"""The Winston-Lutz module loads and processes EPID images that have acquired Winston-Lutz type images.
Features:
* **Couch shift instructions** - After running a WL test, get immediate feedback on how to shift the couch.
Couch values can also be passed in and the new couch values will be presented so you don't have to do that pesky conversion.
"Do I subtract that number or add it?"
* **Automatic field & BB positioning** - When an image or directory is loaded, the field CAX and the BB
are automatically found, along with the vector and scalar distance between them.
* **Isocenter size determination** - Using backprojections of the EPID images, the 3D gantry isocenter size
and position can be determined *independent of the BB position*. Additionally, the 2D planar isocenter size
of the collimator and couch can also be determined.
* **Image plotting** - WL images can be plotted separately or together, each of which shows the field CAX, BB and
scalar distance from BB to CAX.
* **Axis deviation plots** - Plot the variation of the gantry, collimator, couch, and EPID in each plane
as well as RMS variation.
* **File name interpretation** - Rename DICOM filenames to include axis information for linacs that don't include
such information in the DICOM tags. E.g. "myWL_gantry45_coll0_couch315.dcm".
"""
import dataclasses
import enum
import io
import math
import os.path as osp
import webbrowser
from dataclasses import dataclass
from itertools import zip_longest
from pathlib import Path
from textwrap import wrap
from typing import Union, List, Tuple, Optional, BinaryIO, Iterable, Dict
import argue
import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage, optimize, linalg
from skimage import measure
from skimage.measure._regionprops import RegionProperties
from .core import image, pdf
from .core.decorators import lru_cache
from .core.geometry import Point, Line, Vector, cos, sin
from .core.io import TemporaryZipDirectory, get_url, retrieve_demo_file, is_dicom_image
from .core.mask import bounding_box
from .core.utilities import is_close, ResultBase, convert_to_enum
class Axis(enum.Enum):
GANTRY = "Gantry" #:
COLLIMATOR = "Collimator" #:
COUCH = "Couch" #:
GB_COMBO = "GB Combo" #:
GBP_COMBO = "GBP Combo" #:
EPID = "Epid" #:
REFERENCE = "Reference" #:
@dataclass
class WinstonLutz2DResult(ResultBase):
variable_axis: str #:
cax2epid_vector: Vector #:
cax2epid_distance: float #:
cax2bb_distance: float #:
cax2bb_vector: Vector #:
bb_location: Point #:
field_cax: Point #:
[docs]@dataclass
class WinstonLutzResult(ResultBase):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
It is a dataclass under the hood and thus comes with all the dunder magic.
Use the following attributes as normal class attributes."""
num_gantry_images: int #:
num_gantry_coll_images: int #:
num_coll_images: int #:
num_couch_images: int #:
num_total_images: int #:
max_2d_cax_to_bb_mm: float #:
median_2d_cax_to_bb_mm: float #:
mean_2d_cax_to_bb_mm: float #:
max_2d_cax_to_epid_mm: float #:
median_2d_cax_to_epid_mm: float #:
mean_2d_cax_to_epid_mm: float #:
gantry_3d_iso_diameter_mm: float #:
max_gantry_rms_deviation_mm: float #:
max_epid_rms_deviation_mm: float #:
gantry_coll_3d_iso_diameter_mm: float #:
coll_2d_iso_diameter_mm: float #:
max_coll_rms_deviation_mm: float #:
couch_2d_iso_diameter_mm: float #:
max_couch_rms_deviation_mm: float #:
[docs]class WinstonLutz:
"""Class for performing a Winston-Lutz test of the radiation isocenter."""
images: list #:
def __init__(
self,
directory: Union[str, List[str], Path],
use_filenames: bool = False,
axis_mapping: Optional[Dict[str, Tuple[int, int, int]]] = None,
):
"""
Parameters
----------
directory : str, list[str]
Path to the directory of the Winston-Lutz EPID images or a list of the image paths
use_filenames: bool
Whether to try to use the file name to determine axis values.
Useful for Elekta machines that do not include that info in the DICOM data.
axis_mapping: dict
An optional way of instantiating by passing each file along with the axis values.
Structure should be <filename>: (<gantry>, <coll>, <couch>).
"""
self.images = []
if axis_mapping:
for filename, (gantry, coll, couch) in axis_mapping.items():
self.images.append(
WinstonLutz2D(
Path(directory) / filename,
use_filenames=False,
gantry=gantry,
coll=coll,
couch=couch,
)
)
elif isinstance(directory, list):
for file in directory:
if is_dicom_image(file):
img = WinstonLutz2D(file, use_filenames)
self.images.append(img)
elif not osp.isdir(directory):
raise ValueError(
"Invalid directory passed. Check the correct method and file was used."
)
else:
image_files = image.retrieve_image_files(directory)
for file in image_files:
img = WinstonLutz2D(file, use_filenames)
self.images.append(img)
if len(self.images) < 2:
raise ValueError(
"<2 valid WL images were found in the folder/file or passed. Ensure you chose the correct folder/file for analysis."
)
self.images.sort(
key=lambda i: (i.gantry_angle, i.collimator_angle, i.couch_angle)
)
self._is_analyzed = False
[docs] @classmethod
def from_demo_images(cls):
"""Instantiate using the demo images."""
demo_file = retrieve_demo_file(name="winston_lutz.zip")
return cls.from_zip(demo_file)
[docs] @classmethod
def from_zip(
cls,
zfile: Union[str, BinaryIO],
use_filenames: bool = False,
axis_mapping: Optional[Dict[str, Tuple[int, int, int]]] = None,
):
"""Instantiate from a zip file rather than a directory.
