def test_pull_structure():
dicom_structure = dicom_dataset_from_dict({
"StructureSetROISequence": [
{
"ROIName": A_STRUCTURE.name,
"ROINumber": A_STRUCTURE.number
},
{
"ROIName": ANOTHER_STRUCTURE.name,
"ROINumber": ANOTHER_STRUCTURE.number,
},
],
"ROIContourSequence": [
# Sequence purposely placed in reverse order to ensure ROI
# number is being used and not list order.
create_contour_sequence_dict(ANOTHER_STRUCTURE),
create_contour_sequence_dict(A_STRUCTURE),
],
})
x, y, z = pull_structure(A_STRUCTURE.name, dicom_structure)
assert np.all(x == np.array(A_STRUCTURE.coords)[:, 0, :])
assert np.all(y == np.array(A_STRUCTURE.coords)[:, 1, :])
assert np.all(z == np.array(A_STRUCTURE.coords)[:, 2, :])
def align_cube_to_structure(
structure_name: str,
dcm_struct: pydicom.dataset.FileDataset,
quiet=False,
niter=10,
x0=None,
):
"""Align a cube to a dicom structure set.
Designed to allow arbitrary references frames within a dicom file
to be extracted via contouring a cube.
Parameters
----------
structure_name
The DICOM label of the cube structure
dcm_struct
The pydicom reference to the DICOM structure file.
quiet : ``bool``
Tell the function to not print anything. Defaults to False.
x0 : ``np.ndarray``, optional
A 3x3 array with each row defining a 3-D point in space.
These three points are used as initial conditions to search for
a cube that fits the contours. Choosing initial values close to
the structure set, and in the desired orientation will allow
consistent results. See examples within
`pymedphys.experimental.cubify`_ on what the
effects of each of the three points are on the resulting cube.
By default, this parameter is defined using the min/max values
of the contour structure.
Returns
-------
cube_definition_array
Four 3-D points the define the vertices of the cube.
vectors
The vectors between the points that can be used to traverse the cube.
Examples
--------
>>> import numpy as np
>>> import pydicom
>>> import pymedphys
>>> from pymedphys.experimental import align_cube_to_structure
>>>
>>> struct_path = str(pymedphys.data_path('example_structures.dcm'))
>>> dcm_struct = pydicom.dcmread(struct_path, force=True)
>>> structure_name = 'ANT Box'
>>> cube_definition_array, vectors = align_cube_to_structure(
... structure_name, dcm_struct, quiet=True, niter=1)
>>> np.round(cube_definition_array)
array([[-266., -31., 43.],
[-266., 29., 42.],
[-207., -31., 33.],
[-276., -31., -16.]])
>>>
>>> np.round(vectors, 1)
array([[ 0.7, 59.9, -0.5],
[ 59.2, -0.7, -9.7],
[ -9.7, -0.4, -59.2]])
"""
contours = pull_structure(structure_name, dcm_struct)
contour_points = contour_to_points(contours)
def to_minimise(cube):
cube_definition = cubify([tuple(cube[0:3]), tuple(cube[3:6]), tuple(cube[6::])])
min_dist_squared = calc_min_distance(cube_definition, contour_points)
return np.sum(min_dist_squared)
if x0 is None:
concatenated_contours = [
np.concatenate(contour_coord) for contour_coord in contours
]
bounds = [
(np.min(concatenated_contour), np.max(concatenated_contour))
for concatenated_contour in concatenated_contours
]
x0 = np.array(
[
(bounds[1][0], bounds[0][0], bounds[2][1]),
(bounds[1][0], bounds[0][1], bounds[2][1]),
(bounds[1][1], bounds[0][0], bounds[2][1]),
]
)
if quiet:
def print_fun(x, f, accepted): # pylint: disable = unused-argument
pass
else:
def print_fun(x, f, accepted): # pylint: disable = unused-argument
print("at minimum %.4f accepted %d" % (f, int(accepted)))
result = basinhopping(to_minimise, x0, callback=print_fun, niter=niter, stepsize=5)
cube = result.x
cube_definition = cubify([tuple(cube[0:3]), tuple(cube[3:6]), tuple(cube[6::])])
cube_definition_array = np.array([np.array(list(item)) for item in cube_definition])
vectors = [
cube_definition_array[1] - cube_definition_array[0],
cube_definition_array[2] - cube_definition_array[0],
cube_definition_array[3] - cube_definition_array[0],
]
return cube_definition_array, vectors