def transform_axis(x, y, transform):
transformed_x = transform @ np.vstack(
[x, np.zeros(len(x)), np.ones(len(x))])
transformed_y = transform @ np.vstack(
[np.zeros(len(y)), y, np.ones(len(y))])
return transformed_x[0:2], transformed_y[0:2]
def merge_view_horz(volume, dx, dy):
junctions = []
# creating merged volume
merge_vol = np.zeros((volume.shape[0], volume.shape[1]))
# creating vector for processing along cols (one row)
amplitude = np.zeros(
(volume.shape[0],
volume.shape[2])) # 1 if it is vertical 0 if the bars are horizontal
y = np.linspace(0,
0 + (volume.shape[0] * dy),
volume.shape[0],
endpoint=False) # definition of the distance axis
# x = np.arange(0,) #definition of the distance axis
# merging the two images together
ampl_resamp = np.zeros(((volume.shape[0]) * 10, volume.shape[2]))
# amp_peak = np.zeros((volume.shape[0]) * 10)
for item in tqdm(range(0, volume.shape[2])):
merge_vol = merge_vol + volume[:, :, item]
amplitude[:, item] = volume[:, int(volume.shape[1] / 2), item]
ampl_resamp[:, item] = signal.resample(
amplitude[:, item],
int(len(amplitude)) * 10) # resampling the amplitude vector
# amp_peak = amp_peak + ampl_resamp[:, item] / volume.shape[2]
fig, ax = plt.subplots(nrows=2, squeeze=True, figsize=(6, 8))
extent = (0, 0 + (volume.shape[1] * dx), 0, 0 + (volume.shape[0] * dy))
ax[0].imshow(merge_vol, extent=extent, aspect="auto")
ax[0].set_xlabel("x distance [mm]")
ax[0].set_ylabel("y distance [mm]")
ax[1].plot(y, amplitude, label="Amplitude profile")
ax[1].set_ylabel("amplitude")
ax[1].set_xlabel("y distance [mm]")
ax[1].legend()
fig.suptitle("Merged volume", fontsize=16)
# peaks, peak_type, peak_figs = peak_find(ampl_resamp, dy)
peaks, peak_type, peak_figs = pf.peak_find(ampl_resamp, dy)
# junction_figs = minimize_junction_Y(ampl_resamp, peaks, peak_type, dy / 10)
junction_figs = minY.minimize_junction_Y(ampl_resamp, peaks, peak_type,
dy / 10)
junctions.append(junction_figs)
return fig, peak_figs, junctions
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 = matplotlib.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 calculate_anti_aliased_mask(contours, dcm_ct, expansion=5):
transformation_params = get_image_transformation_parameters(dcm_ct)
dx, dy, Cx, Cy, Ox, Oy = transformation_params
x_grid, y_grid, ct_size = get_grid(
dcm_ct, transformation_params=transformation_params)
new_ct_size = np.array(ct_size) * expansion
expanded_mask = np.zeros(new_ct_size)
for xyz in contours:
x = np.array(xyz[0::3])
y = np.array(xyz[1::3])
z = xyz[2::3]
assert len(set(z)) == 1
r = (((y - Cy) / dy * Oy)) * expansion + (expansion - 1) * 0.5
c = (((x - Cx) / dx * Ox)) * expansion + (expansion - 1) * 0.5
expanded_mask = np.logical_or(
expanded_mask,
skimage.draw.polygon2mask(new_ct_size, np.array(list(zip(r, c)))),
)
mask = reduce_expanded_mask(expanded_mask, ct_size[0], expansion)
mask = 2 * mask - 1
return x_grid, y_grid, mask
def calculate_expanded_mask(contours, dcm_ct, expansion):
dx, dy, Cx, Cy, Ox, Oy = get_image_transformation_parameters(dcm_ct)
ct_size = np.shape(dcm_ct.pixel_array)
new_ct_size = np.array(ct_size) * expansion
expanded_mask = np.zeros(new_ct_size)
for xyz in contours:
x = np.array(xyz[0::3])
y = np.array(xyz[1::3])
z = xyz[2::3]
if len(set(z)) != 1:
raise ValueError("Expected only one z value for a given contour")
r = (((y - Cy) / dy * Oy)) * expansion + (expansion - 1) * 0.5
c = (((x - Cx) / dx * Ox)) * expansion + (expansion - 1) * 0.5
expanded_mask = np.logical_or(
expanded_mask,
skimage.draw.polygon2mask(new_ct_size, np.array(list(zip(r, c)))),
)
return expanded_mask
def _profile(self):
"""The actual profile array; private attr that is passed to MultiProfile."""
profile = np.zeros(len(self._multi_x_locations[0]))
for _, x, y in zip(
self._radii, self._multi_x_locations, self._multi_y_locations
):
profile += scipy.ndimage.map_coordinates(self.image_array, [y, x], order=0)
profile /= self.num_profiles
return profile
def create_output_mask(dcm_ct, contours_by_ct_uid, structure, ct_uid, expansion=5):
_, _, ct_size = mask.get_grid(dcm_ct)
contours_on_this_slice = contours_by_ct_uid[ct_uid].keys()
if structure in contours_on_this_slice:
original_contours = contours_by_ct_uid[ct_uid][structure]
_, _, calculated_mask = mask.calculate_anti_aliased_mask(
original_contours, dcm_ct, expansion=expansion
)
else:
calculated_mask = np.zeros(ct_size) - 1
return calculated_mask
def calc_mu_density(
mu,
mlc,
jaw,
grid_resolution=None,
max_leaf_gap=None,
leaf_pair_widths=None,
min_step_per_pixel=None,
):
"""Determine the MU Density.