Parameters
----------
zfile
Path to the archive file.
use_filenames : bool
Whether to interpret axis angles using the filenames.
Set to true for Elekta machines where the gantry/coll/couch data is not in the DICOM metadata.
axis_mapping: dict
An optional way of instantiating by passing each file along with the axis values.
Structure should be <filename>: (<gantry>, <coll>, <couch>).
"""
with TemporaryZipDirectory(zfile) as tmpz:
obj = cls(tmpz, use_filenames=use_filenames, axis_mapping=axis_mapping)
return obj
[docs] @classmethod
def from_url(cls, url: str, use_filenames: bool = False):
"""Instantiate from a URL.
Parameters
----------
url : str
URL that points to a zip archive of the DICOM images.
use_filenames : bool
Whether to interpret axis angles using the filenames.
Set to true for Elekta machines where the gantry/coll/couch data is not in the DICOM metadata.
"""
zfile = get_url(url)
return cls.from_zip(zfile, use_filenames=use_filenames)
[docs] @staticmethod
def run_demo():
"""Run the Winston-Lutz demo, which loads the demo files, prints results, and plots a summary image."""
wl = WinstonLutz.from_demo_images()
wl.analyze()
print(wl.results())
wl.plot_summary()
[docs] def analyze(self, bb_size_mm: float = 5):
"""Analyze the WL images.
Parameters
----------
bb_size_mm
The expected size of the BB in mm. The actual size of the BB can be +/-2mm from the passed value.
"""
for img in self.images:
img.analyze(bb_size_mm)
self._is_analyzed = True
@lru_cache()
def _minimize_axis(self, axes: Union[Axis, Tuple[Axis, ...]] = (Axis.GANTRY,)):
"""Return the minimization result of the given axis."""
if isinstance(axes, Axis):
axes = (axes,)
def max_distance_to_lines(p, lines) -> float:
"""Calculate the maximum distance to any line from the given point."""
point = Point(p[0], p[1], p[2])
return max(line.distance_to(point) for line in lines)
things = [
image.cax_line_projection
for image in self.images
if image.variable_axis in (axes + (Axis.REFERENCE,))
]
if len(things) <= 1:
raise ValueError(
"Not enough images of the given type to identify the axis isocenter"
)
initial_guess = np.array([0, 0, 0])
bounds = [(-20, 20), (-20, 20), (-20, 20)]
result = optimize.minimize(
max_distance_to_lines, initial_guess, args=things, bounds=bounds
)
return result
@property
def gantry_iso_size(self) -> float:
"""The diameter of the 3D gantry isocenter size in mm. Only images where the collimator
and couch were at 0 are used to determine this value."""
num_gantry_like_images = self._get_images((Axis.GANTRY, Axis.REFERENCE))[0]
if num_gantry_like_images > 1:
return self._minimize_axis(Axis.GANTRY).fun * 2
else:
return 0
@property
def gantry_coll_iso_size(self) -> float:
"""The diameter of the 3D gantry isocenter size in mm *including collimator and gantry/coll combo images*.
Images where the couch!=0 are excluded."""
num_gantry_like_images = self._get_images(
(Axis.GANTRY, Axis.COLLIMATOR, Axis.GB_COMBO, Axis.REFERENCE)
)[0]
if num_gantry_like_images > 1:
return (
self._minimize_axis((Axis.GANTRY, Axis.COLLIMATOR, Axis.GB_COMBO)).fun
* 2
)
else:
return 0
@staticmethod
def _find_max_distance_between_points(images) -> float:
"""Find the maximum distance between a set of points. Used for 2D images like collimator and couch."""
points = [
Point(image.cax2bb_vector.x, image.cax2bb_vector.y) for image in images
]
dists = []
for point1 in points:
for point2 in points:
p = point1.distance_to(point2)
dists.append(p)
return max(dists)
@property
def collimator_iso_size(self) -> float:
"""The 2D collimator isocenter size (diameter) in mm. The iso size is in the plane
normal to the gantry."""
num_collimator_like_images, images = self._get_images(
(Axis.COLLIMATOR, Axis.REFERENCE)
)
if num_collimator_like_images > 1:
return self._find_max_distance_between_points(images)
else:
return 0
@property
def couch_iso_size(self) -> float:
"""The diameter of the 2D couch isocenter size in mm. Only images where
the gantry and collimator were at zero are used to determine this value."""
num_couch_like_images, images = self._get_images((Axis.COUCH, Axis.REFERENCE))
if num_couch_like_images > 1:
return self._find_max_distance_between_points(images)
else:
return 0
@property
def bb_shift_vector(self) -> Vector:
"""The shift necessary to place the BB at the radiation isocenter.
The values are in the coordinates defined in the documentation.
The shift is based on the paper by Low et al. See online documentation for more.
