torchio.transforms.augmentation.intensity.random_ghosting.RandomGhosting.__init__() - Code Metrics - Inspection of "Add some deterministic transforms" - fepegar/torchio - Measure and Improve Code Quality continuously with Scrutinizer

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Pull Request — master (#353)

by Fernando

created 2020-11-22 12:25 UTC

RandomGhosting.init() B

↳ Parent: torchio.transforms.augmentation.intensity.random_ghosting

Complexity

Conditions

Size

Total Lines	24
Code Lines	23

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	6
eloc	23
nop	7
dl	0
loc	24
rs	8.3946
c	0
b	0
f	0

from collections import defaultdict
from typing import Tuple, Optional, Union, Sequence, Dict

import torch
import numpy as np

from ....torchio import DATA
from ....data.subject import Subject
from ... import IntensityTransform, FourierTransform
from .. import RandomTransform


class RandomGhosting(RandomTransform, IntensityTransform):
    r"""Add random MRI ghosting artifact.

    Discrete "ghost" artifacts may occur along the phase-encode direction
    whenever the position or signal intensity of imaged structures within the
    field-of-view vary or move in a regular (periodic) fashion. Pulsatile flow
    of blood or CSF, cardiac motion, and respiratory motion are the most
    important patient-related causes of ghost artifacts in clinical MR imaging
    (from `mriquestions.com <http://mriquestions.com/why-discrete-ghosts.html>`_).

    Args:
        num_ghosts: Number of 'ghosts' :math:`n` in the image.
            If :py:attr:`num_ghosts` is a tuple :math:`(a, b)`, then
            :math:`n \sim \mathcal{U}(a, b) \cap \mathbb{N}`.
            If only one value :math:`d` is provided,
            :math:`n \sim \mathcal{U}(0, d) \cap \mathbb{N}`.
        axes: Axis along which the ghosts will be created. If
            :py:attr:`axes` is a tuple, the axis will be randomly chosen
            from the passed values. Anatomical labels may also be used (see
            :py:class:`~torchio.transforms.augmentation.RandomFlip`).
        intensity: Positive number representing the artifact strength
            :math:`s` with respect to the maximum of the :math:`k`-space.
            If ``0``, the ghosts will not be visible. If a tuple
            :math:`(a, b)` is provided then :math:`s \sim \mathcal{U}(a, b)`.
            If only one value :math:`d` is provided,
            :math:`s \sim \mathcal{U}(0, d)`.
        restore: Number between ``0`` and ``1`` indicating how much of the
            :math:`k`-space center should be restored after removing the planes
            that generate the artifact.
        p: Probability that this transform will be applied.
        keys: See :py:class:`~torchio.transforms.Transform`.

    .. note:: The execution time of this transform does not depend on the
        number of ghosts.
    """
    def __init__(
            self,
            num_ghosts: Union[int, Tuple[int, int]] = (4, 10),
            axes: Union[int, Tuple[int, ...]] = (0, 1, 2),
            intensity: Union[float, Tuple[float, float]] = (0.5, 1),
            restore: float = 0.02,
            p: float = 1,
            keys: Optional[Sequence[str]] = None,
            ):
        super().__init__(p=p, keys=keys)
        if not isinstance(axes, tuple):
            try:
                axes = tuple(axes)
            except TypeError:
                axes = (axes,)
        for axis in axes:
            if not isinstance(axis, str) and axis not in (0, 1, 2):
                raise ValueError(f'Axes must be in (0, 1, 2), not "{axes}"')
        self.axes = axes
        self.num_ghosts_range = self.parse_range(
            num_ghosts, 'num_ghosts', min_constraint=0, type_constraint=int)
        self.intensity_range = self.parse_range(
            intensity, 'intensity_range', min_constraint=0)
        self.restore = _parse_restore(restore)

    def apply_transform(self, subject: Subject) -> Subject:
        arguments = defaultdict(dict)
        if any(isinstance(n, str) for n in self.axes):
            subject.check_consistent_orientation()
        for name, image in self.get_images_dict(subject).items():
            is_2d = image.is_2d()
            axes = [a for a in self.axes if a != 2] if is_2d else self.axes
            params = self.get_params(
                self.num_ghosts_range,
                axes,
                self.intensity_range,
            )
            num_ghosts_param, axis_param, intensity_param = params
            arguments['num_ghosts'][name] = num_ghosts_param
            arguments['axis'][name] = axis_param
            arguments['intensity'][name] = intensity_param
            arguments['restore'][name] = self.restore
        transform = Ghosting(**arguments)
        transformed = transform(subject)
        return transformed

    def get_params(
            self,
            num_ghosts_range: Tuple[int, int],
            axes: Tuple[int, ...],
            intensity_range: Tuple[float, float],
            ) -> Tuple:
        ng_min, ng_max = num_ghosts_range
        num_ghosts = torch.randint(ng_min, ng_max + 1, (1,)).item()
        axis = axes[torch.randint(0, len(axes), (1,))]
        intensity = self.sample_uniform(*intensity_range).item()
        return num_ghosts, axis, intensity


class Ghosting(IntensityTransform, FourierTransform):
    r"""Add MRI ghosting artifact.

