Source code for pylinac.vmat

"""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.
"""
from __future__ import annotations

import copy
import dataclasses
import enum
import typing
import webbrowser
from dataclasses import dataclass
from io import BytesIO
from pathlib import Path
from typing import BinaryIO, Sequence

import matplotlib.pyplot as plt
import numpy as np

from . import Normalization
from .core import image
from .core.geometry import Point, Rectangle
from .core.image import DicomImage, ImageLike
from .core.io import TemporaryZipDirectory, get_url, retrieve_demo_file
from .core.pdf import PylinacCanvas
from .core.profile import FWXMProfile
from .core.utilities import 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 #: stdev: 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] #: named_segment_data: dict[str, SegmentResult] #:
[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 documentation for equation info. """ # width of the segment (i.e. parallel to MLC motion) in pixels under reference conditions _nominal_width_mm: int _nominal_height_mm: int r_dev: float def __init__( self, center_point: Point, open_image: image.DicomImage, dmlc_image: image.DicomImage, tolerance: 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 stdev(self) -> float: """Return the standard deviation of the segment.""" 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, ] 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, ] # we multiply by 100 to be consistent w/ r_corr. I.e. this is a % value. return float(np.std(dmlc_value / open_value)) @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"
[docs] class VMATBase: _url_suffix: str _result_header: str _result_short_header: str roi_config: dict default_roi_config: dict dmlc_image: image.DicomImage open_image: image.DicomImage segments: list[Segment] _tolerance: float def __init__( self, image_paths: Sequence[str | BinaryIO | Path], ground=True, check_inversion=True, **kwargs, ): """ Parameters ---------- image_paths : iterable (list, tuple, etc) A sequence of paths to the image files. kwargs Passed to the image loading function. See :func:`~pylinac.core.image.load`. """ ground = kwargs.get("ground", False) or ground check_inversion = kwargs.get("check_inversion", False) or check_inversion if len(image_paths) != 2: raise ValueError("Exactly 2 images (open, DMLC) must be passed") image1, image2 = self._load_images(image_paths, ground=ground, **kwargs) if check_inversion: image1, image2 = self._check_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 | Path, **kwargs): """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. kwargs Passed to the constructor. """ with TemporaryZipDirectory(path) as tmpzip: image_files = image.retrieve_image_files(tmpzip) return cls(image_paths=image_files, **kwargs)
[docs] @classmethod def from_demo_images(cls, **kwargs): """Construct a VMAT instance using the demo images.""" demo_file = retrieve_demo_file(name=cls._url_suffix) return cls.from_zip(demo_file, **kwargs)
[docs] def analyze( self, tolerance: float | int = 1.5, segment_size_mm: tuple = (5, 100), roi_config: dict | None = None, ): """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. roi_config : dict A dict of the ROI settings. The keys are the names of the ROIs and each value is a dict containing the offset in mm 'offset_mm'. """ self._tolerance = tolerance / 100 self.roi_config = roi_config or self.default_roi_config """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[str | BytesIO], ground, **kwargs ) -> tuple[ImageLike, ImageLike]: image1 = image.load(image_paths[0], **kwargs) image2 = image.load(image_paths[1], **kwargs) if ground: image1.ground() image2.ground() return image1, image2 def _identify_images(self, image1: DicomImage, image2: DicomImage): """Identify which image is the DMLC and which is the open field.""" profile1, profile2 = self._median_profiles(image1=image1, image2=image2) field_profile1 = profile1.field_values() field_profile2 = profile2.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) -> VMATResult | dict: """Present the results data and metadata as a dataclass or dict. The default return type is a dataclass.""" segment_data = [] named_segment_data = {} for segment, (roi_name, roi_data) in zip( self.segments, self.roi_config.items() ): segment = SegmentResult( passed=segment.passed, r_corr=segment.r_corr, r_dev=segment.r_dev, center_x_y=segment.center.as_array(), x_position_mm=roi_data["offset_mm"], stdev=segment.stdev, ) segment_data.append(segment) named_segment_data[roi_name] = segment 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, named_segment_data=named_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 = round(dmlc_prof.center_idx) for roi_data in self.roi_config.values(): x_offset_mm = roi_data["offset_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, show_text: bool = True, **plt_kwargs: dict ): """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. show_text : bool Whether to show the ROI names on the image. plt_kwargs : dict Keyword args passed to the plt.subplots() method. Allows one to set things like figure size. """ fig, axes = plt.subplots(ncols=3, sharex=True, **plt_kwargs) 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, show_text=show_text ) 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: str | BytesIO, subimage: ImageType, show_text: bool, **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, show_text=show_text) plt.savefig(filename, **kwargs) def _plot_analyzed_subimage( self, subimage: ImageType, show: bool = True, ax: plt.Axes | None = None, show_text: bool = True, ): """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. show_text : bool Whether to show the ROI names on the image. """ 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, show_text) 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, show_text: bool): """Draw the segments onto a plot. Parameters ---------- axis : matplotlib.axes.Axes The plot to draw the objects on. show_text : bool Whether to show the ROI name on the image """ for segment, roi_name in zip(self.segments, self.roi_config.keys()): color = segment.get_bg_color() if show_text: text = f"{roi_name} : {segment.r_dev:2.2f}%" else: text = "" segment.plot2axes( axis, edgecolor=color, text=text, text_rotation=90, fontsize="small" ) @classmethod def _median_profiles( cls, image1: DicomImage, image2: DicomImage ) -> list[FWXMProfile, FWXMProfile]: """Return two median profiles from the open and DMLC image. Only used for visual purposes. Evaluation is not based on these profiles.""" profiles = [] for orig_img in (image1, image2): img = copy.deepcopy(orig_img) img.ground() img.check_inversion() profile = FWXMProfile( np.mean(img.array, axis=0), ground=True, normalization=Normalization.BEAM_CENTER, ) profile.stretch() norm_val = np.percentile(profile.values, 90) profile.normalize(norm_val) profiles.append(profile) return profiles
[docs] def publish_pdf( self, filename: str, notes: str = None, open_file: bool = False, metadata: dict | None = None, logo: Path | str | None = 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 -------------- logo: Path, str A custom logo to use in the PDF report. If nothing is passed, the default pylinac logo is used. """ canvas = PylinacCanvas( filename=filename, page_title=f"{self._result_short_header} VMAT Analysis", metadata=metadata, logo=logo, ) 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, show_text=True) 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: webbrowser.open(filename)
@staticmethod def _check_inversion(image1, image2): for img in (image1, image2): img.check_inversion() return image1, image2
[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" default_roi_config = { "ROI 1": {"offset_mm": -60}, "ROI 2": {"offset_mm": -40}, "ROI 3": {"offset_mm": -20}, "ROI 4": {"offset_mm": 0}, "ROI 5": {"offset_mm": 20}, "ROI 6": {"offset_mm": 40}, "ROI 7": {"offset_mm": 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" default_roi_config = { "ROI 1": {"offset_mm": -45}, "ROI 2": {"offset_mm": -15}, "ROI 3": {"offset_mm": 15}, "ROI 4": {"offset_mm": 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()