Both jaw and mlc positions are defined in bipolar format for each control
point. A negative value indicates travel over the isocentre. All positional
arguments are defined at the isocentre projection with the units of mm.
Parameters
----------
mu : numpy.ndarray
1-D array containing an MU value for each control point.
mlc : numpy.ndarray
3-D array containing the MLC positions
| axis 0: control point
| axis 1: mlc pair
| axis 2: leaf bank
jaw : numpy.ndarray
2-D array containing the jaw positions.
| axis 0: control point
| axis 1: diaphragm
grid_resolution : float, optional
The calc grid resolution. Defaults to 1 mm.
max_leaf_gap : float, optional
The maximum possible distance between opposing leaves. Defaults to
400 mm.
leaf_pair_widths : tuple, optional
The widths of each leaf pair in the
MLC limiting device. The number of entries in the tuples defines
the number of leaf pairs. Each entry itself defines that particular
leaf pair width. Defaults to 80 leaf pairs each 5 mm wide.
min_step_per_pixel : int, optional
The minimum number of time steps
used per pixel for each control point. Defaults to 10.
Returns
-------
mu_density : numpy.ndarray
2-D array containing the calculated mu density.
| axis 0: jaw direction
| axis 1: mlc direction
Examples
--------
>>> import numpy as np
>>> import pymedphys
>>>
>>> leaf_pair_widths = (5, 5, 5)
>>> max_leaf_gap = 10
>>> mu = np.array([0, 2, 5, 10])
>>> mlc = np.array([
... [
... [1, 1],
... [2, 2],
... [3, 3]
... ],
... [
... [2, 2],
... [3, 3],
... [4, 4]
... ],
... [
... [-2, 3],
... [-2, 4],
... [-2, 5]
... ],
... [
... [0, 0],
... [0, 0],
... [0, 0]
... ]
... ])
>>> jaw = np.array([
... [7.5, 7.5],
... [7.5, 7.5],
... [-2, 7.5],
... [0, 0]
... ])
>>>
>>> grid = pymedphys.mudensity.grid(
... max_leaf_gap=max_leaf_gap, leaf_pair_widths=leaf_pair_widths)
>>> grid['mlc']
array([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5.])
>>>
>>> grid['jaw']
array([-8., -7., -6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4.,
5., 6., 7., 8.])
>>>
>>> mu_density = pymedphys.mudensity.calculate(
... mu, mlc, jaw, max_leaf_gap=max_leaf_gap,
... leaf_pair_widths=leaf_pair_widths)
>>> pymedphys.mudensity.display(grid, mu_density)
>>>
>>> np.round(mu_density, 1)
array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0.3, 1.9, 2.2, 1.9, 0.4, 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.2, 2.5, 2.2, 0.6, 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.4, 2.8, 2.5, 0.8, 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.5, 3.1, 2.8, 1. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.5, 3.4, 3.1, 1.3, 0. , 0. , 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.7, 3.7, 3.5, 1.6, 0. , 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.8, 4. , 3.8, 1.9, 0.1, 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.8, 4.3, 4.1, 2.3, 0.1, 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.9, 5.2, 4.7, 2.6, 0.2, 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.8, 5.4, 6.6, 3.8, 0.5, 0. ],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 5.1, 7.5, 6.7, 3.9, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.7, 6.9, 6.7, 3.9, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.5, 6.3, 6.4, 3.9, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.5, 5.6, 5.7, 3.8, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.5, 5.1, 5.1, 3.3, 0.5],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
MU Density from a Mosaiq record
>>> import pymedphys
>>>
>>> def mu_density_from_mosaiq(msq_server_name, field_id):
... with pymedphys.mosaiq.connect(msq_server_name) as cursor:
... delivery = pymedphys.Delivery.from_mosaiq(cursor, field_id)
...
... grid = pymedphys.mudensity.grid()
... mu_density = delivery.mudensity()
... pymedphys.mudensity.display(grid, mu_density)
>>>
>>> mu_density_from_mosaiq('a_server_name', 11111) # doctest: +SKIP
MU Density from a logfile at a given filepath
>>> import pymedphys
>>>
>>> def mu_density_from_logfile(filepath):
... delivery_data = Delivery.from_logfile(filepath)
... mu_density = Delivery.mudensity()
...
