# -*- coding: utf-8 -*-
"""The VMAT module consists of the class VMAT, which is capable of loading an EPID DICOM Open field image and MLC field image and analyzing the
images according to the Varian RapidArc QA tests and procedures, specifically the Dose-Rate & Gantry-Speed (DRGS)
and Dose-Rate & MLC speed (DRMLC) tests.
Features:
* **Do both tests** - Pylinac can handle either DRGS or DRMLC tests.
* **Automatic offset correction** - Older VMAT tests had the ROIs offset, newer ones are centered. No worries, pylinac finds the ROIs automatically.
* **Automatic open/DMLC identification** - Pass in both images--don't worry about naming. Pylinac will automatically identify the right images.
"""
import dataclasses
import enum
import typing
from dataclasses import dataclass
from io import BytesIO
from typing import Union, List, Tuple, Sequence, Optional, BinaryIO
import argue
import matplotlib.pyplot as plt
import numpy as np
from .core import image
from .core.geometry import Point, Rectangle
from .core.image import ImageLike
from .core.io import get_url, TemporaryZipDirectory, retrieve_demo_file
from .core.pdf import PylinacCanvas
from .core.profile import SingleProfile, Interpolation, Edge
from .core.utilities import open_path, ResultBase
from .settings import get_dicom_cmap
class ImageType(enum.Enum):
"""The image type options"""
DMLC = 'dmlc' #:
OPEN = 'open' #:
PROFILE = 'profile' #:
[docs]@dataclass
class SegmentResult:
"""An individual segment/ROI result"""
passed: bool #:
x_position_mm: float #:
r_corr: float #:
r_dev: float #:
center_x_y: float #:
[docs]@dataclass
class VMATResult(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."""
test_type: str #:
tolerance_percent: float #:
max_deviation_percent: float #:
abs_mean_deviation: float #:
passed: bool #:
segment_data: typing.Iterable[SegmentResult] #:
[docs]class VMATBase:
_url_suffix: str
_result_header: str
_result_short_header: str
SEGMENT_X_POSITIONS_MM: Tuple
dmlc_image: image.DicomImage
open_image: image.DicomImage
segments: List
_tolerance: float
def __init__(self, image_paths: Sequence[Union[str, BinaryIO]]):
"""
Parameters
----------
image_paths : iterable (list, tuple, etc)
A sequence of paths to the image files.
"""
if len(image_paths) != 2:
raise ValueError("Exactly 2 images (open, DMLC) must be passed")
image1, image2 = self._load_images(image_paths)
image1, image2 = self._check_img_inversion(image1, image2)
self._identify_images(image1, image2)
self.segments = []
self._tolerance = 0
[docs] @classmethod
def from_url(cls, url: str):
"""Load a ZIP archive from a URL. Must follow the naming convention.
Parameters
----------
url : str
Must point to a valid URL that is a ZIP archive of two VMAT images.
"""
zfile = get_url(url)
return cls.from_zip(zfile)
[docs] @classmethod
def from_zip(cls, path: str):
"""Load VMAT images from a ZIP file that contains both images. Must follow the naming convention.
Parameters
----------
path : str
Path to the ZIP archive which holds the VMAT image files.
"""
with TemporaryZipDirectory(path) as tmpzip:
image_files = image.retrieve_image_files(tmpzip)
return cls(image_paths=image_files)
[docs] @classmethod
def from_demo_images(cls):
"""Construct a VMAT instance using the demo images."""
demo_file = retrieve_demo_file(url=cls._url_suffix)
return cls.from_zip(demo_file)
[docs] @argue.bounds(tolerance=(0, 8))
def analyze(self, tolerance: Union[float, int] = 1.5, segment_size_mm: Tuple = (5, 100)):
"""Analyze the open and DMLC field VMAT images, according to 1 of 2 possible tests.
Parameters
----------
tolerance : float, int, optional
The tolerance of the sample deviations in percent. Default is 1.5.
Must be between 0 and 8.
segment_size_mm : tuple(int, int)
The (width, height) of the ROI segments in mm.
