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RandomMotion

MRI k-space motion artifacts

RandomMotion

Bases: RandomTransform, IntensityTransform, FourierTransform

Add random MRI motion artifact.

Magnetic resonance images suffer from motion artifacts when the subject moves during image acquisition. This transform follows Shaw et al., 2019 to simulate motion artifacts for data augmentation.

Parameters:

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

Tuple \((a, b)\) defining the rotation range in degrees of the simulated movements. The rotation angles around each axis are \((\theta_1, \theta_2, \theta_3)\), where \(\theta_i \sim \mathcal{U}(a, b)\). If only one value \(d\) is provided, \(\theta_i \sim \mathcal{U}(-d, d)\). Larger values generate more distorted images.

 10
translation float | tuple[float, float]

Tuple \((a, b)\) defining the translation in mm of the simulated movements. The translations along each axis are \((t_1, t_2, t_3)\), where \(t_i \sim \mathcal{U}(a, b)\). If only one value \(t\) is provided, \(t_i \sim \mathcal{U}(-t, t)\). Larger values generate more distorted images.

 10
num_transforms int

Number of simulated movements. Larger values generate more distorted images.

 2
image_interpolation str

See Interpolation.

 'linear'
**kwargs

See Transform for additional keyword arguments.

 {}
Warning

Large numbers of movements lead to longer execution times for 3D images.

__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.