... grid = pymedphys.mudensity.grid()
... pymedphys.mudensity.display(grid, mu_density)
>>>
>>> mu_density_from_logfile(r"a/path/goes/here") # doctest: +SKIP
"""
if grid_resolution is None:
grid_resolution = __DEFAULT_GRID_RESOLUTION
if max_leaf_gap is None:
max_leaf_gap = __DEFAULT_MAX_LEAF_GAP
if leaf_pair_widths is None:
leaf_pair_widths = __DEFAULT_LEAF_PAIR_WIDTHS
if min_step_per_pixel is None:
min_step_per_pixel = __DEFAULT_MIN_STEP_PER_PIXEL
divisibility_of_max_leaf_gap = np.array(max_leaf_gap / 2 / grid_resolution)
max_leaf_gap_is_divisible = (
divisibility_of_max_leaf_gap.astype(int) == divisibility_of_max_leaf_gap
)
if not max_leaf_gap_is_divisible:
raise ValueError(
"The grid resolution needs to be able to divide the max leaf gap exactly by"
" four"
)
leaf_pair_widths = np.array(leaf_pair_widths)
if not np.max(np.abs(mlc)) <= max_leaf_gap / 2: # pylint: disable = unneeded-not
first_failing_control_point = np.where(np.abs(mlc) > max_leaf_gap / 2)[0][0]
raise ValueError(
"The mlc should not travel further out than half the maximum leaf gap.\n"
"The first failing control point has the following positions:\n"
f"{np.array(mlc)[first_failing_control_point, :, :]}"
)
mu, mlc, jaw = remove_irrelevant_control_points(mu, mlc, jaw)
full_grid = get_grid(max_leaf_gap, grid_resolution, leaf_pair_widths)
mu_density = np.zeros((len(full_grid["jaw"]), len(full_grid["mlc"])))
for i in range(len(mu) - 1):
control_point_slice = slice(i, i + 2, 1)
current_mlc = mlc[control_point_slice, :, :]
current_jaw = jaw[control_point_slice, :]
delivered_mu = np.diff(mu[control_point_slice])
grid, mu_density_of_slice = calc_single_control_point(
current_mlc,
current_jaw,
delivered_mu,
leaf_pair_widths=leaf_pair_widths,
grid_resolution=grid_resolution,
min_step_per_pixel=min_step_per_pixel,
)
full_grid_mu_density_of_slice = _convert_to_full_grid(
grid, full_grid, mu_density_of_slice
)
mu_density += full_grid_mu_density_of_slice
return mu_density
def convert_dose(plan, export_path):
# Check that the plan has a primary image, as we can't create a meaningful RTDOSE without it:
if not plan.primary_image:
plan.logger.error("No primary image found for plan. Unable to generate RTDOSE.")
return
patient_info = plan.pinnacle.patient_info
plan_info = plan.plan_info
trial_info = plan.trial_info
image_info = plan.primary_image.image_info[0]
patient_position = plan.patient_position
# Get the UID for the Dose and the Plan
doseInstanceUID = plan.dose_inst_uid
planInstanceUID = plan.plan_inst_uid
# Populate required values for file meta information
file_meta = pydicom.dataset.Dataset()
file_meta.MediaStorageSOPClassUID = RTDoseSOPClassUID
file_meta.TransferSyntaxUID = GTransferSyntaxUID
file_meta.MediaStorageSOPInstanceUID = doseInstanceUID
file_meta.ImplementationClassUID = GImplementationClassUID
# Create the pydicom.dataset.FileDataset instance (initially no data elements, but
# file_meta supplied)
RDfilename = f"RD.{file_meta.MediaStorageSOPInstanceUID}.dcm"
ds = pydicom.dataset.FileDataset(
RDfilename, {}, file_meta=file_meta, preamble=b"\x00" * 128
)
ds.SpecificCharacterSet = "ISO_IR 100"
ds.InstanceCreationDate = time.strftime("%Y%m%d")
ds.InstanceCreationTime = time.strftime("%H%M%S")
ds.SOPClassUID = RTDoseSOPClassUID # RT Dose Storage
ds.SOPInstanceUID = doseInstanceUID
datetimesplit = plan_info["ObjectVersion"]["WriteTimeStamp"].split()
# Read more accurate date from trial file if it is available
trial_info = plan.trial_info
if trial_info:
datetimesplit = trial_info["ObjectVersion"]["WriteTimeStamp"].split()
ds.StudyDate = datetimesplit[0].replace("-", "")
ds.StudyTime = datetimesplit[1].replace(":", "")
ds.AccessionNumber = ""
ds.Modality = RTDOSEModality
ds.Manufacturer = Manufacturer
ds.OperatorsName = ""
ds.ManufacturerModelName = plan_info["ToolType"]
ds.SoftwareVersions = [plan_info["PinnacleVersionDescription"]]
ds.PhysiciansOfRecord = patient_info["RadiationOncologist"]
ds.PatientName = patient_info["FullName"]