"""
self._tolerance = tolerance/100
"""Analysis"""
points = self._calculate_segment_centers()
Segment._nominal_width_mm = segment_size_mm[0]
Segment._nominal_height_mm = segment_size_mm[1]
self._construct_segments(points)
@staticmethod
def _load_images(image_paths: Sequence[Union[str, BytesIO]]) -> Tuple[ImageLike, ImageLike]:
image1 = image.load(image_paths[0])
image2 = image.load(image_paths[1])
image1.ground()
image2.ground()
return image1, image2
@staticmethod
def _check_img_inversion(image1: ImageLike, image2: ImageLike) -> Tuple[ImageLike, ImageLike]:
"""Check that the images are correctly inverted."""
for image in [image1, image2]:
image.check_inversion()
return image1, image2
def _identify_images(self, image1: ImageLike, image2: ImageLike):
"""Identify which image is the DMLC and which is the open field."""
profile1, profile2 = self._median_profiles((image1, image2))
field_profile1 = profile1.field_data()['field values']
field_profile2 = profile2.field_data()['field values']
if np.std(field_profile1) > np.std(field_profile2):
self.dmlc_image = image1
self.open_image = image2
else:
self.dmlc_image = image2
self.open_image = image1
[docs] def results(self) -> str:
"""A string of the summary of the analysis results.
Returns
-------
str
The results string showing the overall result and deviation statistics by segment.
"""
if self.passed:
passfail_str = 'PASS'
else:
passfail_str = 'FAIL'
string = f'{self._result_header}\nTest Results (Tol. +/-{self._tolerance*100:2.2}%): {passfail_str}\n'
string += f'Max Deviation: {self.max_r_deviation:2.3}%\nAbsolute Mean Deviation: {self.avg_abs_r_deviation:2.3}%'
return string
[docs] def results_data(self, as_dict=False) -> Union[VMATResult, dict]:
"""Present the results data and metadata as a dataclass or dict.
The default return type is a dataclass."""
segment_data = []
for idx, segment in enumerate(self.segments):
segment_data.append(SegmentResult(passed=segment.passed,
r_corr=segment.r_corr,
r_dev=segment.r_dev,
center_x_y=segment.center.as_array(),
x_position_mm=self.SEGMENT_X_POSITIONS_MM[idx]))
data = VMATResult(
test_type=self._result_header,
tolerance_percent=self._tolerance*100,
max_deviation_percent=self.max_r_deviation,
abs_mean_deviation=self.avg_abs_r_deviation,
passed=self.passed,
segment_data=segment_data,
)
if as_dict:
return dataclasses.asdict(data)
return data
def _calculate_segment_centers(self) -> List[Point]:
"""Construct the center points of the segments based on the field center and known x-offsets."""
points = []
dmlc_prof, _ = self._median_profiles((self.dmlc_image, self.open_image))
x_field_center = dmlc_prof.beam_center()['index (rounded)']
for x_offset_mm in self.SEGMENT_X_POSITIONS_MM:
y = self.open_image.center.y
x_offset_pixels = x_offset_mm * self.open_image.dpmm
x = x_field_center + x_offset_pixels
points.append(Point(x, y))
return points
def _construct_segments(self, points: List[Point]):
for point in points:
segment = Segment(point, self.open_image, self.dmlc_image, self._tolerance)
self.segments.append(segment)
# post-analysis to update R_corr values
self._update_r_corrs()
def _update_r_corrs(self):
"""After the Segment constructions, the R_corr must be set for each segment."""
avg_r_corr = np.array([segment.r_corr for segment in self.segments]).mean()
for segment in self.segments:
segment.r_dev = ((segment.r_corr / avg_r_corr) * 100) - 100
@property
def passed(self) -> bool:
return all(segment.passed for segment in self.segments)
@property
def r_devs(self) -> np.ndarray:
"""Return the deviations of all segments as an array."""
return np.array([segment.r_dev for segment in self.segments])
@property
def avg_abs_r_deviation(self) -> float:
"""Return the average of the absolute R_deviation values."""
return np.abs(self.r_devs).mean()
@property
def avg_r_deviation(self) -> float:
"""Return the average of the R_deviation values, including the sign."""
return self.r_devs.mean()
@property
def max_r_deviation(self) -> float:
"""Return the value of the maximum R_deviation segment."""
return np.max(np.abs(self.r_devs))
[docs] def plot_analyzed_image(self, show: bool=True):
"""Plot the analyzed images. Shows the open and dmlc images with the segments drawn; also plots the median
profiles of the two images for visual comparison.
