Image Generator¶

Overview¶

Added in version 2.4.

The image generator module allows users to generate simulated radiation images. This module is different than other modules in that the goal here is non-deterministic. There are no phantom analysis routines here. What is here started as a testing concept for pylinac itself, but has uses for advanced users of pylinac who wish to build their own tools.

Warning

This feature is currently experimental and untested.

The module allows users to create a pipeline ala keras, where layers are added to an empty image. The user can add as many layers as they wish.

Quick Start¶

The basics to get started are to import the image simulators and layers from pylinac and add the layers as desired.

from matplotlib import pyplot as plt

from pylinac.core.image_generator import AS1000Image
from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

as1000 = AS1000Image()  # this will set the pixel size and shape automatically
as1000.add_layer(FilteredFieldLayer(field_size_mm=(50, 50)))  # create a 50x50mm square field
as1000.add_layer(GaussianFilterLayer(sigma_mm=2))  # add an image-wide gaussian to simulate penumbra/scatter
as1000.generate_dicom(file_out_name="my_AS1000.dcm", gantry_angle=45)  # create a DICOM file with the simulated image
# plot the generated image
plt.imshow(as1000.image)

(Source code, png, hires.png, pdf)

Layers & Simulators¶

Layers are very simple structures. They usually have constructor arguments specific to the layer and always define an apply method with the signature .apply(image, pixel_size) -> image. The apply method returns the modified image (a numpy array). That’s it!

Simulators are also simple and define the parameters of the image to which layers are added. They have pixel_size and shape properties and always have an add_layer method with the signature .add_layer(layer: Layer). They also have a generate_dicom method for dumping the image along with mostly stock metadata to DICOM.

Extending Layers & Simulators¶

This module is meant to be extensible. That’s why the structures are defined so simply. To create a custom simulator, inherit from Simulator and define the pixel size and shape. Note that generating DICOM does not come for free:

from pylinac.core.image_generator.simulators import Simulator

class AS5000(Simulator):
    pixel_size = 0.12
    shape = (5000, 5000)

# use like any other simulator

To implement a custom layer, inherit from Layer and implement the apply method:

from pylinac.core.image_generator.layers import Layer


class MyAwesomeLayer(Layer):
    def apply(image, pixel_size):
        # do stuff here
        return image


# use
from pylinac.core.image_generator import AS1000Image

as1000 = AS1000Image()
as1000.add_layer(MyAwesomeLayer())
...

Examples¶

Let’s make some images!

Simple Open Field¶

from matplotlib import pyplot as plt
from pylinac.core.image_generator import AS1000Image
from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

as1000 = AS1000Image()  # this will set the pixel size and shape automatically
as1000.add_layer(FilteredFieldLayer(field_size_mm=(150, 150)))  # create a 50x50mm square field
as1000.add_layer(GaussianFilterLayer(sigma_mm=2))  # add an image-wide gaussian to simulate penumbra/scatter
# plot the generated image
plt.imshow(as1000.image)

(Source code, png, hires.png, pdf)

Off-center Open Field¶

from matplotlib import pyplot as plt
from pylinac.core.image_generator import AS1000Image
from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

as1000 = AS1000Image()  # this will set the pixel size and shape automatically
as1000.add_layer(FilteredFieldLayer(field_size_mm=(30, 30), cax_offset_mm=(20, 40)))
as1000.add_layer(GaussianFilterLayer(sigma_mm=3))
# plot the generated image
plt.imshow(as1000.image)

(Source code, png, hires.png, pdf)

Winston-Lutz FFF Cone Field with Noise¶

from matplotlib import pyplot as plt
from pylinac.core.image_generator import AS1200Image
from pylinac.core.image_generator.layers import FilterFreeConeLayer, GaussianFilterLayer, PerfectBBLayer, RandomNoiseLayer

as1200 = AS1200Image()
as1200.add_layer(FilterFreeConeLayer(50))
as1200.add_layer(PerfectBBLayer(bb_size_mm=5))
as1200.add_layer(GaussianFilterLayer(sigma_mm=2))
as1200.add_layer(RandomNoiseLayer(sigma=0.02))
# plot the generated image
plt.imshow(as1200.image)

(Source code, png, hires.png, pdf)