"""
A = np.empty([2 * len(self.images), 3])
epsilon = np.empty([2 * len(self.images), 1])
for idx, img in enumerate(self.images):
g = img.gantry_angle
c = img.couch_angle_varian_scale
A[2 * idx : 2 * idx + 2, :] = np.array(
[
[-cos(c), -sin(c), 0],
[-cos(g) * sin(c), cos(g) * cos(c), -sin(g)],
]
) # equation 6 (minus delta)
epsilon[2 * idx : 2 * idx + 2] = np.array(
[[img.cax2bb_vector.y], [img.cax2bb_vector.x]]
) # equation 7
B = linalg.pinv(A)
delta = B.dot(epsilon) # equation 9
return Vector(x=delta[1][0], y=-delta[0][0], z=-delta[2][0])
[docs] def bb_shift_instructions(
self,
couch_vrt: Optional[float] = None,
couch_lng: Optional[float] = None,
couch_lat: Optional[float] = None,
) -> str:
"""Returns a string describing how to shift the BB to the radiation isocenter looking from the foot of the couch.
Optionally, the current couch values can be passed in to get the new couch values. If passing the current
couch position all values must be passed.
Parameters
----------
couch_vrt : float
The current couch vertical position in cm.
couch_lng : float
The current couch longitudinal position in cm.
couch_lat : float
The current couch lateral position in cm.
"""
sv = self.bb_shift_vector
x_dir = "LEFT" if sv.x < 0 else "RIGHT"
y_dir = "IN" if sv.y > 0 else "OUT"
z_dir = "UP" if sv.z > 0 else "DOWN"
move = f"{x_dir} {abs(sv.x):2.2f}mm; {y_dir} {abs(sv.y):2.2f}mm; {z_dir} {abs(sv.z):2.2f}mm"
if all(val is not None for val in [couch_vrt, couch_lat, couch_lng]):
new_lat = round(couch_lat + sv.x / 10, 2)
new_vrt = round(couch_vrt + sv.z / 10, 2)
new_lng = round(couch_lng + sv.y / 10, 2)
move += f"\nNew couch coordinates (mm): VRT: {new_vrt:3.2f}; LNG: {new_lng:3.2f}; LAT: {new_lat:3.2f}"
return move
[docs] @argue.options(value=("all", "range"))
def axis_rms_deviation(
self, axis: Axis = Axis.GANTRY, value: str = "all"
) -> Union[Iterable, float]:
"""The RMS deviations of a given axis/axes.
Parameters
----------
axis : ('Gantry', 'Collimator', 'Couch', 'Epid', 'GB Combo', 'GBP Combo')
The axis desired.
value : {'all', 'range'}
Whether to return all the RMS values from all images for that axis, or only return the maximum range of
values, i.e. the 'sag'.
"""
axis = convert_to_enum(axis, Axis)
if axis != Axis.EPID:
attr = "cax2bb_vector"
else:
attr = "cax2epid_vector"
axis = (Axis.GANTRY, Axis.COLLIMATOR, Axis.REFERENCE)
imgs = self._get_images(axis=axis)[1]
if len(imgs) <= 1:
return (0,)
rms = [getattr(img, attr).as_scalar() for img in imgs]
if value == "range":
rms = max(rms) - min(rms)
return rms
[docs] @argue.options(metric=("max", "median", "mean"))
def cax2bb_distance(self, metric: str = "max") -> float:
"""The distance in mm between the CAX and BB for all images according to the given metric.
Parameters
----------
metric : {'max', 'median', 'mean'}
The metric of distance to use.
"""
if metric == "max":
return max(image.cax2bb_distance for image in self.images)
elif metric == "median":
return float(np.median([image.cax2bb_distance for image in self.images]))
elif metric == "mean":
return float(np.mean([image.cax2bb_distance for image in self.images]))
[docs] @argue.options(metric=("max", "median", "mean"))
def cax2epid_distance(self, metric: str = "max") -> float:
"""The distance in mm between the CAX and EPID center pixel for all images according to the given metric.
Parameters
----------
metric : {'max', 'median', 'mean'}
The metric of distance to use.
"""
if metric == "max":
return max(image.cax2epid_distance for image in self.images)
elif metric == "median":
return float(np.median([image.cax2epid_distance for image in self.images]))
elif metric == "mean":
return float(np.mean([image.cax2epid_distance for image in self.images]))
def _plot_deviation(
self, axis: Axis, ax: Optional[plt.Axes] = None, show: bool = True
):
"""Helper function: Plot the sag in Cartesian coordinates.
Parameters
----------
axis : {'gantry', 'epid', 'collimator', 'couch'}
The axis to plot.
ax : None, matplotlib.Axes
The axis to plot to. If None, creates a new plot.
show : bool
Whether to show the image.
"""
title = f"In-plane {axis.value} displacement"
if axis == Axis.EPID:
attr = "cax2epid_vector"
axis = Axis.GANTRY
else:
attr = "cax2bb_vector"
# get axis images, angles, and shifts
imgs = [
image
for image in self.images
if image.variable_axis in (axis, Axis.REFERENCE)
]
angles = [
getattr(image, "{}_angle".format(axis.value.lower())) for image in imgs
]
xz_sag = np.array([getattr(img, attr).x for img in imgs])
y_sag = np.array([getattr(img, attr).y for img in imgs])
rms = np.sqrt(xz_sag**2 + y_sag**2)
# plot the axis deviation
if ax is None:
ax = plt.subplot(111)
ax.plot(angles, y_sag, "bo", label="Y-axis", ls="-.")
ax.plot(angles, xz_sag, "m^", label="X/Z-axis", ls="-.")
ax.plot(angles, rms, "g+", label="RMS", ls="-")
ax.set_title(title)
ax.set_ylabel("mm")
ax.set_xlabel(f"{axis.value} angle")
ax.set_xticks(np.arange(0, 361, 45))
ax.set_xlim(-15, 375)
ax.grid(True)
ax.legend(numpoints=1)
if show:
plt.show()
def _get_images(
self, axis: Union[Axis, Tuple[Axis, ...]] = (Axis.GANTRY,)
) -> Tuple[float, list]:
if isinstance(axis, Axis):
axis = (axis,)
images = [image for image in self.images if image.variable_axis in axis]
return len(images), images
[docs] def plot_axis_images(
self, axis: Axis = Axis.GANTRY, show: bool = True, ax: Optional[plt.Axes] = None
):
"""Plot all CAX/BB/EPID positions for the images of a given axis.