Parameters
----------
show : bool
Whether to actually show the image.
"""
fig, axes = plt.subplots(ncols=3, sharex=True)
subimages = (ImageType.OPEN, ImageType.DMLC, ImageType.PROFILE)
titles = ('Open', 'DMLC', 'Median Profiles')
for subimage, axis, title in zip(subimages, axes, titles):
self._plot_analyzed_subimage(subimage=subimage, ax=axis, show=False)
axis.set_title(title)
axis.set_ylabel('Normalized Response')
axis.legend(loc='lower center')
if show:
plt.tight_layout(h_pad=1.5)
plt.show()
def _save_analyzed_subimage(self, filename: Union[str, BytesIO], subimage: ImageType, **kwargs):
"""Save the analyzed images as a png file.
Parameters
----------
filename : str, file-object
Where to save the file to.
kwargs
Passed to matplotlib.
"""
self._plot_analyzed_subimage(subimage=subimage, show=False)
plt.savefig(filename, **kwargs)
def _plot_analyzed_subimage(self, subimage: ImageType, show: bool=True, ax: Optional[plt.Axes]=None):
"""Plot an individual piece of the VMAT analysis.
Parameters
----------
subimage : str
Specifies which image to plot.
show : bool
Whether to actually plot the image.
ax : matplotlib Axes, None
If None (default), creates a new figure to plot to, otherwise plots to the given axes.
"""
plt.ioff()
if ax is None:
fig, ax = plt.subplots()
# plot DMLC or OPEN image
if subimage in (ImageType.DMLC, ImageType.OPEN):
if subimage == ImageType.DMLC:
img = self.dmlc_image
elif subimage == ImageType.OPEN:
img = self.open_image
ax.imshow(img, cmap=get_dicom_cmap())
self._draw_segments(ax)
plt.sca(ax)
plt.axis('off')
plt.tight_layout()
# plot profile
elif subimage == ImageType.PROFILE:
dmlc_prof, open_prof = self._median_profiles((self.dmlc_image, self.open_image))
ax.plot(dmlc_prof.values, label='DMLC')
ax.plot(open_prof.values, label='Open')
ax.autoscale(axis='x', tight=True)
ax.legend(loc=8, fontsize='large')
ax.grid()
if show:
plt.show()
def _draw_segments(self, axis: plt.Axes):
"""Draw the segments onto a plot.
Parameters
----------
axis : matplotlib.axes.Axes
The plot to draw the objects on.
"""
for segment in self.segments:
color = segment.get_bg_color()
segment.plot2axes(axis, edgecolor=color)
@staticmethod
def _median_profiles(images) -> Tuple[SingleProfile, SingleProfile]:
"""Return two median profiles from the open and dmlc image. For visual comparison."""
profile1 = SingleProfile(np.mean(images[0], axis=0), interpolation=Interpolation.NONE, edge_detection_method=Edge.INFLECTION_DERIVATIVE)
profile1.stretch()
profile2 = SingleProfile(np.mean(images[1], axis=0), interpolation=Interpolation.NONE, edge_detection_method=Edge.INFLECTION_DERIVATIVE)
profile2.stretch()
# normalize the profiles to approximately the same value
norm_val = np.percentile(profile1.values, 90)
profile1.normalize(norm_val)
norm_val = np.percentile(profile2.values, 90)
profile2.normalize(norm_val)
return profile1, profile2
[docs] def publish_pdf(self, filename: str, notes: 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
--------------
"""
canvas = PylinacCanvas(filename=filename, page_title=f"{self._result_short_header} VMAT Analysis", metadata=metadata)
for y, x, width, img in zip((9, 9, -2), (1, 11, 3), (9, 9, 14), (ImageType.OPEN, ImageType.DMLC, ImageType.PROFILE)):
data = BytesIO()
self._save_analyzed_subimage(data, subimage=img)
canvas.add_image(data, location=(x, y), dimensions=(width, 18))
# canvas.add_text(text=f"{img} Image", location=(x + 2, y + 10), font_size=18)
canvas.add_text(text='Open Image', location=(4, 22), font_size=18)
canvas.add_text(text=f'{self.open_image.base_path}', location=(4, 21.5))
canvas.add_text(text='DMLC Image', location=(14, 22), font_size=18)
canvas.add_text(text=f'{self.dmlc_image.base_path}', location=(14, 21.5))
canvas.add_text(text='Median profiles', location=(8, 12), font_size=18)
text = [f'{self._result_header} VMAT results:',
f'Source-to-Image Distance (mm): {self.open_image.sid:2.0f}',
f'Tolerance (%): {self._tolerance*100:2.1f}',
f'Absolute mean deviation (%): {self.avg_abs_r_deviation:2.2f}',
f'Maximum deviation (%): {self.max_r_deviation:2.2f}',
]
canvas.add_text(text=text, location=(10, 25.5))
if notes is not None:
canvas.add_text(text="Notes:", location=(1, 5.5), font_size=14)
canvas.add_text(text=notes, location=(1, 5))
canvas.finish()
if open_file:
open_path(filename)
[docs]class DRGS(VMATBase):
"""Class representing a Dose-Rate, Gantry-speed VMAT test. Will accept, analyze, and return the results."""