ds.PatientBirthDate = patient_info["DOB"]
ds.PatientID = patient_info["MedicalRecordNumber"]
ds.PatientSex = patient_info["Gender"][0]
ds.SliceThickness = trial_info["DoseGrid .VoxelSize .Z"] * 10
ds.SeriesInstanceUID = doseInstanceUID
ds.InstanceNumber = "1"
ds.StudyInstanceUID = image_info["StudyInstanceUID"]
ds.FrameOfReferenceUID = image_info["FrameUID"]
ds.StudyID = plan.primary_image.image["StudyID"]
# Assume zero struct shift for now (may not the case for versions below Pinnacle 9)
if patient_position in ("HFP", "FFS"):
dose_origin_x = -trial_info["DoseGrid .Origin .X"] * 10
elif patient_position in ("HFS", "FFP"):
dose_origin_x = trial_info["DoseGrid .Origin .X"] * 10
if patient_position in ("HFS", "FFS"):
dose_origin_y = -trial_info["DoseGrid .Origin .Y"] * 10
elif patient_position in ("HFP", "FFP"):
dose_origin_y = trial_info["DoseGrid .Origin .Y"] * 10
if patient_position in ("HFS", "HFP"):
dose_origin_z = -trial_info["DoseGrid .Origin .Z"] * 10
elif patient_position in ("FFS", "FFP"):
dose_origin_z = trial_info["DoseGrid .Origin .Z"] * 10
# Image Position (Patient) seems off, so going to calculate shift assuming
# dose origin in center and I want outer edge
ydoseshift = (
trial_info["DoseGrid .VoxelSize .Y"] * 10 * trial_info["DoseGrid .Dimension .Y"]
- trial_info["DoseGrid .VoxelSize .Y"] * 10
)
zdoseshift = (
trial_info["DoseGrid .VoxelSize .Z"] * 10 * trial_info["DoseGrid .Dimension .Z"]
- trial_info["DoseGrid .VoxelSize .Z"] * 10
)
if patient_position == "HFS":
ds.ImagePositionPatient = [
dose_origin_x,
dose_origin_y - ydoseshift,
dose_origin_z - zdoseshift,
]
elif patient_position == "HFP":
ds.ImagePositionPatient = [
dose_origin_x,
dose_origin_y + ydoseshift,
dose_origin_z - zdoseshift,
]
elif patient_position == "FFS":
ds.ImagePositionPatient = [
dose_origin_x,
dose_origin_y - ydoseshift,
dose_origin_z + zdoseshift,
]
elif patient_position == "FFP":
ds.ImagePositionPatient = [
dose_origin_x,
dose_origin_y + ydoseshift,
dose_origin_z + zdoseshift,
]
# Read this from CT DCM if available?
if "HFS" in patient_position or "FFS" in patient_position:
ds.ImageOrientationPatient = [1.0, 0.0, 0.0, 0.0, 1.0, -0.0]
elif "HFP" in patient_position or "FFP" in patient_position:
ds.ImageOrientationPatient = [-1.0, 0.0, 0.0, 0.0, -1.0, -0.0]
# Read this from CT DCM if available
ds.PositionReferenceIndicator = ""
ds.SamplesPerPixel = 1
ds.PhotometricInterpretation = "MONOCHROME2"
ds.NumberOfFrames = int(
trial_info["DoseGrid .Dimension .Z"]
) # is this Z dimension???
# Using y for Rows because that's what's in the exported dicom file for
# test patient
ds.Rows = int(trial_info["DoseGrid .Dimension .Y"])
ds.Columns = int(trial_info["DoseGrid .Dimension .X"])
ds.PixelSpacing = [
trial_info["DoseGrid .VoxelSize .X"] * 10,
trial_info["DoseGrid .VoxelSize .Y"] * 10,
]
ds.BitsAllocated = 16
ds.BitsStored = 16
ds.HighBit = 15
ds.PixelRepresentation = 0
ds.DoseUnits = "GY"
ds.DoseType = "PHYSICAL"
ds.DoseSummationType = "PLAN"
# Since DoseSummationType is PLAN, only need to reference RTPLAN here, no need to
# reference fraction group.
ds.ReferencedRTPlanSequence = pydicom.sequence.Sequence()
ds.ReferencedRTPlanSequence.append(pydicom.dataset.Dataset())
ds.ReferencedRTPlanSequence[0].ReferencedSOPClassUID = RTPlanSOPClassUID
ds.ReferencedRTPlanSequence[0].ReferencedSOPInstanceUID = planInstanceUID
ds.TissueHeterogeneityCorrection = "IMAGE"
grid_frame_offset_vector = []
for p in range(0, int(trial_info["DoseGrid .Dimension .Z"])):
grid_frame_offset_vector.append(
p * float(trial_info["DoseGrid .VoxelSize .X"] * 10)
)
ds.GridFrameOffsetVector = grid_frame_offset_vector
# Array in which to sum the dose values of all beams
summed_pixel_values = []
# For each beam in the trial, convert the dose from the Pinnacle binary
# file and sum together
beam_list = trial_info["BeamList"] if trial_info["BeamList"] else []
if len(beam_list) == 0:
plan.logger.warning("No Beams found in Trial. Unable to generate RTDOSE.")
