# Copyright (C) 2016-2019 Matthew Jennings and Simon Biggs
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version (the "AGPL-3.0+").
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# details.
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
# ADDITIONAL TERMS are also included as allowed by Section 7 of the GNU
# Affero General Public License. These additional terms are Sections 1, 5,
# 6, 7, 8, and 9 from the Apache License, Version 2.0 (the "Apache-2.0")
# where all references to the definition "License" are instead defined to
# mean the AGPL-3.0+.
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# program. If not, see <http://www.apache.org/licenses/LICENSE-2.0>.
"""A DICOM RT Dose toolbox"""
import numpy as np
from scipy.interpolate import RegularGridInterpolator
import matplotlib.pyplot as plt
from matplotlib import path
import pydicom
from .coords import xyz_axes_from_dataset
from .rtplan import get_surface_entry_point_with_fallback, require_gantries_be_zero
from .structure import pull_structure
# pylint: disable=C0103
[docs]def zyx_and_dose_from_dataset(dataset):
x, y, z = xyz_axes_from_dataset(dataset)
coords = (z, y, x)
dose = dose_from_dataset(dataset)
return coords, dose
def dose_from_dataset(ds, set_transfer_syntax_uid=True):
r"""Extract the dose grid from a DICOM RT Dose file.
"""
if set_transfer_syntax_uid:
ds.file_meta.TransferSyntaxUID = pydicom.uid.ImplicitVRLittleEndian
dose = ds.pixel_array * ds.DoseGridScaling
return dose
[docs]def dicom_dose_interpolate(interp_coords, dicom_dose_dataset: pydicom.Dataset):
"""Interpolates across a DICOM dose dataset.
Parameters
----------
interp_coords : tuple(z, y, x)
A tuple of coordinates in DICOM order, z axis first, then y, then x
where x, y, and z are DICOM axes.
dose : pydicom.Dataset
An RT DICOM Dose object
"""
interp_z = np.array(interp_coords[0], copy=False)[:, None, None]
interp_y = np.array(interp_coords[1], copy=False)[None, :, None]
interp_x = np.array(interp_coords[2], copy=False)[None, None, :]
coords, dicom_dose_dataset = zyx_and_dose_from_dataset(dicom_dose_dataset)
interpolation = RegularGridInterpolator(coords, dicom_dose_dataset)
try:
result = interpolation((interp_z, interp_y, interp_x))
except ValueError:
print(f"coords: {coords}")
raise
return result
[docs]def depth_dose(depths, dose_dataset: pydicom.Dataset, plan_dataset: pydicom.Dataset):
"""Interpolates dose for defined depths within a DICOM dose dataset.
Since the DICOM dose dataset is in CT coordinates the corresponding
DICOM plan is also required in order to calculate the conversion
between CT coordinate space and depth.
Currently, `depth_dose()` only supports a `dose_dataset` for which
the patient orientation is HFS and that any beams in `plan_dataset`
have gantry angle equal to 0 (head up). Depth is assumed to be
purely in the y axis direction in DICOM coordinates.
Parameters
----------
depths : numpy.ndarray
An array of depths to interpolate within the DICOM dose file. 0 is
defined as the surface of the phantom using either the
``SurfaceEntryPoint`` parameter or a combination of
``SourceAxisDistance``, ``SourceToSurfaceDistance``, and
``IsocentrePosition``.
dose_dataset : pydicom.dataset.Dataset
The RT DICOM dose dataset to be interpolated
plan_dataset : pydicom.dataset.Dataset
The RT DICOM plan used to extract surface parameters and verify gantry
angle 0 beams are used.
"""
require_patient_orientation_be_HFS(dose_dataset)
require_gantries_be_zero(plan_dataset)
depths = np.array(depths, copy=False)
surface_entry_point = get_surface_entry_point_with_fallback(plan_dataset)
depth_adjust = surface_entry_point.y
y = depths + depth_adjust
x, z = [surface_entry_point.x], [surface_entry_point.z]
coords = (z, y, x)
extracted_dose = np.squeeze(dicom_dose_interpolate(coords, dose_dataset))
return extracted_dose
[docs]def profile(
displacements,
depth,
direction,
dose_dataset: pydicom.Dataset,
plan_dataset: pydicom.Dataset,
):
"""Interpolates dose for cardinal angle horizontal profiles within a
DICOM dose dataset.
Since the DICOM dose dataset is in CT coordinates the corresponding
DICOM plan is also required in order to calculate the conversion
between CT coordinate space and depth and horizontal displacement.
Currently, `profile()` only supports a `dose_dataset` for which
the patient orientation is HFS and that any beams in `plan_dataset`
have gantry angle equal to 0 (head up). Depth is assumed to be
purely in the y axis direction in DICOM coordinates.