    Discrete "ghost" artifacts may occur along the phase-encode direction
    whenever the position or signal intensity of imaged structures within the
    field-of-view vary or move in a regular (periodic) fashion. Pulsatile flow
    of blood or CSF, cardiac motion, and respiratory motion are the most
    important patient-related causes of ghost artifacts in clinical MR imaging
    (from `mriquestions.com <http://mriquestions.com/why-discrete-ghosts.html>`_).

    Args:
        num_ghosts: Number of 'ghosts' :math:`n` in the image.
        axes: Axis along which the ghosts will be created.
        intensity: Positive number representing the artifact strength
            :math:`s` with respect to the maximum of the :math:`k`-space.
            If ``0``, the ghosts will not be visible.
        restore: Number between ``0`` and ``1`` indicating how much of the
            :math:`k`-space center should be restored after removing the planes
            that generate the artifact.
        keys: See :py:class:`~torchio.transforms.Transform`.

    .. note:: The execution time of this transform does not depend on the
        number of ghosts.
    """
    def __init__(
            self,
            num_ghosts: Union[int, Dict[str, int]],
            axis: Union[int, Dict[str, int]],
            intensity: Union[float, Dict[str, float]],
            restore: Union[float, Dict[str, float]],
            keys: Optional[Sequence[str]] = None,
            ):
        super().__init__(keys=keys)
        self.axis = axis
        self.num_ghosts = num_ghosts
        self.intensity = intensity
        self.restore = restore
        self.args_names = 'num_ghosts', 'axis', 'intensity', 'restore'

    def apply_transform(self, subject: Subject) -> Subject:
        axis = self.axis
        num_ghosts = self.num_ghosts
        intensity = self.intensity
        restore = self.restore
        for name, image in self.get_images_dict(subject).items():
            if self.arguments_are_dict():
                axis = self.axis[name]
                num_ghosts = self.num_ghosts[name]
                intensity = self.intensity[name]
                restore = self.restore[name]
            transformed_tensors = []
            for tensor in image.data:
                transformed_tensor = self.add_artifact(
                    tensor,
                    num_ghosts,
                    axis,
                    intensity,
                    restore,
                )
                transformed_tensors.append(transformed_tensor)
            image[DATA] = torch.stack(transformed_tensors)
        return subject

    def add_artifact(
            self,
            tensor: torch.Tensor,
            num_ghosts: int,
            axis: int,
            intensity: float,
            restore_center: float,
            ):
        if not num_ghosts or not intensity:
            return tensor

        array = tensor.numpy()
        spectrum = self.fourier_transform(array)

        shape = np.array(array.shape)
        ri, rj, rk = np.round(restore_center * shape).astype(np.uint16)
        mi, mj, mk = np.array(array.shape) // 2

        # Variable "planes" is the part of the spectrum that will be modified
        if axis == 0:
            planes = spectrum[::num_ghosts, :, :]
            restore = spectrum[mi, :, :].copy()
        elif axis == 1:
            planes = spectrum[:, ::num_ghosts, :]
            restore = spectrum[:, mj, :].copy()
        elif axis == 2:
            planes = spectrum[:, :, ::num_ghosts]
            restore = spectrum[:, :, mk].copy()

        # Multiply by 0 if intensity is 1
        planes *= 1 - intensity


        # Restore the center of k-space to avoid extreme artifacts
        if axis == 0:
            spectrum[mi, :, :] = restore

        elif axis == 1:
            spectrum[:, mj, :] = restore
        elif axis == 2:
            spectrum[:, :, mk] = restore

        array_ghosts = self.inv_fourier_transform(spectrum)
        array_ghosts = np.real(array_ghosts)
        return torch.from_numpy(array_ghosts)


def _parse_restore(restore):
    if not isinstance(restore, float):
        raise TypeError(f'Restore must be a float, not {restore}')
    if not 0 <= restore <= 1:
        message = (
            f'Restore must be a number between 0 and 1, not {restore}')
        raise ValueError(message)
    return restore