VMAT DRMLC¶

from matplotlib import pyplot as plt
from pylinac.core.image_generator import AS1200Image
from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

as1200 = AS1200Image()
as1200.add_layer(FilteredFieldLayer((150, 20), cax_offset_mm=(0, -40)))
as1200.add_layer(FilteredFieldLayer((150, 20), cax_offset_mm=(0, -10)))
as1200.add_layer(FilteredFieldLayer((150, 20), cax_offset_mm=(0, 20)))
as1200.add_layer(FilteredFieldLayer((150, 20), cax_offset_mm=(0, 50)))
as1200.add_layer(GaussianFilterLayer())
plt.imshow(as1200.image)
plt.show()

(Source code, png, hires.png, pdf)

Picket Fence¶

from matplotlib import pyplot as plt
from pylinac.core.image_generator import AS1200Image
from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

as1200 = AS1200Image()
height = 350
width = 4
offsets = range(-100, 100, 20)
for offset in offsets:
    as1200.add_layer(FilteredFieldLayer((height, width), cax_offset_mm=(0, offset)))
as1200.add_layer(GaussianFilterLayer())
plt.imshow(as1200.image)
plt.show()

(Source code, png, hires.png, pdf)

Starshot¶

Simulating a starshot requires a small trick as angled fields cannot be handled by default. The following example rotates the image after every layer is applied.

Note

Rotating the image like this is a convenient trick but note that it will rotate the entire existing image including all previous layers. This will also possibly erroneously adjust the horn effect simulation. Use with caution.

from scipy import ndimage
from matplotlib import pyplot as plt
from pylinac.core.image_generator import AS1200Image
from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

as1200 = AS1200Image()
as1200.add_layer(FilteredFieldLayer((250, 7), alpha=0.5))
as1200.image = ndimage.rotate(as1200.image, 30, reshape=False, mode='nearest')
as1200.add_layer(FilteredFieldLayer((250, 7), alpha=0.5))
as1200.image = ndimage.rotate(as1200.image, 30, reshape=False, mode='nearest')
as1200.add_layer(FilteredFieldLayer((250, 7), alpha=0.5))
as1200.image = ndimage.rotate(as1200.image, 30, reshape=False, mode='nearest')
as1200.add_layer(FilteredFieldLayer((250, 7), alpha=0.5))
as1200.image = ndimage.rotate(as1200.image, 30, reshape=False, mode='nearest')
as1200.add_layer(FilteredFieldLayer((250, 7), alpha=0.5))
as1200.image = ndimage.rotate(as1200.image, 30, reshape=False, mode='nearest')
as1200.add_layer(FilteredFieldLayer((250, 7), alpha=0.5))
as1200.add_layer(GaussianFilterLayer())
plt.imshow(as1200.image)
plt.show()

(Source code, png, hires.png, pdf)

Helper utilities¶

Using the new utility functions of v2.5+ we can construct full dicom files of picket fence and winston-lutz sets of images:

from pylinac.core.image_generator import generate_picketfence, generate_winstonlutz
from pylinac.core import image_generator

sim = image_generator.simulators.AS1000Image()
field_layer = image_generator.layers.FilteredFieldLayer  # could also do FilterFreeLayer
generate_picketfence(
    simulator=Simulator,
    field_layer=FilteredFieldLayer,
    file_out="pf_image.dcm",
    pickets=11,
    picket_spacing_mm=20,
    picket_width_mm=2,
    picket_height_mm=300,
    gantry_angle=0,
)
# we now have a pf image saved as 'pf_image.dcm'

# create a set of WL images
# this will create 4 images (via image_axes len) with an offset of 3mm to the left
# the function is smart enough to correct for the offset w/r/t gantry angle.
generate_winstonlutz(
    simulator=sim,
    field_layer=field_layer,
    final_layers=[GaussianFilterLayer()],
    gantry_tilt=0,
    dir_out="./wl_dir",
    offset_mm_left=3,
    image_axes=[[0, 0, 0], [180, 0, 0], [90, 0, 0], [270, 0, 0]],
)

Tips & Tricks¶

The FilteredFieldLayer and FilterFree<Field, Cone>Layer have gaussian filters applied to create a first-order approximation of the horn(s) of the beam. It doesn’t claim to be super-accurate, it’s just to give some reality to the images. You can adjust the magnitude of these parameters to simulate other energies (e.g. sharper horns) when defining the layer.
The Perfect...Layer s do not apply any energy correction as above.
Use alpha to adjust the intensity of the layer. E.g. the BB layer has a default alpha of -0.5 to simulate attenuation. This will subtract out up to half of the possible dose range existing on the image thus far (e.g. an open image of alpha 1.0 will be reduced to 0.5 after a BB is layered with alpha=-0.5). If you want to simulate a thick material like tungsten you can adjust the alpha to be lower (more attenuation). An alpha of 1 means full radiation, no attenuation (like an open field).
Generally speaking, don’t apply more than one GaussianFilterLayer since they are additive. A good rule is to apply one filter at the end of your layering.
Apply ConstantLayer s at the beginning rather than the end.