return
for beam in beam_list:
plan.logger.info("Exporting Dose for beam: %s", beam["Name"])
# Get the binary file for this beam
binary_id = re.findall("\\d+", beam["DoseVolume"])[0]
filled_binary_id = str(binary_id).zfill(3)
binary_file = os.path.join(plan.path, f"plan.Trial.binary.{filled_binary_id}")
# Get the prescription for this beam (need this for number of fractions)
prescription = [
p
for p in trial_info["PrescriptionList"]
if p["Name"] == beam["PrescriptionName"]
][0]
# Get the prescription point
plan.logger.debug("PrescriptionPointName: %s", beam["PrescriptionPointName"])
points = plan.points
prescription_point = []
for p in points:
if p["Name"] == beam["PrescriptionPointName"]:
plan.logger.debug(
"Presc Point: %s %s %s %s",
p["Name"],
p["XCoord"],
p["YCoord"],
p["ZCoord"],
)
prescription_point = plan.convert_point(p)
break
if len(prescription_point) < 3:
plan.logger.warning(
"No valid prescription point found for beam! Beam will be ignored for "
"Dose conversion. Dose will most likely be incorrect"
)
continue
plan.logger.debug("Presc Point Dicom: %s, %s", p["Name"], prescription_point)
total_prescription = (
beam["MonitorUnitInfo"]["PrescriptionDose"]
* prescription["NumberOfFractions"]
)
plan.logger.debug("Total Prescription %s", total_prescription)
# Read the dose into a grid, so that we can interpolate for the prescription
# point and determine the MU for the grid
dose_grid = np.zeros(
(
trial_info["DoseGrid .Dimension .X"],
trial_info["DoseGrid .Dimension .Y"],
trial_info["DoseGrid .Dimension .Z"],
)
)
spacing = [
trial_info["DoseGrid .VoxelSize .X"] * 10,
trial_info["DoseGrid .VoxelSize .Y"] * 10,
trial_info["DoseGrid .VoxelSize .Z"] * 10,
]
origin = [
ds.ImagePositionPatient[0],
ds.ImagePositionPatient[1],
ds.ImagePositionPatient[2],
]
if os.path.isfile(binary_file):
with open(binary_file, "rb") as b:
for z in range(trial_info["DoseGrid .Dimension .Z"] - 1, -1, -1):
for y in range(0, trial_info["DoseGrid .Dimension .Y"]):
for x in range(0, trial_info["DoseGrid .Dimension .X"]):
data_element = b.read(4)
value = struct.unpack(">f", data_element)[0]
dose_grid[x, y, z] = value
else:
plan.logger.warning("Dose file not found")
plan.logger.error("Skipping generating RTDOSE")
return
# Get the index within that grid of the dose reference point
idx = [0.0, 0.0, 0.0]
for i in range(3):
idx[i] = -(origin[i] - prescription_point[i]) / spacing[i]
plan.logger.debug("Index of prescription point within grid: %s", idx)
# Trilinear interpolation of that point within the dose grid
cgy_mu = trilinear_interpolation(idx, dose_grid)
plan.logger.debug("cgy_mu: %s", cgy_mu)
# Now that we have the cgy/mu value of the dose reference point, we can
# extract an accurate value for MU
beam_mu = (total_prescription / cgy_mu) / prescription["NumberOfFractions"]
plan.logger.debug("Beam MU: %s", beam_mu)
pixel_data_list = []
for z in range(trial_info["DoseGrid .Dimension .Z"] - 1, -1, -1):
for y in range(0, trial_info["DoseGrid .Dimension .Y"]):
for x in range(0, trial_info["DoseGrid .Dimension .X"]):
value = (
float(prescription["NumberOfFractions"])
* dose_grid[x, y, z]
* beam_mu
/ 100
)
pixel_data_list.append(value)
ds.FrameIncrementPointer = ds.data_element("GridFrameOffsetVector").tag
main_pix_array = []
for h in range(0, trial_info["DoseGrid .Dimension .Z"]):
pixelsforframe = []
for k in range(
0,
trial_info["DoseGrid .Dimension .X"]
* trial_info["DoseGrid .Dimension .Y"],
):
pixelsforframe.append(
float(
pixel_data_list[
h
* trial_info["DoseGrid .Dimension .Y"]
* trial_info["DoseGrid .Dimension .X"]
+ k
]
)
)
main_pix_array = main_pix_array + list(reversed(pixelsforframe))
main_pix_array = list(reversed(main_pix_array))
# Add the values from this beam to the summed values
if len(summed_pixel_values) == 0:
summed_pixel_values = main_pix_array
else:
for i, values in enumerate(summed_pixel_values):
summed_pixel_values[i] = values + main_pix_array[i]
# Compute the scaling factor
scale = max(summed_pixel_values) / 16384
ds.DoseGridScaling = scale
