transform does not depend on the number of spikes.

__call__(data)

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data InputType

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

get_base_args()

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

add_base_args(arguments, overwrite_on_existing=False)

Add the init args to existing arguments

validate_keys_sequence(keys, name) staticmethod

Ensure that the input is not a string but a sequence of strings.

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.