def adaptive_optimization(plan, objective_adaptations):
for beam_set in plan.BeamSets:
completed = False
while (completed == False):
# Get the plan optimization corresponding to the current beam set:
po = RSU.plan_optimization(plan, beam_set)
# Run a new optimization with the updated average dose criterias:
#plan.PlanOptimizations[0].RunOptimization()
po.RunOptimization()
# Assume the goals are reached until proven otherwise:
completed = True
# Check each objective:
for oa in objective_adaptations:
# Continue adapting if target has not been reached, and the achieved dose is above 1 cGy:
if oa.on_target(
) == False and oa.objective.DoseFunctionParameters.DoseLevel > 1:
completed = False
# Function value is either too high or too low:
if oa.objective.FunctionValue.FunctionValue < oa.function_value_low:
# Function value is too low: It is possible to reduce dose further:
oa.nr_dose_reductions += 1
oa.nr_dose_increases = 0
oa.set_dose_high(0.5 * (oa.dose_low + oa.dose_high))
# After new high limit has been set, the target function value should be between low and high limit:
oa.objective.DoseFunctionParameters.DoseLevel = 0.5 * (
oa.dose_low + oa.dose_high)
elif oa.objective.FunctionValue.FunctionValue > oa.function_value_high:
# Function value is too high: We were too aggressive, dose limit must be increased:
oa.nr_dose_increases += 1
oa.nr_dose_reductions = 0
oa.set_dose_low(
oa.objective.DoseFunctionParameters.DoseLevel)
# After new low limit has been set, the target function value should be between low and high limit:
oa.objective.DoseFunctionParameters.DoseLevel = 0.5 * (
oa.dose_low + oa.dose_high)
def isocenter_centered_long_test(self):
t = TEST.Test(
"Isosenter skal i utgangspunktet være mest mulig sentrert i long-retning, avstand mellom isosenter og senter av normeringsvolumet bør være mindre enn 1 cm",
'<1 cm', self.isocenter)
if self.beam_set.Modality == 'Photons':
diff = 0
ss = self.ts_structure_set().structure_set
target0 = None
target1 = None
for roi in ss.RoiGeometries:
if roi.OfRoi.Type == 'Ptv' and roi.PrimaryShape:
# Determine if this target volume is relevant for this beam set (by checking if it is used as an objective):
po = RSU.plan_optimization(self.ts_plan.plan,
self.beam_set)
if po:
for objective in po.Objective.ConstituentFunctions:
if objective.ForRegionOfInterest.Name == roi.OfRoi.Name:
current_target = roi.GetBoundingBox()
if target0 == None or current_target[
0].z < target0:
target0 = current_target[0].z
if target1 == None or current_target[
1].z > target1:
target1 = current_target[1].z
if self.is_vmat():
if target0 and target1:
target_center_z = target0 + 0.5 * abs(target0 - target1)
for beam in self.beam_set.Beams:
photon_iso = beam.Isocenter.Position
diff = abs(photon_iso.z - target_center_z)
elif not self.is_vmat():
if not self.ts_label.label.region in RC.conventional_and_vmat_site_codes:
if self.beam_set.Modality == 'Photons':
for beam in self.beam_set.Beams:
photon_iso = beam.Isocenter.Position
for rg in self.ts_structure_set(
).structure_set.RoiGeometries:
if rg.OfRoi.Name == self.beam_set.Prescription.PrimaryDosePrescription.OnStructure.Name:
target = rg.GetBoundingBox()
target_center_z = target[0].z + 0.5 * abs(
target[0].z - target[1].z)
diff = abs(photon_iso.z - target_center_z)
if diff:
if diff > 1:
return t.fail(round(diff, 2))
else:
return t.succeed()
def target_volume_normalisation_for_sib_test(self):
if self.ts_label.label.region in RC.prostate_codes + RC.breast_codes + RC.rectum_codes:
ss = self.ts_structure_set().structure_set
roi_dict = SSF.create_roi_dict(ss)
potential_target_volumes = [
ROIS.ctv_47.name, ROIS.ctv_56.name, ROIS.ctv_70_sib.name,
ROIS.ctv_57.name
]