_url_suffix = 'drgs.zip'
_result_header = 'Dose Rate & Gantry Speed'
_result_short_header = 'DR/GS'
SEGMENT_X_POSITIONS_MM = (-60, -40, -20, 0, 20, 40, 60)
[docs] @staticmethod
def run_demo():
"""Run the demo for the Dose Rate & Gantry Speed test."""
vmat = DRGS.from_demo_images()
vmat.analyze() # old images (rev1, not new rev2's), which are offset
print(vmat.results())
vmat.plot_analyzed_image()
[docs]class DRMLC(VMATBase):
"""Class representing a Dose-Rate, MLC speed VMAT test. Will accept, analyze, and return the results."""
_url_suffix = 'drmlc.zip'
_result_header = 'Dose Rate & MLC Speed'
_result_short_header = 'DR/MLCS'
SEGMENT_X_POSITIONS_MM = (-45, -15, 15, 45)
[docs] @staticmethod
def run_demo():
"""Run the demo for the MLC leaf speed test."""
vmat = DRMLC.from_demo_images()
vmat.analyze()
print(vmat.results())
vmat.plot_analyzed_image()
[docs]class Segment(Rectangle):
"""A class for holding and analyzing segment data of VMAT tests.
For VMAT tests, there are either 4 or 7 'segments', which represents a section of the image that received
radiation under the same conditions.
Attributes
----------
r_dev : float
The reading deviation (R_dev) from the average readings of all the segments. See RTD for equation info.
r_corr : float
The corrected reading (R_corr) of the pixel values. See RTD for explanation and equation info.
passed : boolean
Specifies where the segment reading deviation was under tolerance.
"""
# width of the segment (i.e. parallel to MLC motion) in pixels under reference conditions
_nominal_width_mm: int
_nominal_height_mm: int
def __init__(self, center_point: Point, open_image: image.DicomImage, dmlc_image: image.DicomImage,
tolerance: Union[float, int]):
self.r_dev: float = 0.0 # is assigned after all segments constructed
self._tolerance = tolerance
self._open_image = open_image
self._dmlc_image = dmlc_image
width = self._nominal_width_mm * dmlc_image.dpmm
height = self._nominal_height_mm * dmlc_image.dpmm
super().__init__(width, height, center=center_point, as_int=True)
@property
def r_corr(self) -> float:
"""Return the ratio of the mean pixel values of DMLC/OPEN images."""
dmlc_value = self._dmlc_image.array[self.bl_corner.y:self.bl_corner.y + self.height,
self.bl_corner.x: self.bl_corner.x + self.width].mean()
open_value = self._open_image.array[self.bl_corner.y:self.bl_corner.y + self.height,
self.bl_corner.x: self.bl_corner.x + self.width].mean()
ratio = (dmlc_value / open_value) * 100
return ratio
@property
def passed(self) -> bool:
"""Return whether the segment passed or failed."""
return abs(self.r_dev) < self._tolerance * 100
[docs] def get_bg_color(self) -> str:
"""Get the background color of the segment when plotted, based on the pass/fail status."""
return 'blue' if self.passed else 'red'