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import warnings |
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from typing import Tuple, Optional, Union |
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import torch |
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import numpy as np |
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import SimpleITK as sitk |
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from ....torchio import DATA, AFFINE |
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from ....data.subject import Subject |
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from .. import RandomTransform |
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class RandomGhosting(RandomTransform): |
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r"""Add random MRI ghosting artifact. |
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Args: |
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num_ghosts: Number of 'ghosts' :math:`n` in the image. |
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If :py:attr:`num_ghosts` is a tuple :math:`(a, b)`, then |
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:math:`n \sim \mathcal{U}(a, b) \cap \mathbb{N}`. |
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axes: Axis along which the ghosts will be created. If |
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:py:attr:`axes` is a tuple, the axis will be randomly chosen |
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from the passed values. |
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intensity: Number between 0 and 1 representing the artifact strength |
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:math:`s`. If ``0``, the ghosts will not be visible. If a tuple |
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:math:`(a, b)`, is provided then |
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:math:`s \sim \mathcal{U}(a, b)`. |
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p: Probability that this transform will be applied. |
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seed: See :py:class:`~torchio.transforms.augmentation.RandomTransform`. |
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.. note:: The execution time of this transform does not depend on the |
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number of ghosts. |
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""" |
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def __init__( |
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self, |
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num_ghosts: Union[int, Tuple[int, int]] = (4, 10), |
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axes: Union[int, Tuple[int, ...]] = (0, 1, 2), |
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intensity: Union[float, Tuple[float, float]] = (0.5, 1), |
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p: float = 1, |
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seed: Optional[int] = None, |
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): |
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super().__init__(p=p, seed=seed) |
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if not isinstance(axes, tuple): |
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try: |
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axes = tuple(axes) |
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except TypeError: |
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axes = (axes,) |
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for axis in axes: |
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if axis not in (0, 1, 2): |
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raise ValueError(f'Axes must be in (0, 1, 2), not "{axes}"') |
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self.axes = axes |
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if isinstance(num_ghosts, int): |
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self.num_ghosts_range = num_ghosts, num_ghosts |
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elif isinstance(num_ghosts, tuple) and len(num_ghosts) == 2: |
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self.num_ghosts_range = num_ghosts |
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self.intensity_range = self.parse_range(intensity, 'intensity') |
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for n in self.intensity_range: |
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if not 0 <= n <= 1: |
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message = ( |
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'Intensity must be a number between 0 and 1, not {n}') |
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raise ValueError(message) |
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def apply_transform(self, sample: Subject) -> dict: |
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random_parameters_images_dict = {} |
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for image_name, image_dict in sample.get_images_dict().items(): |
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data = image_dict[DATA] |
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is_2d = data.shape[-3] == 1 |
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axes = [a for a in self.axes if a != 0] if is_2d else self.axes |
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params = self.get_params( |
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self.num_ghosts_range, |
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axes, |
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self.intensity_range, |
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) |
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num_ghosts_param, axis_param, intensity_param = params |
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random_parameters_dict = { |
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'axis': axis_param, |
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'num_ghosts': num_ghosts_param, |
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'intensity': intensity_param, |
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} |
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random_parameters_images_dict[image_name] = random_parameters_dict |
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if (data[0] < -0.1).any(): |
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# I use -0.1 instead of 0 because Python was warning me when |
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# a value in a voxel was -7.191084e-35 |
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# There must be a better way of solving this |
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message = ( |
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f'Image "{image_name}" from "{image_dict["stem"]}"' |
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' has negative values.' |
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' Results can be unexpected because the transformed sample' |
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' is computed as the absolute values' |
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' of an inverse Fourier transform' |
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) |
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warnings.warn(message) |
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image = self.nib_to_sitk( |
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data[0], |
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image_dict[AFFINE], |
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) |
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data = self.add_artifact( |
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image, |
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num_ghosts_param, |
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axis_param, |
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intensity_param, |
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) |
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# Add channels dimension |
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data = data[np.newaxis, ...] |
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image_dict[DATA] = torch.from_numpy(data) |
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sample.add_transform(self, random_parameters_images_dict) |
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return sample |
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@staticmethod |
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def get_params( |
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num_ghosts_range: Tuple[int, int], |
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axes: Tuple[int, ...], |
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intensity_range: Tuple[float, float], |
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) -> Tuple: |
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ng_min, ng_max = num_ghosts_range |
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num_ghosts = torch.randint(ng_min, ng_max + 1, (1,)).item() |
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axis = axes[torch.randint(0, len(axes), (1,))] |
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intensity = torch.FloatTensor(1).uniform_(*intensity_range).item() |
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return num_ghosts, axis, intensity |
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@staticmethod |
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def get_axis_and_size(axis, array): |
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if axis == 1: |
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axis = 0 |
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size = array.shape[0] |
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elif axis == 0: |
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axis = 1 |
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size = array.shape[1] |
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elif axis == 2: # we will also traverse in sagittal (if RAS) |
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size = array.shape[0] |
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else: |
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raise RuntimeError(f'Axis "{axis}" is not valid') |
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return axis, size |
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@staticmethod |
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def get_slice(axis, array, slice_idx): |
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# Comments apply if RAS |
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if axis == 0: # sagittal (columns) - artifact AP |
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image_slice = array[slice_idx, ...] |
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elif axis == 1: # coronal (columns) - artifact LR |
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image_slice = array[:, slice_idx, :] |
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elif axis == 2: # sagittal (rows) - artifact IS |
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image_slice = array[slice_idx, ...].T |
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else: |
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raise RuntimeError(f'Axis "{axis}" is not valid') |
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return image_slice |
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def add_artifact( |
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self, |
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image: sitk.Image, |
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num_ghosts: int, |
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axis: int, |
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intensity: float, |
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): |
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array = sitk.GetArrayFromImage(image).transpose() |
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# Leave first 5% of frequencies untouched. If the image is in RAS |
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# orientation, this helps applying the ghosting in the desired axis |
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# intuitively |
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# [Why? I forgot] |
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percentage_to_avoid = 0.05 |
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axis, size = self.get_axis_and_size(axis, array) |
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for slice_idx in range(size): |
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image_slice = self.get_slice(axis, array, slice_idx) |
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spectrum = self.fourier_transform(image_slice) |
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for row_idx, row in enumerate(spectrum): |
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if row_idx % num_ghosts: |
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continue |
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progress = row_idx / array.shape[0] |
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if np.abs(progress - 0.5) < percentage_to_avoid / 2: |
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continue |
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row *= 1 - intensity |
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image_slice *= 0 |
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image_slice += self.inv_fourier_transform(spectrum) |
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return array |
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fepegar /
torchio
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