plan.logger.debug("Dose Grid Scaling: %s", ds.DoseGridScaling)
pixel_binary_block = bytes()
# Scale by the scaling factor
pixelvaluelist = []
for _, element in enumerate(summed_pixel_values, 0):
if scale != 0:
element = round(element / scale)
else:
element = 0
pixelvaluelist.append(int(element))
# Set the PixelData
pixel_binary_block = struct.pack("%sh" % len(pixelvaluelist), *pixelvaluelist)
ds.PixelData = pixel_binary_block
# Save the RTDose Dicom File
output_file = os.path.join(export_path, RDfilename)
plan.logger.info("Creating Dose file: %s", output_file)
ds.save_as(output_file)
def read_dicom3D(direc, i_option):
# item = 0
for subdir, dirs, files in os.walk(direc): # pylint: disable = unused-variable
k = 0
for file in tqdm(sorted(files)):
# print('filename=', file)
if os.path.splitext(file)[1] == ".dcm":
dataset = pydicom.dcmread(direc + file)
if k == 0:
ArrayDicom = np.zeros(
(dataset.Rows, dataset.Columns, 0),
dtype=dataset.pixel_array.dtype,
)
tmp_array = dataset.pixel_array
if i_option.startswith(("y", "yeah", "yes")):
max_val = np.amax(tmp_array)
tmp_array = tmp_array / max_val
min_val = np.amin(tmp_array)
tmp_array = tmp_array - min_val
tmp_array = 1 - tmp_array # inverting the range
# min_val = np.amin(tmp_array) # normalizing
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array))
tmp_array = u.norm01(tmp_array)
else:
# min_val = np.amin(tmp_array)
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array))
tmp_array = u.norm01(tmp_array) # just normalize
ArrayDicom = np.dstack((ArrayDicom, tmp_array))
# print("item thickness [mm]=", dataset.SliceThickness)
SID = dataset.RTImageSID
dx = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[0]) / 1000)
dy = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[1]) / 1000)
print("pixel spacing row [mm]=", dx)
print("pixel spacing col [mm]=", dy)
else:
tmp_array = dataset.pixel_array
if i_option.startswith(("y", "yeah", "yes")):
max_val = np.amax(tmp_array)
tmp_array = tmp_array / max_val
min_val = np.amin(tmp_array)
tmp_array = tmp_array - min_val
tmp_array = 1 - tmp_array # inverting the range
# min_val = np.amin(tmp_array) # normalizing
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array))
tmp_array = u.norm01(tmp_array)
else:
# min_val = np.amin(tmp_array)
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array)) # just normalize
tmp_array = u.norm01(tmp_array)
ArrayDicom = np.dstack((ArrayDicom, tmp_array))
k = k + 1
xfield, yfield, rotfield = image_analyze(ArrayDicom, i_option)
multi_slice_viewer(ArrayDicom, dx, dy)
if np.shape(xfield)[2] == 2:
fig, peak_figs, junctions_figs = merge_view_vert(xfield, dx, dy)
with PdfPages(direc + "jaws_X_report.pdf") as pdf:
pdf.savefig(fig)
# for i in range(0, len(peak_figs)):
for _, f in enumerate(peak_figs):
pdf.savefig(f)
# for i in range(0, len(junctions_figs)):
for _, f in enumerate(junctions_figs):
pdf.savefig(f)
plt.close()
else:
print(
"X jaws data analysis not completed please verify that you have two X jaws images. For more information see manual."
)
if np.shape(yfield)[2] == 4:
fig, peak_figs, junctions_figs = merge_view_horz(yfield, dx, dy)
# print('peak_figs********************************************************=', len(peak_figs),peak_figs)
with PdfPages(direc + "jaws_Y_report.pdf") as pdf:
pdf.savefig(fig)
# for i in range(0, len(peak_figs)):
for _, f in enumerate(peak_figs):
pdf.savefig(f)
for _, f in enumerate(junctions_figs):
pdf.savefig(f)
plt.close()
else:
print(
"Y jaws data analysis not completed please verify that you have four Y jaws images. For more information see manual."
)
if np.shape(rotfield)[2] == 4:
fig, peak_figs, junctions_figs = merge_view_filtrot(rotfield, dx, dy)
with PdfPages(direc + "jaws_FR_report.pdf") as pdf:
pdf.savefig(fig)
for _, f in enumerate(peak_figs):
pdf.savefig(f)
for _, f in enumerate(junctions_figs):
pdf.savefig(f)
plt.close()
else:
print(
"Field rotation data analysis not completed please verify that you have four field rotation images. For more information see manual."