Parameters
----------
displacements : numpy.ndarray
An array of displacements to interpolate within the DICOM dose
file. 0 is defined in the DICOM z or x directions based either
upon the ``SurfaceEntryPoint`` or the ``IsocenterPosition``
depending on what is available within the DICOM plan file.
depth : float
The depth at which to interpolate within the DICOM dose file. 0 is
defined as the surface of the phantom using either the
``SurfaceEntryPoint`` parameter or a combination of
``SourceAxisDistance``, ``SourceToSurfaceDistance``, and
``IsocentrePosition``.
direction : str, one of ('inplane', 'inline', 'crossplane', 'crossline')
Corresponds to the axis upon which to apply the displacements.
- 'inplane' or 'inline' converts to DICOM z direction
- 'crossplane' or 'crossline' converts to DICOM x direction
dose_dataset : pydicom.dataset.Dataset
The RT DICOM dose dataset to be interpolated
plan_dataset : pydicom.dataset.Dataset
The RT DICOM plan used to extract surface and isocentre
parameters and verify gantry angle 0 beams are used.
"""
require_patient_orientation_be_HFS(dose_dataset)
require_gantries_be_zero(plan_dataset)
displacements = np.array(displacements, copy=False)
surface_entry_point = get_surface_entry_point_with_fallback(plan_dataset)
depth_adjust = surface_entry_point.y
y = [depth + depth_adjust]
if direction in ("inplane", "inline"):
coords = (displacements + surface_entry_point.z, y, [surface_entry_point.x])
elif direction in ("crossplane", "crossline"):
coords = ([surface_entry_point.z], y, displacements + surface_entry_point.x)
else:
raise ValueError(
"Expected direction to be equal to one of "
"'inplane', 'inline', 'crossplane', or 'crossline'"
)
extracted_dose = np.squeeze(dicom_dose_interpolate(coords, dose_dataset))
return extracted_dose
def _get_indices(z_list, z_val):
indices = np.array([item[0] for item in z_list])
# This will error if more than one contour exists on a given slice
desired_indices = np.where(indices == z_val)[0]
# Multiple contour sets per slice not yet implemented
return desired_indices
def get_dose_grid_structure_mask(structure_name, dcm_struct, dcm_dose):
x_dose, y_dose, z_dose = xyz_axes_from_dataset(dcm_dose)
xx_dose, yy_dose = np.meshgrid(x_dose, y_dose)
points = np.swapaxes(np.vstack([xx_dose.ravel(), yy_dose.ravel()]), 0, 1)
x_structure, y_structure, z_structure = pull_structure(structure_name, dcm_struct)
structure_z_values = np.array([item[0] for item in z_structure])
mask = np.zeros((len(y_dose), len(x_dose), len(z_dose)), dtype=bool)
for z_val in structure_z_values:
structure_indices = _get_indices(z_structure, z_val)
for structure_index in structure_indices:
dose_index = int(np.where(z_dose == z_val)[0])
assert z_structure[structure_index][0] == z_dose[dose_index]
structure_polygon = path.Path(
[
(x_structure[structure_index][i], y_structure[structure_index][i])
for i in range(len(x_structure[structure_index]))
]
)
mask[:, :, dose_index] = mask[:, :, dose_index] | (
structure_polygon.contains_points(points).reshape(
len(y_dose), len(x_dose)
)
)
return mask
def find_dose_within_structure(structure_name, dcm_struct, dcm_dose):
dose = dose_from_dataset(dcm_dose)
mask = get_dose_grid_structure_mask(structure_name, dcm_struct, dcm_dose)
return dose[mask]
def create_dvh(structure, dcm_struct, dcm_dose):
structure_dose_values = find_dose_within_structure(structure, dcm_struct, dcm_dose)
hist = np.histogram(structure_dose_values, 100)
freq = hist[0]
bin_edge = hist[1]
bin_mid = (bin_edge[1::] + bin_edge[:-1:]) / 2
cumulative = np.cumsum(freq[::-1])
cumulative = cumulative[::-1]
bin_mid = np.append([0], bin_mid)
cumulative = np.append(cumulative[0], cumulative)
percent_cumulative = cumulative / cumulative[0] * 100
plt.plot(bin_mid, percent_cumulative, label=structure)
plt.title("DVH")
plt.xlabel("Dose (Gy)")
plt.ylabel("Relative Volume (%)")
def require_patient_orientation_be_HFS(ds):
if not np.array_equal(ds.ImageOrientationPatient, np.array([1, 0, 0, 0, 1, 0])):
raise ValueError(
"The supplied dataset has a patient "
"orientation other than head-first supine."
)