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from typing import Tuple, Optional, Union, List |
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import torch |
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import numpy as np |
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from ....torchio import DATA |
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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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Discrete "ghost" artifacts may occur along the phase-encode direction |
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whenever the position or signal intensity of imaged structures within the |
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field-of-view vary or move in a regular (periodic) fashion. Pulsatile flow |
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of blood or CSF, cardiac motion, and respiratory motion are the most |
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important patient-related causes of ghost artifacts in clinical MR imaging |
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(from `mriquestions.com <http://mriquestions.com/why-discrete-ghosts.html>`_). |
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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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If only one value :math:`d` is provided, |
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:math:`n \sim \mathcal{U}(0, d) \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. Anatomical labels may also be used (see |
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:py:class:`~torchio.transforms.augmentation.RandomFlip`). |
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intensity: Positive number representing the artifact strength |
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:math:`s` with respect to the maximum of the :math:`k`-space. |
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If ``0``, the ghosts will not be visible. If a tuple |
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:math:`(a, b)` is provided then :math:`s \sim \mathcal{U}(a, b)`. |
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If only one value :math:`d` is provided, |
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:math:`s \sim \mathcal{U}(0, d)`. |
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restore: Number between ``0`` and ``1`` indicating how much of the |
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:math:`k`-space center should be restored after removing the planes |
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that generate the artifact. |
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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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keys: See :py:class:`~torchio.transforms.Transform`. |
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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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.. warning:: Note that height and width of 2D images correspond to axes |
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``1`` and ``2`` respectively, as TorchIO images are generally considered |
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to have 3 spatial dimensions. |
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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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restore: float = 0.02, |
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p: float = 1, |
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seed: Optional[int] = None, |
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keys: Optional[List[str]] = None, |
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): |
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super().__init__(p=p, seed=seed, keys=keys) |
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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 not isinstance(axis, str) and 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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self.num_ghosts_range = self.parse_range( |
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num_ghosts, 'num_ghosts', min_constraint=0, type_constraint=int) |
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self.intensity_range = self.parse_range( |
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intensity, 'intensity_range', min_constraint=0) |
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self.restore = self.parse_restore(restore) |
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@staticmethod |
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def parse_restore(restore): |
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if not isinstance(restore, float): |
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raise TypeError(f'Restore must be a float, not {restore}') |
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if not 0 <= restore <= 1: |
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message = ( |
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f'Restore must be a number between 0 and 1, not {restore}') |
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raise ValueError(message) |
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return restore |
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def apply_transform(self, sample: Subject) -> dict: |
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random_parameters_images_dict = {} |
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axes_string = False |
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if any(isinstance(n, str) for n in self.axes): |
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sample.check_consistent_orientation() |
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axes_string = True |
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for image_name, image in sample.get_images_dict().items(): |
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transformed_tensors = [] |
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is_2d = image.is_2d() |
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axes = [a for a in self.axes if a != 0] if is_2d else self.axes |
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for channel_idx, tensor in enumerate(image[DATA]): |
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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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key = f'{image_name}_channel_{channel_idx}' |
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random_parameters_images_dict[key] = random_parameters_dict |
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transformed_tensor = self.add_artifact( |
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tensor, |
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num_ghosts_param, |
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axis_param, |
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intensity_param, |
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self.restore, |
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) |
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transformed_tensors.append(transformed_tensor) |
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image[DATA] = torch.stack(transformed_tensors) |
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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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def add_artifact( |
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self, |
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tensor: torch.Tensor, |
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num_ghosts: int, |
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axis: int, |
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intensity: float, |
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restore_center: float, |
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): |
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if not num_ghosts or not intensity: |
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return tensor |
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array = tensor.numpy() |
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spectrum = self.fourier_transform(array) |
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shape = np.array(array.shape) |
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ri, rj, rk = np.round(restore_center * shape).astype(np.uint16) |
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mi, mj, mk = np.array(array.shape) // 2 |
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# Variable "planes" is the part of the spectrum that will be modified |
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if axis == 0: |
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planes = spectrum[::num_ghosts, :, :] |
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restore = spectrum[mi, :, :].copy() |
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elif axis == 1: |
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planes = spectrum[:, ::num_ghosts, :] |
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restore = spectrum[:, mj, :].copy() |
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elif axis == 2: |
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planes = spectrum[:, :, ::num_ghosts] |
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restore = spectrum[:, :, mk].copy() |
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# Multiply by 0 if intensity is 1 |
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planes *= 1 - intensity |
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# Restore the center of k-space to avoid extreme artifacts |
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if axis == 0: |
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spectrum[mi, :, :] = restore |
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elif axis == 1: |
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spectrum[:, mj, :] = restore |
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elif axis == 2: |
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spectrum[:, :, mk] = restore |
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array_ghosts = self.inv_fourier_transform(spectrum) |
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array_ghosts = np.real(array_ghosts) |
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return torch.from_numpy(array_ghosts) |
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fepegar /
torchio
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