)
def merge_view_filtrot(volume, dx, dy):
volume_resort = np.copy(
volume
) # this will hold the resorted volume 0 to 3 clockwise
junctions_comb = []
peaks_figs_comb = []
# we need to create 4 matches
# 0,1,2,3 will be tagged top left, top right, bottom right, bottom left
for i in range(0, int(np.shape(volume)[2])):
diag_stack = [
0,
0,
0,
0,
] # we will sum along one direction whichever is biggest will tag the file
for j in range(0, int(min([np.shape(volume)[0], np.shape(volume)[1]]) / 2)):
# print('j=',j,int(np.shape(volume)[0] / 2)+j, int(np.shape(volume)[1] / 2)+j)
diag_stack[0] = (
diag_stack[0]
+ volume[
int(np.shape(volume)[0] / 2) - j,
int(np.shape(volume)[1] / 2) - j,
i,
]
)
diag_stack[1] = (
diag_stack[1]
+ volume[
int(np.shape(volume)[0] / 2) - j,
int(np.shape(volume)[1] / 2) + j,
i,
]
)
diag_stack[2] = (
diag_stack[2]
+ volume[
int(np.shape(volume)[0] / 2) + j,
int(np.shape(volume)[1] / 2) + j,
i,
]
)
diag_stack[3] = (
diag_stack[3]
+ volume[
int(np.shape(volume)[0] / 2) + j,
int(np.shape(volume)[1] / 2) - j,
i,
]
)
volume_resort[:, :, np.argmax(diag_stack)] = volume[:, :, i]
# creating merged volumes
merge_vol = np.zeros((volume_resort.shape[0], volume_resort.shape[1]))
# creating vector for processing (1 horizontal & 1 vertical)
amplitude_horz = np.zeros(
(volume_resort.shape[1], volume_resort.shape[2])
) # 1 if it is vertical 0 if the bars are horizontal
amplitude_vert = np.zeros((volume_resort.shape[0], volume_resort.shape[2]))
# y = np.linspace(0, 0 + (volume_resort.shape[0] * dy), volume_resort.shape[0],
# endpoint=False) # definition of the distance axis
# x = np.linspace(0, 0 + (volume_resort.shape[1] * dy), volume_resort.shape[1],
# endpoint=False) # definition of the distance axis
ampl_resamp_y1 = np.zeros(
((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
)
ampl_resamp_y2 = np.zeros(
((volume_resort.shape[0]) * 10, int(volume_resort.shape[2] / 2))
)
ampl_resamp_x1 = np.zeros(
((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
)
ampl_resamp_x2 = np.zeros(
((volume_resort.shape[1]) * 10, int(volume_resort.shape[2] / 2))
)
amplitude_horz[:, 0] = volume_resort[
int(volume_resort.shape[0] / 3.25), :, 0
] # for profile 1
amplitude_horz[:, 1] = volume_resort[
int(volume_resort.shape[0] / 3.25), :, 1
] # for profile 1
amplitude_horz[:, 3] = volume_resort[
int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 2
] # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error #for profile 2
amplitude_horz[:, 2] = volume_resort[
int(volume_resort.shape[0]) - int(volume_resort.shape[0] / 3.25), :, 3
]
amplitude_vert[:, 0] = volume_resort[
:, int(volume_resort.shape[1]) - int(volume_resort.shape[1] / 2.8), 1
] # the numbers here are reversed because we are going to slide the second graph (the overlay) to minimize the error #for profile 3
amplitude_vert[:, 1] = volume_resort[
:, int(volume_resort.shape[1]) - int(volume_resort.shape[1] / 2.8), 2
]
amplitude_vert[:, 3] = volume_resort[
:, int(volume_resort.shape[1] / 2.8), 3
] # for profile 4
amplitude_vert[:, 2] = volume_resort[:, int(volume_resort.shape[1] / 2.8), 0]
plt.figure()
for item in tqdm(range(0, int(volume.shape[2] / 2))):
merge_vol = merge_vol + volume[:, :, item]
data_samp = amplitude_vert[:, item]
ampl_resamp_y1[:, item] = signal.resample(
data_samp, int(np.shape(amplitude_vert)[0]) * 10
)
data_samp = amplitude_horz[:, item]
ampl_resamp_x1[:, item] = signal.resample(
data_samp, int(np.shape(amplitude_horz)[0]) * 10
)
for item in tqdm(range(int(volume.shape[2] / 2), volume.shape[2])):
merge_vol = merge_vol + volume[:, :, item]
data_samp = amplitude_vert[:, item]
ampl_resamp_y2[:, item - int(volume.shape[2] / 2)] = signal.resample(
data_samp, int(np.shape(amplitude_vert)[0]) * 10
)
data_samp = amplitude_horz[:, item]
ampl_resamp_x2[:, item - int(volume.shape[2] / 2)] = signal.resample(
data_samp, int(np.shape(amplitude_horz)[0]) * 10
)
fig, ax = plt.subplots(ncols=1, nrows=1, squeeze=True, figsize=(6, 8))
extent = (0, 0 + (volume.shape[1] * dx), 0, 0 + (volume.shape[0] * dy))
ax.imshow(merge_vol, extent=extent, aspect="auto")
ax.set_aspect("equal", "box")
ax.set_xlabel("x distance [mm]")
ax.set_ylabel("y distance [mm]")
fig.suptitle("Merged volume", fontsize=16)