For example, axis='Couch' plots a reference image, and all the BB points of the other
images where the couch was moving.
Parameters
----------
axis : {'Gantry', 'Collimator', 'Couch', 'GB Combo', 'GBP Combo'}
The images/markers from which accelerator axis to plot.
show : bool
Whether to actually show the images.
ax : None, matplotlib.Axes
The axis to plot to. If None, creates a new plot.
"""
axis = convert_to_enum(axis, Axis)
images = [
image
for image in self.images
if image.variable_axis in (axis, Axis.REFERENCE)
]
ax = images[0].plot(
show=False, ax=ax
) # plots the first marker; plot the rest of the markers below
if axis != Axis.COUCH:
# plot EPID
epid_xs = [img.epid.x for img in images[1:]]
epid_ys = [img.epid.y for img in images[1:]]
ax.plot(epid_xs, epid_ys, "b+", ms=8)
# get CAX positions
xs = [img.field_cax.x for img in images[1:]]
ys = [img.field_cax.y for img in images[1:]]
marker = "gs"
else:
# get BB positions
xs = [img.bb.x for img in images[1:]]
ys = [img.bb.y for img in images[1:]]
marker = "ro"
ax.plot(xs, ys, marker, ms=8)
# set labels
ax.set_title(axis.value + " wobble")
ax.set_xlabel(axis.value + " positions superimposed")
ax.set_ylabel(
axis.value
+ f" iso size: {getattr(self, axis.value.lower() + '_iso_size'):3.2f}mm"
)
if show:
plt.show()
[docs] def plot_images(
self, axis: Axis = Axis.GANTRY, show: bool = True, split: bool = False, **kwargs
) -> (List[plt.Figure], List[str]):
"""Plot a grid of all the images acquired.
Four columns are plotted with the titles showing which axis that column represents.
Parameters
----------
axis : {'Gantry', 'Collimator', 'Couch', 'GB Combo', 'GBP Combo', 'All'}
show : bool
Whether to show the image.
split : bool
Whether to show/plot the images individually or as one large figure.
"""
axis = convert_to_enum(axis, Axis)
if not self._is_analyzed:
raise ValueError("The set is not analyzed. Use .analyze() first.")
def plot_image(image, axis):
"""Helper function to plot a WLImage to an axis."""
if image is None:
axis.set_frame_on(False)
axis.axis("off")
else:
image.plot(ax=axis, show=False)
# get axis images
if axis == Axis.GANTRY:
images = [
image
for image in self.images
if image.variable_axis in (Axis.GANTRY, Axis.REFERENCE)
]
elif axis == Axis.COLLIMATOR:
images = [
image
for image in self.images
if image.variable_axis in (Axis.COLLIMATOR, Axis.REFERENCE)
]
elif axis == Axis.COUCH:
images = [
image
for image in self.images
if image.variable_axis in (Axis.COUCH, Axis.REFERENCE)
]
elif axis == Axis.GB_COMBO:
images = [
image
for image in self.images
if image.variable_axis
in (Axis.GB_COMBO, Axis.GANTRY, Axis.COLLIMATOR, Axis.REFERENCE)
]
elif axis == Axis.GBP_COMBO:
images = self.images
# set the figsize if it wasn't passed
if not kwargs.get("figsize"):
dpi = 72
width_px = 1080
width_in = width_px / dpi
if not split:
max_num_images = math.ceil(len(images) / 4)
height_in = (width_in / 4) * max_num_images
else:
height_in = width_in = 3
kwargs["figsize"] = (width_in, height_in)
figs = []
names = []
# create plots
if not split:
fig, axes = plt.subplots(nrows=max_num_images, ncols=4, **kwargs)
for mpl_axis, wl_image in zip_longest(axes.flatten(), images):
plot_image(wl_image, mpl_axis)
# set titles
fig.suptitle(f"{axis.value} images", fontsize=14, y=1)
fig.tight_layout()
figs.append(fig)
names.append("image")
else:
for wl_image in images:
fig, axes = plt.subplots(**kwargs)
plot_image(wl_image, axes)
figs.append(fig)
names.append(str(wl_image))
if show:
plt.show()
return figs, names
[docs] def save_images(
self, filename: Union[str, BinaryIO], axis: Axis = Axis.GANTRY, **kwargs
):
"""Save the figure of `plot_images()` to file. Keyword arguments are passed to `matplotlib.pyplot.savefig()`.
Parameters
----------
filename : str
The name of the file to save to.
axis
The axis to save.