1			from collections import defaultdict
2			from typing import Tuple, Optional, Union, Sequence, Dict
3
4			import torch
5			import numpy as np
6
7			from ....torchio import DATA
8			from ....data.subject import Subject
9			from ... import IntensityTransform, FourierTransform
10			from .. import RandomTransform
11
12
13			class RandomGhosting(RandomTransform, IntensityTransform):
14			r"""Add random MRI ghosting artifact.
15
16			Discrete "ghost" artifacts may occur along the phase-encode direction
17			whenever the position or signal intensity of imaged structures within the
18			field-of-view vary or move in a regular (periodic) fashion. Pulsatile flow
19			of blood or CSF, cardiac motion, and respiratory motion are the most
20			important patient-related causes of ghost artifacts in clinical MR imaging
21			(from `mriquestions.com <http://mriquestions.com/why-discrete-ghosts.html>`_).
22
23			Args:
24			num_ghosts: Number of 'ghosts' :math:`n` in the image.
25			If :py:attr:`num_ghosts` is a tuple :math:`(a, b)`, then
26			:math:`n \sim \mathcal{U}(a, b) \cap \mathbb{N}`.
27			If only one value :math:`d` is provided,
28			:math:`n \sim \mathcal{U}(0, d) \cap \mathbb{N}`.
29			axes: Axis along which the ghosts will be created. If
30			:py:attr:`axes` is a tuple, the axis will be randomly chosen
31			from the passed values. Anatomical labels may also be used (see
32			:py:class:`~torchio.transforms.augmentation.RandomFlip`).
33			intensity: Positive number representing the artifact strength
34			:math:`s` with respect to the maximum of the :math:`k`-space.
35			If ``0``, the ghosts will not be visible. If a tuple
36			:math:`(a, b)` is provided then :math:`s \sim \mathcal{U}(a, b)`.
37			If only one value :math:`d` is provided,
38			:math:`s \sim \mathcal{U}(0, d)`.
39			restore: Number between ``0`` and ``1`` indicating how much of the
40			:math:`k`-space center should be restored after removing the planes
41			that generate the artifact.
42			p: Probability that this transform will be applied.
43			keys: See :py:class:`~torchio.transforms.Transform`.
44
45			.. note:: The execution time of this transform does not depend on the
46			number of ghosts.
47			"""
48			def __init__(
49			self,
50			num_ghosts: Union[int, Tuple[int, int]] = (4, 10),
51			axes: Union[int, Tuple[int, ...]] = (0, 1, 2),
52			intensity: Union[float, Tuple[float, float]] = (0.5, 1),
53			restore: float = 0.02,
54			p: float = 1,
55			keys: Optional[Sequence[str]] = None,
56			):
57			super().__init__(p=p, keys=keys)
58			if not isinstance(axes, tuple):
59			try:
60			axes = tuple(axes)
61			except TypeError:
62			axes = (axes,)
63			for axis in axes:
64			if not isinstance(axis, str) and axis not in (0, 1, 2):
65			raise ValueError(f'Axes must be in (0, 1, 2), not "{axes}"')
66			self.axes = axes
67			self.num_ghosts_range = self.parse_range(
68			num_ghosts, 'num_ghosts', min_constraint=0, type_constraint=int)
69			self.intensity_range = self.parse_range(
70			intensity, 'intensity_range', min_constraint=0)
71			self.restore = _parse_restore(restore)
72
73			def apply_transform(self, subject: Subject) -> Subject:
74			arguments = defaultdict(dict)
75			if any(isinstance(n, str) for n in self.axes):
76			subject.check_consistent_orientation()
77			for name, image in self.get_images_dict(subject).items():
78			is_2d = image.is_2d()
79			axes = [a for a in self.axes if a != 2] if is_2d else self.axes
80			params = self.get_params(
81			self.num_ghosts_range,
82			axes,
83			self.intensity_range,
84			)
85			num_ghosts_param, axis_param, intensity_param = params
86			arguments['num_ghosts'][name] = num_ghosts_param
87			arguments['axis'][name] = axis_param
88			arguments['intensity'][name] = intensity_param
89			arguments['restore'][name] = self.restore
90			transform = Ghosting(**arguments)
91			transformed = transform(subject)
92			return transformed
93
94			def get_params(
95			self,
96			num_ghosts_range: Tuple[int, int],
97			axes: Tuple[int, ...],
98			intensity_range: Tuple[float, float],
99			) -> Tuple:
100			ng_min, ng_max = num_ghosts_range
101			num_ghosts = torch.randint(ng_min, ng_max + 1, (1,)).item()
102			axis = axes[torch.randint(0, len(axes), (1,))]
103			intensity = self.sample_uniform(*intensity_range).item()
104			return num_ghosts, axis, intensity
105
106
107			class Ghosting(IntensityTransform, FourierTransform):
108			r"""Add MRI ghosting artifact.
109
110			Discrete "ghost" artifacts may occur along the phase-encode direction