objective_target_volumes = {}
# Create a dictionary of all CTVs defined as an objective.
po = RSU.plan_optimization(self.ts_plan.plan, self.beam_set)
if po:
for objective in po.Objective.ConstituentFunctions:
if objective.ForRegionOfInterest.Type == 'Ctv':
objective_target_volumes[
objective.ForRegionOfInterest.Name] = True
# Check in the list of potential SIB volumes if any exist in the structure set, define as target
for i in range(len(potential_target_volumes)):
if roi_dict.get(potential_target_volumes[i]):
target = potential_target_volumes[i]
t = TEST.Test(
"Skal stemme overens (innenfor 0.5%) med aktuell dose for normeringsvolumet "
+ target + ".", True, self.norm_dose)
# Check if the target was used as objective
if objective_target_volumes.get(target):
# Use the two last characters in the target-string to find the wanted median dose of that target volume
if int(target[-2:]) > 30:
prescription_dose = int(target[-2:])
low_dose = round(prescription_dose * 0.995, 2)
high_dose = round(prescription_dose * 1.005, 2)
real_dose_d50 = RSU.gy(
self.beam_set.FractionDose.
GetDoseAtRelativeVolumes(
RoiName=target, RelativeVolumes=[0.50])[0]
) * self.beam_set.FractionationPattern.NumberOfFractions
t.expected = ">" + str(
low_dose) + " og " + "<" + str(high_dose)
if real_dose_d50 < low_dose or real_dose_d50 > high_dose:
t.fail(round(real_dose_d50, 2))
else:
t.succeed()
def asymmetric_jaw_opening_long_test(self):
t = TEST.Test(
"Isosenter ser ut til å være asymmetrisk, det bør vurderes å flytte isosenter. Dette for å få målt hele målvolumet med ArcCheck-fantomet",
'<10.5 cm', self.isocenter)
ss = self.ts_structure_set().structure_set
isocenter = None
target0 = None
target1 = None
if self.is_vmat():
for roi in ss.RoiGeometries:
if roi.OfRoi.Type == 'Ptv' and roi.PrimaryShape:
# Determine if this target volume is relevant for this beam set (by checking if it is used as an objective):
po = RSU.plan_optimization(self.ts_plan.plan,
self.beam_set)
if po:
for objective in po.Objective.ConstituentFunctions:
if objective.ForRegionOfInterest.Name == roi.OfRoi.Name:
current_target = roi.GetBoundingBox()
if target0 == None or current_target[
0].z < target0:
target0 = current_target[0].z
if target1 == None or current_target[
1].z > target1:
target1 = current_target[1].z
for beam in self.beam_set.Beams:
photon_iso = beam.Isocenter.Position
if target0 and target1:
if abs(target0 - target1) <= 21:
if abs(photon_iso.z - target0) > 10.5:
return t.fail(round(abs(photon_iso.z - target0),
2))
elif abs(photon_iso.z - target1) > 10.5:
return t.fail(round(abs(photon_iso.z - target1),
2))
else:
return t.succeed()
else:
return t.succeed()
for poi_geometry in structure_set.PoiGeometries:
ts_poi_geometry = TS_PG.TSPOIGeometry(
poi_geometry, ts_structure_set=ts_structure_set)
for roi_geometry in structure_set.RoiGeometries:
ts_roi_geometry = TS_RG.TSROIGeometry(
roi_geometry, ts_structure_set=ts_structure_set)
ts_plan = TS_PLAN.TSPlan(plan, ts_case=ts_case)
for beam_set in plan.BeamSets:
ts_beam_set = TS_BS.TSBeamSet(beam_set, ts_plan=ts_plan)
ts_label = TS_L.TSLabel(beam_set.DicomPlanLabel,
ts_beam_set=ts_beam_set)
if beam_set.Prescription.PrimaryDosePrescription:
ts_prescription = TS_PR.TSPrescription(beam_set.Prescription,
ts_beam_set=ts_beam_set)
po = RSU.plan_optimization(plan, beam_set)
if po:
ts_optimization = TS_O.TSOptimization(po,
ts_beam_set=ts_beam_set)
for beam in beam_set.Beams:
ts_beam = TS_B.TSBeam(beam, ts_beam_set=ts_beam_set)
for segment in beam.Segments:
ts_segment = TS_S.TSSegment(segment, ts_beam=ts_beam)
# Store the patient test results:
self.result = ts_patient.param
# Run tests:
# Patient tests:
ts_patient.id_length_test()
ts_patient.id_space_test()