"""
self.plot_images(axis=axis, show=False)
plt.savefig(filename, **kwargs)
[docs] def save_images_to_stream(self, **kwargs) -> Dict[str, io.BytesIO]:
"""Save the individual image plots to stream"""
figs, names = self.plot_images(
axis=Axis.GBP_COMBO, show=False, split=True
) # all images
streams = [io.BytesIO() for _ in figs]
for fig, stream in zip(figs, streams):
fig.savefig(stream, **kwargs)
return {name: stream for name, stream in zip(names, streams)}
[docs] def plot_summary(self, show: bool = True, fig_size: Optional[tuple] = None):
"""Plot a summary figure showing the gantry sag and wobble plots of the three axes."""
if not self._is_analyzed:
raise ValueError("The set is not analyzed. Use .analyze() first.")
figsize = (11, 9) if fig_size is None else fig_size
plt.figure(figsize=figsize)
grid = (3, 6)
gantry_sag_ax = plt.subplot2grid(grid, (0, 0), colspan=3)
self._plot_deviation(Axis.GANTRY, gantry_sag_ax, show=False)
epid_sag_ax = plt.subplot2grid(grid, (0, 3), colspan=3)
self._plot_deviation(Axis.EPID, epid_sag_ax, show=False)
if self._get_images((Axis.COLLIMATOR, Axis.REFERENCE))[0] > 1:
coll_sag_ax = plt.subplot2grid(grid, (1, 0), colspan=3)
self._plot_deviation(Axis.COLLIMATOR, coll_sag_ax, show=False)
if self._get_images((Axis.COUCH, Axis.REFERENCE))[0] > 1:
couch_sag_ax = plt.subplot2grid(grid, (1, 3), colspan=3)
self._plot_deviation(Axis.COUCH, couch_sag_ax, show=False)
for axis, axnum in zip((Axis.GANTRY, Axis.COLLIMATOR, Axis.COUCH), (0, 2, 4)):
if self._get_images((axis, Axis.REFERENCE))[0] > 1:
ax = plt.subplot2grid(grid, (2, axnum), colspan=2)
self.plot_axis_images(axis=axis, ax=ax, show=False)
if show:
plt.tight_layout()
plt.show()
[docs] def save_summary(self, filename: Union[str, BinaryIO], **kwargs):
"""Save the summary image."""
self.plot_summary(show=False, fig_size=kwargs.pop("fig_size", None))
plt.tight_layout()
plt.savefig(filename, **kwargs)
[docs] def results(self, as_list: bool = False) -> str:
"""Return the analysis results summary.
Parameters
----------
as_list : bool
Whether to return as a list of strings vs single string. Pretty much for internal usage.
"""
if not self._is_analyzed:
raise ValueError("The set is not analyzed. Use .analyze() first.")
num_gantry_imgs = self._get_images(axis=(Axis.GANTRY, Axis.REFERENCE))[0]
num_gantry_coll_imgs = self._get_images(
axis=(Axis.GANTRY, Axis.COLLIMATOR, Axis.GB_COMBO, Axis.REFERENCE)
)[0]
num_coll_imgs = self._get_images(axis=(Axis.COLLIMATOR, Axis.REFERENCE))[0]
num_couch_imgs = self._get_images(axis=(Axis.COUCH, Axis.REFERENCE))[0]
num_imgs = len(self.images)
result = [
"Winston-Lutz Analysis",
"=================================",
f"Number of images: {num_imgs}",
f"Maximum 2D CAX->BB distance: {self.cax2bb_distance('max'):.2f}mm",
f"Median 2D CAX->BB distance: {self.cax2bb_distance('median'):.2f}mm",
f"Shift to iso: facing gantry, move BB: {self.bb_shift_instructions()}",
f"Gantry 3D isocenter diameter: {self.gantry_iso_size:.2f}mm ({num_gantry_imgs}/{num_imgs} images considered)",
f"Maximum Gantry RMS deviation (mm): {max(self.axis_rms_deviation(Axis.GANTRY)):.2f}mm",
f"Maximum EPID RMS deviation (mm): {max(self.axis_rms_deviation(Axis.EPID)):.2f}mm",
f"Gantry+Collimator 3D isocenter diameter: {self.gantry_coll_iso_size:.2f}mm ({num_gantry_coll_imgs}/{num_imgs} images considered)",
f"Collimator 2D isocenter diameter: {self.collimator_iso_size:.2f}mm ({num_coll_imgs}/{num_imgs} images considered)",
f"Maximum Collimator RMS deviation (mm): {max(self.axis_rms_deviation(Axis.COLLIMATOR)):.2f}",
f"Couch 2D isocenter diameter: {self.couch_iso_size:.2f}mm ({num_couch_imgs}/{num_imgs} images considered)",
f"Maximum Couch RMS deviation (mm): {max(self.axis_rms_deviation(Axis.COUCH)):.2f}",
]
if not as_list:
result = "\n".join(result)
return result
[docs] def results_data(self, as_dict=False) -> Union[WinstonLutzResult, dict]:
"""Present the results data and metadata as a dataclass or dict.
The default return type is a dataclass."""
if not self._is_analyzed:
raise ValueError("The set is not analyzed. Use .analyze() first.")