111			whenever the position or signal intensity of imaged structures within the
112			field-of-view vary or move in a regular (periodic) fashion. Pulsatile flow
113			of blood or CSF, cardiac motion, and respiratory motion are the most
114			important patient-related causes of ghost artifacts in clinical MR imaging
115			(from `mriquestions.com <http://mriquestions.com/why-discrete-ghosts.html>`_).
116
117			Args:
118			num_ghosts: Number of 'ghosts' :math:`n` in the image.
119			axes: Axis along which the ghosts will be created.
120			intensity: Positive number representing the artifact strength
121			:math:`s` with respect to the maximum of the :math:`k`-space.
122			If ``0``, the ghosts will not be visible.
123			restore: Number between ``0`` and ``1`` indicating how much of the
124			:math:`k`-space center should be restored after removing the planes
125			that generate the artifact.
126			keys: See :py:class:`~torchio.transforms.Transform`.
127
128			.. note:: The execution time of this transform does not depend on the
129			number of ghosts.
130			"""
131			def __init__(
132			self,
133			num_ghosts: Union[int, Dict[str, int]],
134			axis: Union[int, Dict[str, int]],
135			intensity: Union[float, Dict[str, float]],
136			restore: Union[float, Dict[str, float]],
137			keys: Optional[Sequence[str]] = None,
138			):
139			super().__init__(keys=keys)
140			self.axis = axis
141			self.num_ghosts = num_ghosts
142			self.intensity = intensity
143			self.restore = restore
144			self.args_names = 'num_ghosts', 'axis', 'intensity', 'restore'
145
146			def apply_transform(self, subject: Subject) -> Subject:
147			axis = self.axis
148			num_ghosts = self.num_ghosts
149			intensity = self.intensity
150			restore = self.restore
151			for name, image in self.get_images_dict(subject).items():
152			if self.arguments_are_dict():
153			axis = self.axis[name]
154			num_ghosts = self.num_ghosts[name]
155			intensity = self.intensity[name]
156			restore = self.restore[name]
157			transformed_tensors = []
158			for tensor in image.data:
159			transformed_tensor = self.add_artifact(
160			tensor,
161			num_ghosts,
162			axis,
163			intensity,
164			restore,
165			)
166			transformed_tensors.append(transformed_tensor)
167			image[DATA] = torch.stack(transformed_tensors)
168			return subject
169
170			def add_artifact(
171			self,
172			tensor: torch.Tensor,
173			num_ghosts: int,
174			axis: int,
175			intensity: float,
176			restore_center: float,
177			):
178			if not num_ghosts or not intensity:
179			return tensor
180
181			array = tensor.numpy()
182			spectrum = self.fourier_transform(array)
183
184			shape = np.array(array.shape)
185			ri, rj, rk = np.round(restore_center * shape).astype(np.uint16)
186			mi, mj, mk = np.array(array.shape) // 2
187
188			# Variable "planes" is the part of the spectrum that will be modified
189			if axis == 0:
190			planes = spectrum[::num_ghosts, :, :]
191			restore = spectrum[mi, :, :].copy()
192			elif axis == 1:
193			planes = spectrum[:, ::num_ghosts, :]
194			restore = spectrum[:, mj, :].copy()
195			elif axis == 2:
196			planes = spectrum[:, :, ::num_ghosts]
197			restore = spectrum[:, :, mk].copy()
198
199			# Multiply by 0 if intensity is 1
200			planes *= 1 - intensity
			0 ignored issues – show introduced 2020-06-24 14:37 UTC by Report Bug Copy Issue Report The variable `planes` does not seem to be defined for all execution paths. Loading history...
201
202			# Restore the center of k-space to avoid extreme artifacts
203			if axis == 0:
204			spectrum[mi, :, :] = restore
			0 ignored issues – show introduced 2020-06-24 14:37 UTC by Report Bug Copy Issue Report The variable `restore` does not seem to be defined for all execution paths. Loading history...
205			elif axis == 1:
206			spectrum[:, mj, :] = restore
207			elif axis == 2:
208			spectrum[:, :, mk] = restore
209
210			array_ghosts = self.inv_fourier_transform(spectrum)
211			array_ghosts = np.real(array_ghosts)
212			return torch.from_numpy(array_ghosts)
213
214
215			def _parse_restore(restore):
216			if not isinstance(restore, float):
217			raise TypeError(f'Restore must be a float, not {restore}')
218			if not 0 <= restore <= 1:
219			message = (
220			f'Restore must be a number between 0 and 1, not {restore}')
221			raise ValueError(message)
222			return restore
223

fepegar / torchio

Pull Request — master (#353)

RandomGhosting.__init__() B

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RandomGhosting.init() B