Warning

Pylinac uses unsigned int16 datatypes (native EPID dtype). To keep images from flipping bits when adding layers, pylinac will clip the values. Just be careful when, e.g. adding a ConstantLayer at the end of a layering. Better to do this at the beginning.

API Documentation¶

Layers¶

class pylinac.core.image_generator.layers.PerfectConeLayer(cone_size_mm: float = 10, cax_offset_mm: float, float = (0, 0), alpha: float = 1.0)[source]¶

Bases: Layer

A cone without flattening filter effects

Parameters¶

cone_size_mm: Cone size in mm at the iso plane
cax_offset_mm: The offset in mm. (out, right)
alpha: The intensity of the layer. 1 is full saturation/radiation. 0 is none.

apply(image: ndarray, pixel_size: float, mag_factor: float) → ndarray[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.FilterFreeConeLayer(cone_size_mm: float = 10, cax_offset_mm: float, float = (0, 0), alpha: float = 1.0, filter_magnitude: float = 0.4, filter_sigma_mm: float = 80)[source]¶

Bases: PerfectConeLayer

A cone with flattening filter effects.

Parameters¶

cone_size_mm: Cone size in mm at the iso plane
cax_offset_mm: The offset in mm. (out, right)
alpha: The intensity of the layer. 1 is full saturation/radiation. 0 is none.
filter_magnitude: The magnitude of the CAX peak. Larger values result in “pointier” fields.
filter_sigma_mm: Proportional to the width of the CAX peak. Larger values produce wider curves.

apply(image: ndarray, pixel_size: float, mag_factor: float) → ndarray[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.PerfectFieldLayer(field_size_mm: float, float = (10, 10), cax_offset_mm: float, float = (0, 0), alpha: float = 1.0)[source]¶

Bases: Layer

A square field without flattening filter effects

Parameters¶

field_size_mm: Field size in mm at the iso plane
cax_offset_mm: The offset in mm. (out, right)
alpha: The intensity of the layer. 1 is full saturation/radiation. 0 is none.

apply(image: ndarray, pixel_size: float, mag_factor: float) → array[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.FilteredFieldLayer(field_size_mm: float, float = (10, 10), cax_offset_mm: float, float = (0, 0), alpha: float = 1.0, gaussian_height: float = 0.03, gaussian_sigma_mm: float = 32)[source]¶

Bases: PerfectFieldLayer

A square field with flattening filter effects

Parameters¶

field_size_mm: Field size in mm at the iso plane
cax_offset_mm: The offset in mm. (out, right)
alpha: The intensity of the layer. 1 is full saturation/radiation. 0 is none.
gaussian_height: The intensity of the “horns”, or more accurately, the CAX dip. Proportional to the max value allowed for the data type. Increase to make the horns more prominent.
gaussian_sigma_mm: The width of the “horns”. A.k.a. the CAX dip width. Increase to create a wider horn effect.

apply(image: array, pixel_size: float, mag_factor: float) → array[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.FilterFreeFieldLayer(field_size_mm: float, float = (10, 10), cax_offset_mm: float, float = (0, 0), alpha: float = 1.0, gaussian_height: float = 0.4, gaussian_sigma_mm: float = 80)[source]¶

Bases: FilteredFieldLayer

A square field with flattening filter free (FFF) effects

Parameters¶

field_size_mm: Field size in mm at the iso plane
cax_offset_mm: The offset in mm. (out, right)
alpha: The intensity of the layer. 1 is full saturation/radiation. 0 is none.
gaussian_height: The magnitude of the CAX peak. Larger values result in “pointier” fields.
gaussian_sigma_mm: Proportional to the width of the CAX peak. Larger values produce wider curves.

apply(image: array, pixel_size: float, mag_factor: float) → array[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.PerfectBBLayer(bb_size_mm: float = 5, cax_offset_mm: float, float = (0, 0), alpha: float = -0.5)[source]¶

Bases: PerfectConeLayer

A BB-like layer. Like a cone, but with lower alpha (i.e. higher opacity)

Parameters¶

cone_size_mm: Cone size in mm at the iso plane
cax_offset_mm: The offset in mm. (out, right)
alpha: The intensity of the layer. 1 is full saturation/radiation. 0 is none.