num_gantry_imgs = self._get_images(axis=(Axis.GANTRY, Axis.REFERENCE))[0]
num_gantry_coll_imgs = self._get_images(
axis=(Axis.GANTRY, Axis.COLLIMATOR, Axis.GB_COMBO, Axis.REFERENCE)
)[0]
num_coll_imgs = self._get_images(axis=(Axis.COLLIMATOR, Axis.REFERENCE))[0]
num_couch_imgs = self._get_images(axis=(Axis.COUCH, Axis.REFERENCE))[0]
data = WinstonLutzResult(
num_total_images=len(self.images),
num_gantry_images=num_gantry_imgs,
num_coll_images=num_coll_imgs,
num_gantry_coll_images=num_gantry_coll_imgs,
num_couch_images=num_couch_imgs,
max_2d_cax_to_bb_mm=self.cax2bb_distance("max"),
median_2d_cax_to_bb_mm=self.cax2bb_distance("median"),
mean_2d_cax_to_bb_mm=self.cax2bb_distance("mean"),
max_2d_cax_to_epid_mm=self.cax2epid_distance("max"),
median_2d_cax_to_epid_mm=self.cax2epid_distance("median"),
mean_2d_cax_to_epid_mm=self.cax2epid_distance("mean"),
coll_2d_iso_diameter_mm=self.collimator_iso_size,
couch_2d_iso_diameter_mm=self.couch_iso_size,
gantry_3d_iso_diameter_mm=self.gantry_iso_size,
gantry_coll_3d_iso_diameter_mm=self.gantry_coll_iso_size,
max_gantry_rms_deviation_mm=max(self.axis_rms_deviation(axis=Axis.GANTRY)),
max_coll_rms_deviation_mm=max(
self.axis_rms_deviation(axis=Axis.COLLIMATOR)
),
max_couch_rms_deviation_mm=max(self.axis_rms_deviation(axis=Axis.COUCH)),
max_epid_rms_deviation_mm=max(self.axis_rms_deviation(axis=Axis.EPID)),
)
if as_dict:
return dataclasses.asdict(data)
return data
[docs] def publish_pdf(
self,
filename: str,
notes: Optional[Union[str, List[str]]] = None,
open_file: bool = False,
metadata: Optional[dict] = None,
):
"""Publish (print) a PDF containing the analysis, images, and quantitative results.
Parameters
----------
filename : (str, file-like object}
The file to write the results to.
notes : str, list of strings
Text; if str, prints single line.
If list of strings, each list item is printed on its own line.
open_file : bool
Whether to open the file using the default program after creation.
metadata : dict
Extra data to be passed and shown in the PDF. The key and value will be shown with a colon.
E.g. passing {'Author': 'James', 'Unit': 'TrueBeam'} would result in text in the PDF like:
--------------
Author: James
Unit: TrueBeam
--------------
"""
if not self._is_analyzed:
raise ValueError("The set is not analyzed. Use .analyze() first.")
plt.ioff()
title = "Winston-Lutz Analysis"
canvas = pdf.PylinacCanvas(filename, page_title=title, metadata=metadata)
text = self.results(as_list=True)
canvas.add_text(text=text, location=(7, 25.5))
# draw summary image on 1st page
data = io.BytesIO()
self.save_summary(data, fig_size=(8, 8))
canvas.add_image(image_data=data, location=(2, 3), dimensions=(16, 16))
if notes is not None:
canvas.add_text(text="Notes:", location=(1, 4.5), font_size=14)
canvas.add_text(text=notes, location=(1, 4))
# add more pages showing individual axis images
for ax in (
Axis.GANTRY,
Axis.COLLIMATOR,
Axis.COUCH,
Axis.GB_COMBO,
Axis.GBP_COMBO,
):
if self._contains_axis_images(ax):
canvas.add_new_page()
data = io.BytesIO()
self.save_images(data, axis=ax)
canvas.add_image(data, location=(2, 7), dimensions=(18, 18))
canvas.finish()
if open_file:
webbrowser.open(filename)
def _contains_axis_images(self, axis: Axis = Axis.GANTRY) -> bool:
"""Return whether or not the set of WL images contains images pertaining to a given axis"""
return any(True for image in self.images if image.variable_axis in (axis,))
[docs]class WinstonLutz2D(image.LinacDicomImage):
"""Holds individual Winston-Lutz EPID images, image properties, and automatically finds the field CAX and BB."""
bb: Point
field_cax: Point
_rad_field_bounding_box: list
def __init__(
self, file: Union[str, BinaryIO, Path], use_filenames: bool = False, **kwargs
):
"""
Parameters
----------
file : str
Path to the image file.
use_filenames: bool
Whether to try to use the file name to determine axis values.
Useful for Elekta machines that do not include that info in the DICOM data.
"""
super().__init__(file, use_filenames=use_filenames, **kwargs)
self._is_analyzed = False
[docs] def analyze(self, bb_size_mm: float = 5) -> None:
"""Analyze the image."""
self.check_inversion_by_histogram(percentiles=(0.01, 50, 99.99))
self.flipud()
self._clean_edges()
self.ground()
self.normalize()
self.field_cax, self._rad_field_bounding_box = self._find_field_centroid()
self.bb = self._find_bb(bb_size_mm)
self._is_analyzed = True
def __repr__(self):
return f"WLImage(G={self.gantry_angle:.1f}, B={self.collimator_angle:.1f}, P={self.couch_angle:.1f})"
def _clean_edges(self, window_size: int = 2) -> None:
"""Clean the edges of the image to be near the background level."""
def has_noise(self, window_size):
"""Helper method to determine if there is spurious signal at any of the image edges.
Determines if the min or max of an edge is within 10% of the baseline value and trims if not.
"""
near_min, near_max = np.percentile(self.array, [5, 99.5])
img_range = near_max - near_min
top = self[:window_size, :]
left = self[:, :window_size]
bottom = self[-window_size:, :]
right = self[:, -window_size:]
edge_array = np.concatenate(
(top.flatten(), left.flatten(), bottom.flatten(), right.flatten())
)
edge_too_low = edge_array.min() < (near_min - img_range / 10)
edge_too_high = edge_array.max() > (near_max + img_range / 10)
return edge_too_low or edge_too_high
safety_stop = np.min(self.shape) / 10
while has_noise(self, window_size) and safety_stop > 0:
self.crop(window_size)
safety_stop -= 1
def _find_field_centroid(self) -> Tuple[Point, List]:
"""Find the centroid of the radiation field based on a 50% height threshold.