ax.hlines(dy * int(volume_resort.shape[0] / 3.25), 0, dx * volume_resort.shape[1])
ax.text(
dx * int(volume_resort.shape[1] / 2.25),
dy * int(volume_resort.shape[0] / 3),
"Profile 2",
)
ax.hlines(
dy * int(volume_resort.shape[0]) - dy * int(volume_resort.shape[0] / 3.25),
0,
dx * volume_resort.shape[1],
)
ax.text(
dx * int(volume_resort.shape[1] / 2.25),
dy * int(volume_resort.shape[0]) - dy * int(volume_resort.shape[0] / 3.5),
"Profile 1",
)
ax.vlines(dx * int(volume_resort.shape[1] / 2.8), 0, dy * volume_resort.shape[0])
ax.text(
dx * int(volume_resort.shape[1] / 3.1),
dy * int(volume_resort.shape[0] / 1.8),
"Profile 4",
rotation=90,
)
ax.vlines(
dx * int(volume_resort.shape[1]) - dx * int(volume_resort.shape[1] / 2.8),
0,
dy * volume_resort.shape[0],
)
ax.text(
dx * int(volume_resort.shape[1]) - dx * int(volume_resort.shape[1] / 2.9),
dy * int(volume_resort.shape[0] / 1.8),
"Profile 3",
rotation=90,
)
# plt.show()
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_x1, dx, "Profile 1"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_x1, peaks, peak_type, dx / 10, "Profile 1"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_x2, dx, "Profile 2"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_x2, peaks, peak_type, dx / 10, "Profile 2"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_y1, dy, "Profile 3"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_y1, peaks, peak_type, dy / 10, "Profile 3"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
peaks, peak_type, peak_figs = pffr.peak_find_fieldrot(
ampl_resamp_y2, dy, "Profile 4"
)
junction_figs = minFR.minimize_junction_fieldrot(
ampl_resamp_y2, peaks, peak_type, dy / 10, "Profile 4"
)
peaks_figs_comb.append(peak_figs)
junctions_comb.append(junction_figs)
return fig, peaks_figs_comb, junctions_comb
def getMLCdata(ds):
leafIndices = MLCdata.TrueBeam # import leaf index values from MLC data file
beamSequence = ds.BeamSequence
beamNames = [] # getting the beam names
mlcData = {}
jawData = {}
segWeightData = {}
maxPositions = {}
maxJawPos = {}
maxSep = {}
maxSepSum = {}
for b in beamSequence: # looping through the beams
name = b.BeamName
beamNames.append(name)
cps = b.ControlPointSequence
cp = len(cps)
leafPositions = {} # empty dictionary to place leaf positions into
jawPositions = {}
segWeight = {}
maxA = np.zeros((60))
maxB = np.zeros((60))
maxJaws = np.zeros((4))
maxPos = []
for i in range(0, cp - 1): # looping through control points
if len(cps[i].BeamLimitingDevicePositionSequence
) == 3: # assymm jaws
xJaws_ = cps[i].BeamLimitingDevicePositionSequence[
0].LeafJawPositions
yJaws_ = cps[i].BeamLimitingDevicePositionSequence[
1].LeafJawPositions
mlcPos = cps[i].BeamLimitingDevicePositionSequence[
2].LeafJawPositions
elif len(cps[i].BeamLimitingDevicePositionSequence
) == 2: # symmetric jaws
xJaws_ = cps[i].BeamLimitingDevicePositionSequence[
0].LeafJawPositions
yJaws_ = cps[i].BeamLimitingDevicePositionSequence[
0].LeafJawPositions
mlcPos = cps[i].BeamLimitingDevicePositionSequence[
1].LeafJawPositions
leafPosA = list(map(float, mlcPos[0:60]))
leafPosB = list(map(float, mlcPos[60:120]))
leafPositions[i] = np.column_stack(
(leafIndices, leafPosA, leafPosB))
xJaws = list(map(float, xJaws_))
yJaws = list(map(float, yJaws_))
positions = list((xJaws[0], xJaws[1], yJaws[0], yJaws[1]))
indices = list(("x1", "x2", "y1", "y2"))
jawPositions[i] = np.column_stack((indices, positions))
if i == 0:
segWeight[i] = float(cps[i + 1].CumulativeMetersetWeight)
else:
segWeight[i] = float(cps[i + 1].CumulativeMetersetWeight -
cps[i].CumulativeMetersetWeight)
for l in range(0, 60):
if leafPosA[l] < maxA[l]:
maxA[l] = float(leafPosA[l])
if leafPosB[l] > maxB[l]:
maxB[l] = float(leafPosB[l])
for l in range(0, 4):
if abs(positions[l]) > abs(maxJaws[l]):
maxJaws[l] = float(positions[l])
string = str(f"{name}")
maxPos = np.column_stack((maxA, maxB))
mlcData[string] = leafPositions
jawData[string] = jawPositions
segWeightData[string] = segWeight
maxPositions[string] = maxPos
maxJawPos[string] = maxJaws
_ = list(abs(np.array(maxA) - np.array(maxB)))
maxSepSum[string] = np.nansum(_)
maxSep[string] = _
return (mlcData, jawData, segWeightData, maxSep, maxSepSum, maxPositions,
maxJawPos)