apply(image: ndarray, pixel_size: float, mag_factor: float) → ndarray¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.GaussianFilterLayer(sigma_mm: float = 2)[source]¶

Bases: Layer

A Gaussian filter. Simulates the effects of scatter on the field

apply(image: array, pixel_size: float, mag_factor: float) → array[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.RandomNoiseLayer(mean: float = 0.0, sigma: float = 0.001)[source]¶

Bases: Layer

A salt and pepper noise, simulating dark current

apply(image: array, pixel_size: float, mag_factor: float) → array[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

class pylinac.core.image_generator.layers.ConstantLayer(constant: float)[source]¶

Bases: Layer

A constant layer. Can be used to simulate scatter or background.

apply(image: array, pixel_size: float, mag_factor: float) → array[source]¶: Apply the layer. Takes a 2D array and pixel size value in and returns a modified array.

Simulators¶

class pylinac.core.image_generator.simulators.AS500Image(sid: float = 1500)[source]¶

Bases: Simulator

Simulates an AS500 EPID image.

Parameters¶

sid: Source to image distance in mm.

as_dicom(gantry_angle: float = 0.0, coll_angle: float = 0.0, table_angle: float = 0.0) → Dataset[source]¶: Create and return a pydicom Dataset. I.e. create a pseudo-DICOM image.

add_layer(layer: Layer) → None¶: Add a layer to the image

class pylinac.core.image_generator.simulators.AS1000Image(sid: float = 1500)[source]¶

Bases: Simulator

Simulates an AS1000 EPID image.

Parameters¶

sid: Source to image distance in mm.

as_dicom(gantry_angle: float = 0.0, coll_angle: float = 0.0, table_angle: float = 0.0) → Dataset[source]¶: Create and return a pydicom Dataset. I.e. create a pseudo-DICOM image.

add_layer(layer: Layer) → None¶: Add a layer to the image

class pylinac.core.image_generator.simulators.AS1200Image(sid: float = 1500)[source]¶

Bases: Simulator

Simulates an AS1200 EPID image.

Parameters¶

sid: Source to image distance in mm.

add_layer(layer: Layer) → None¶: Add a layer to the image

as_dicom(gantry_angle: float = 0.0, coll_angle: float = 0.0, table_angle: float = 0.0) → Dataset[source]¶: Create and return a pydicom Dataset. I.e. create a pseudo-DICOM image.

Helpers¶

pylinac.core.image_generator.utils.generate_picketfence(simulator: Simulator, field_layer: type[FilterFreeFieldLayer | FilteredFieldLayer | PerfectFieldLayer], file_out: str, final_layers: list[Layer] = None, pickets: int = 11, picket_spacing_mm: float = 20, picket_width_mm: int = 2, picket_height_mm: int = 300, gantry_angle: int = 0, orientation: Orientation = Orientation.UP_DOWN, picket_offset_error: Sequence | None = None) → None[source]¶

Create a mock picket fence image. Will always be up-down.

Parameters¶

simulator: The image simulator
field_layer: The primary field layer
file_out: The name of the file to save the DICOM file to.
final_layers: Optional layers to apply at the end of the procedure. Useful for noise or blurring.
pickets: The number of pickets
picket_spacing_mm: The space between pickets
picket_width_mm: Picket width parallel to leaf motion
picket_height_mm: Picket height parallel to leaf motion
gantry_angle: Gantry angle; sets the DICOM tag.

pylinac.core.image_generator.utils.generate_winstonlutz(simulator: Simulator, field_layer: type[Layer], dir_out: str, field_size_mm: tuple[float, float] = (30, 30), final_layers: list[Layer] | None = None, bb_size_mm: float = 5, offset_mm_left: float = 0, offset_mm_up: float = 0, offset_mm_in: float = 0, image_axes: int, int, int, ... = ((0, 0, 0), (90, 0, 0), (180, 0, 0), (270, 0, 0)), gantry_tilt: float = 0, gantry_sag: float = 0, clean_dir: bool = True, field_alpha: float = 1.0, bb_alpha: float = -0.5) → list[str][source]¶

Create a mock set of WL images, simulating gantry sag effects. Produces one image for each item in image_axes.