Returns
-------
p
The CAX point location.
edges
The bounding box of the field, plus a small margin.
"""
min, max = np.percentile(self.array, [5, 99.9])
threshold_img = self.as_binary((max - min) / 2 + min)
filled_img = ndimage.binary_fill_holes(threshold_img)
# clean single-pixel noise from outside field
cleaned_img = ndimage.binary_erosion(threshold_img)
[*edges] = bounding_box(cleaned_img)
edges[0] -= 10
edges[1] += 10
edges[2] -= 10
edges[3] += 10
coords = ndimage.measurements.center_of_mass(filled_img)
p = Point(x=coords[-1], y=coords[0])
return p, edges
def _find_bb(self, bb_size: float) -> Point:
"""Find the BB within the radiation field. Iteratively searches for a circle-like object
by lowering a low-pass threshold value until found.
Returns
-------
Point
The weighted-pixel value location of the BB.
"""
# get initial starting conditions
hmin, hmax = np.percentile(self.array, [5, 99.99])
spread = hmax - hmin
max_thresh = hmax
lower_thresh = hmax - spread / 1.5
# search for the BB by iteratively lowering the low-pass threshold value until the BB is found.
found = False
while not found:
try:
binary_arr = np.logical_and((max_thresh > self), (self >= lower_thresh))
# use below for debugging
# plt.imshow(binary_arr)
# plt.show()
labeled_arr, num_roi = ndimage.measurements.label(binary_arr)
roi_sizes, bin_edges = np.histogram(labeled_arr, bins=num_roi + 1)
bw_bb_img = np.where(
labeled_arr == np.argsort(roi_sizes)[-3], 1, 0
) # we pick the 3rd largest one because the largest is the background, 2nd is rad field, 3rd is the BB
bw_bb_img = ndimage.binary_fill_holes(bw_bb_img).astype(
int
) # fill holes for low energy beams like 2.5MV
bb_regionprops = measure.regionprops(bw_bb_img)[0]
if not is_round(bb_regionprops):
raise ValueError
if not is_modest_size(bw_bb_img, self.dpmm, bb_size):
raise ValueError
if not is_symmetric(bw_bb_img):
raise ValueError
if not is_near_center(bb_regionprops, self.dpmm, bw_bb_img.shape):
raise ValueError
except (IndexError, ValueError):
max_thresh -= 0.03 * spread
if max_thresh < hmin:
raise ValueError(
"Unable to locate the BB. Make sure the field edges do not obscure the BB and that there is no artifacts in the images."
)
else:
found = True
# determine the center of mass of the BB
inv_img = image.load(self.array)
# we invert so BB intensity increases w/ attenuation
inv_img.check_inversion_by_histogram(percentiles=(99.99, 50, 0.01))
bb_rprops = measure.regionprops(bw_bb_img, intensity_image=inv_img)[0]
return Point(bb_rprops.weighted_centroid[1], bb_rprops.weighted_centroid[0])
@property
def epid(self) -> Point:
"""Center of the EPID panel"""
return self.center
@property
def cax_line_projection(self) -> Line:
"""The projection of the field CAX through space around the area of the BB.
Used for determining gantry isocenter size.
Returns
-------
Line
The virtual line in space made by the beam CAX.
"""
p1 = Point()
p2 = Point()
# point 1 - ray origin
p1.x = self.cax2bb_vector.x * cos(self.gantry_angle) + 20 * sin(
self.gantry_angle
)
p1.y = self.cax2bb_vector.x * -sin(self.gantry_angle) + 20 * cos(
self.gantry_angle
)
p1.z = self.cax2bb_vector.y
# point 2 - ray destination
p2.x = self.cax2bb_vector.x * cos(self.gantry_angle) - 20 * sin(
self.gantry_angle
)
p2.y = self.cax2bb_vector.x * -sin(self.gantry_angle) - 20 * cos(
self.gantry_angle
)
p2.z = self.cax2bb_vector.y
l = Line(p1, p2)
return l
@property
def couch_angle_varian_scale(self) -> float:
"""The couch angle converted from IEC 61217 scale to "Varian" scale. Note that any new Varian machine uses 61217."""
# convert to Varian scale per Low paper scale
if super().couch_angle > 250:
return 2 * 270 - super().couch_angle
else:
return 180 - super().couch_angle
@property
def cax2bb_vector(self) -> Vector:
"""The vector in mm from the CAX to the BB."""
dist = (self.bb - self.field_cax) / self.dpmm
return Vector(dist.x, dist.y, dist.z)
@property
def cax2bb_distance(self) -> float:
"""The scalar distance in mm from the CAX to the BB."""
dist = self.field_cax.distance_to(self.bb)
return dist / self.dpmm
@property
def cax2epid_vector(self) -> Vector:
"""The vector in mm from the CAX to the EPID center pixel"""
dist = (self.epid - self.field_cax) / self.dpmm
return Vector(dist.x, dist.y, dist.z)
@property
def cax2epid_distance(self) -> float:
"""The scalar distance in mm from the CAX to the EPID center pixel"""
return self.field_cax.distance_to(self.epid) / self.dpmm
[docs] def plot(
self, ax: Optional[plt.Axes] = None, show: bool = True, clear_fig: bool = False
):
"""Plot the image, zoomed-in on the radiation field, along with the detected
BB location and field CAX location.
Parameters
----------
ax : None, matplotlib Axes instance
The axis to plot to. If None, will create a new figure.
show : bool
Whether to actually show the image.
clear_fig : bool
Whether to clear the figure first before drawing.
"""
ax = super().plot(ax=ax, show=False, clear_fig=clear_fig)
ax.plot(self.field_cax.x, self.field_cax.y, "gs", ms=8)
ax.plot(self.bb.x, self.bb.y, "ro", ms=8)
ax.axvline(x=self.epid.x, color="b")
ax.axhline(y=self.epid.y, color="b")
ax.set_ylim([self._rad_field_bounding_box[0], self._rad_field_bounding_box[1]])
ax.set_xlim([self._rad_field_bounding_box[2], self._rad_field_bounding_box[3]])
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.set_title("\n".join(wrap(str(self.path), 30)), fontsize=10)
ax.set_xlabel(
f"G={self.gantry_angle:.0f}, B={self.collimator_angle:.0f}, P={self.couch_angle:.0f}"
)
ax.set_ylabel(f"CAX to BB: {self.cax2bb_distance:3.2f}mm")
if show:
plt.show()
return ax
[docs] def save_plot(self, filename: str, **kwargs):
"""Save the image plot to file."""
self.plot(show=False)
plt.savefig(filename, **kwargs)
@property
def variable_axis(self) -> Axis:
"""The axis that is varying.
There are five types of images:
* Reference : All axes are at 0.
* Gantry: All axes but gantry at 0.
* Collimator : All axes but collimator at 0.
* Couch : All axes but couch at 0.
* Combo : More than one axis is not at 0.
"""
G0 = is_close(self.gantry_angle, [0, 360])
B0 = is_close(self.collimator_angle, [0, 360])
P0 = is_close(self.couch_angle, [0, 360])
if G0 and B0 and not P0:
return Axis.COUCH
elif G0 and P0 and not B0:
return Axis.COLLIMATOR
elif P0 and B0 and not G0:
return Axis.GANTRY
elif P0 and B0 and G0:
return Axis.REFERENCE
elif P0:
return Axis.GB_COMBO
else:
return Axis.GBP_COMBO
[docs] def results_data(self, as_dict=False) -> Union[WinstonLutz2DResult, dict]:
"""Present the results data and metadata as a dataclass or dict.
The default return type is a dataclass."""
if not self._is_analyzed:
raise ValueError("The image is not analyzed. Use .analyze() first.")
data = WinstonLutz2DResult(
variable_axis=self.variable_axis.value,
cax2bb_vector=self.cax2bb_vector,
cax2epid_vector=self.cax2epid_vector,
cax2bb_distance=self.cax2bb_distance,
cax2epid_distance=self.cax2epid_distance,
bb_location=self.bb,
field_cax=self.field_cax,
)
if as_dict:
return dataclasses.asdict(data)
return data
def is_symmetric(logical_array: np.ndarray) -> bool:
"""Whether the binary object's dimensions are symmetric, i.e. a perfect circle. Used to find the BB."""
ymin, ymax, xmin, xmax = bounding_box(logical_array)
y = abs(ymax - ymin)
x = abs(xmax - xmin)
if x > max(y * 1.05, y + 3) or x < min(y * 0.95, y - 3):
return False
return True
def is_near_center(
region: RegionProperties, dpmm: float, shape: Tuple[float, float]
) -> bool:
"""Whether the bb is <2cm from the center of the field"""
extent_limit_mm = 20
bottom, left, top, right = region.bbox
bb_center_x = left + (right - left) / 2
bb_center_y = bottom + (top - bottom) / 2
x_lo_limit = shape[1] / 2 - dpmm * extent_limit_mm
x_hi_limit = shape[1] / 2 + dpmm * extent_limit_mm
is_bb_x_centered = x_lo_limit < bb_center_x < x_hi_limit
y_lo_limit = shape[0] / 2 - dpmm * extent_limit_mm
y_hi_limit = shape[0] / 2 + dpmm * extent_limit_mm
is_bb_y_centered = y_lo_limit < bb_center_y < y_hi_limit
return is_bb_x_centered and is_bb_y_centered
def is_modest_size(logical_array: np.ndarray, dpmm: float, bb_size: float) -> bool:
"""Decide whether the ROI is roughly the size of a BB; not noise and not an artifact. Used to find the BB."""
bb_area = np.sum(logical_array) / (dpmm**2)
bb_size = max((bb_size, 2.1))
expected_bb_area = np.pi * (bb_size / 2) ** 2
larger_bb_area = np.pi * ((bb_size + 2) / 2) ** 2
smaller_bb_area = np.pi * ((bb_size - 2) / 2) ** 2
# return bool(np.isclose(bb_area, expected_bb_area, rtol=0.6))
return smaller_bb_area < bb_area < larger_bb_area
def is_round(region: RegionProperties) -> bool:
"""Decide if the ROI is circular in nature by testing the filled area vs bounding box. Used to find the BB."""
expected_fill_ratio = np.pi / 4 # area of a circle inside a square
actual_fill_ratio = region.filled_area / region.bbox_area
return expected_fill_ratio * 1.2 > actual_fill_ratio > expected_fill_ratio * 0.8