Parameters¶

simulator: The image simulator
field_layer: The primary field layer simulating radiation
dir_out: The directory to save the images to.
field_size_mm: The field size of the radiation field in mm
final_layers: Layers to apply after generating the primary field and BB layer. Useful for blurring or adding noise.
bb_size_mm: The size of the BB. Must be positive.
offset_mm_left: How far left (lat) to set the BB. Can be positive or negative.
offset_mm_up: How far up (vert) to set the BB. Can be positive or negative.
offset_mm_in: How far in (long) to set the BB. Can be positive or negative.
image_axes: List of axis values for the images. Sequence is (Gantry, Coll, Couch).
gantry_tilt: The tilt of the gantry that affects the position at 0 and 180. Simulates a simple cosine function.
gantry_sag: The sag of the gantry that affects the position at gantry=90 and 270. Simulates a simple sine function.
clean_dir: Whether to clean out the output directory. Useful when iterating.
field_alpha: The normalized alpha (i.e. signal) of the radiation field. Use in combination with bb_alpha such that the sum of the two is always <= 1.
bb_alpha: The normalized alpha (in the case of the BB think of it as attenuation) of the BB against the radiation field. More negative values attenuate (remove signal) more.

pylinac.core.image_generator.utils.generate_winstonlutz_cone(simulator: Simulator, cone_layer: type[FilterFreeConeLayer] | type[PerfectConeLayer], dir_out: str, cone_size_mm: float = 17.5, final_layers: list[Layer] | None = None, bb_size_mm: float = 5, offset_mm_left: float = 0, offset_mm_up: float = 0, offset_mm_in: float = 0, image_axes: int, int, int, ... = ((0, 0, 0), (90, 0, 0), (180, 0, 0), (270, 0, 0)), gantry_tilt: float = 0, gantry_sag: float = 0, clean_dir: bool = True) → list[str][source]¶

Create a mock set of WL images with a cone field, simulating gantry sag effects. Produces one image for each item in image_axes.

Parameters¶

simulator: The image simulator
cone_layer: The primary field layer simulating radiation
dir_out: The directory to save the images to.
cone_size_mm: The field size of the radiation field in mm
final_layers: Layers to apply after generating the primary field and BB layer. Useful for blurring or adding noise.
bb_size_mm: The size of the BB. Must be positive.
offset_mm_left: How far left (lat) to set the BB. Can be positive or negative.
offset_mm_up: How far up (vert) to set the BB. Can be positive or negative.
offset_mm_in: How far in (long) to set the BB. Can be positive or negative.
image_axes: List of axis values for the images. Sequence is (Gantry, Coll, Couch).
gantry_tilt: The tilt of the gantry in degrees that affects the position at 0 and 180. Simulates a simple cosine function.
gantry_sag: The sag of the gantry that affects the position at gantry=90 and 270. Simulates a simple sine function.
clean_dir: Whether to clean out the output directory. Useful when iterating.

pylinac.core.image_generator.utils.generate_winstonlutz_multi_bb_multi_field(simulator: Simulator, field_layer: type[Layer], dir_out: str, field_offsets: list[list[float]], bb_offsets: list[list[float]] | list[dict[str, float]], field_size_mm: tuple[float, float] = (20, 20), final_layers: list[Layer] | None = None, bb_size_mm: float = 5, image_axes: int, int, int, ... = ((0, 0, 0), (90, 0, 0), (180, 0, 0), (270, 0, 0)), gantry_tilt: float = 0, gantry_sag: float = 0, clean_dir: bool = True, jitter_mm: float = 0, align_to_pixels: bool = True) → list[str][source]¶

Create a mock set of WL images, simulating gantry sag effects. Produces one image for each item in image_axes. This will also generate multiple BBs on the image, one per item in offsets. Each offset should be a list of the shifts of the BB relative to isocenter like so: [<left>, <up>, <in>] OR an arrangement from the WL module.

Parameters¶

simulator: The image simulator
field_layer: The primary field layer simulating radiation
dir_out: The directory to save the images to.
field_offsets: A list of lists containing the shift of the fields. Format is the same as bb_offsets.
bb_offsets: A list of lists containing the shift of the BBs from iso; each sublist should be a 3-item list/tuple of left, up, in. Negative values are acceptable and will go the opposite direction.
field_size_mm: The field size of the radiation field in mm
final_layers: Layers to apply after generating the primary field and BB layer. Useful for blurring or adding noise.
bb_size_mm: The size of the BB. Must be positive.
image_axes: List of axis values for the images. Sequence is (Gantry, Coll, Couch).
gantry_tilt: The tilt of the gantry in degrees that affects the position at 0 and 180. Simulates a simple cosine function.
gantry_sag: The sag of the gantry that affects the position at gantry=90 and 270. Simulates a simple sine function.
clean_dir: Whether to clean out the output directory. Useful when iterating.
jitter_mm: The amount of jitter to add to the in/left/up location of the BB in MM.