def create_additional_palliative_beamsets_prescriptions_and_beams(plan, examination, ss, region_codes, fraction_dose, nr_fractions, external, energy_name, nr_existing_beams=1, isocenter=False):
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
common_isocenter = False
if isocenter:
common_isocenter = True
i = 1
if nr_targets > 1:
for r in region_codes:
region_code = int(r)
# Determine the point which will be our isocenter:
if not isocenter:
if SSF.has_named_roi_with_contours(ss, 'PTV' + str(i+1)):
isocenter = SSF.determine_isocenter(examination, ss, region_code, 'VMAT', 'PTV' + str(i+1), external)
energy_name = SSF.determine_energy_single_target(ss, 'PTV'+str(i+1))
else:
GUIF.handle_missing_ptv()
# Set up beam set and prescription:
beam_set = plan.AddNewBeamSet(
Name=BSF.label(region_code, fraction_dose, nr_fractions, 'VMAT'),
ExaminationName=examination.Name,
MachineName= "ALVersa",
Modality='Photons',
TreatmentTechnique='VMAT',
PatientPosition=CF.determine_patient_position(examination),
NumberOfFractions=nr_fractions
)
BSF.add_prescription(beam_set, nr_fractions, fraction_dose, 'CTV' + str(i+1))
# Setup beams or arcs
nr_beams = BEAMS.setup_beams(ss, examination, beam_set, isocenter, region_code, fraction_dose, 'VMAT', energy_name, iso_index=str(i+1), beam_index=nr_existing_beams+1)
nr_existing_beams += nr_beams
OBJ.create_palliative_objectives_for_additional_beamsets(ss, plan, fraction_dose*nr_fractions, i)
i += 1
if not common_isocenter:
isocenter=False
def beam_set_choices(ss):
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
sep_plan = P.Property('Separate planer',
'sep_plan',
next_category='målvolum')
sep_beamset_sep_iso = P.Property('Separate beam set - separate isosenter',
'sep_beamset_sep_iso')
sep_beamset_iso = P.Property('Separate beam set - felles isosenter',
'sep_beamset_iso',
default=True)
beamset_iso = P.Property('Samme beam set - felles isosenter', 'beamset')
for i in range(nr_targets):
P.Property('CTV' + str(i + 1), 'CTV' + str(i + 1), parent=sep_plan)
return [sep_plan, sep_beamset_sep_iso, sep_beamset_iso, beamset_iso]
def create_additional_stereotactic_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
prescription,
external,
energy_name,
nr_existing_beams=1):
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
i = 1
if nr_targets > 1:
if int(region_codes[0]) in RC.brain_codes + RC.lung_codes:
for r in region_codes:
# Set up beam set and prescription:
region_code = int(r)
beam_set = plan.AddNewBeamSet(
Name=BSF.label_s(region_code, prescription.fraction_dose,
prescription.nr_fractions),
ExaminationName=examination.Name,
MachineName="ALVersa",
Modality='Photons',
TreatmentTechnique='VMAT',
PatientPosition='HeadFirstSupine',
NumberOfFractions=prescription.nr_fractions)
BSF.add_prescription(beam_set, prescription,
'PTV' + str(i + 1))
# Determine the point which will be our isocenter:
isocenter = SSF.determine_isocenter(examination, ss,
region_code, 'VMAT',
'PTV' + str(i + 1),
external)
# Setup beams or arcs
nr_beams = BEAMS.setup_beams(ss,
examination,
beam_set,
isocenter,
region_code,
prescription.fraction_dose,
'VMAT',
energy_name,
iso_index=str(i + 1),
beam_index=nr_existing_beams + 1)
nr_existing_beams += nr_beams
i += 1
def __init__(self, the_window, ss):
Frame.__init__(self, the_window, bg='white')
self.ok = False
self.ss = ss
self.create_widgets(self.ss)
self.nr_targets = SSF.determine_nr_of_indexed_ptvs(self.ss)
# Parameters for h = height, w = width of window, as well as location on the screen: x and y
h = self.nr_targets * 30 + 110
#w = 240
w = 350
x = 1100
y = 500
# Set these parameters
the_window.geometry('%dx%d+%d+%d' % (w, h, x, y))
# Title of window
the_window.title("Regionkoder")
# Set the background to white
the_window.configure(bg='white')
class Plan(object):
def __init__(self, patient, case, mq_patient):
self.patient = patient
self.case = case
self.mq_patient = mq_patient
# Load patient model, examination and structure set:
pm = case.PatientModel
examination = get_current("Examination")
ss = PMF.get_structure_set(pm, examination)
# Determine if a target volume is present (raises error if not):
if not PMF.has_defined_ctv_or_ptv(pm, examination):
GUIF.handle_missing_ctv_or_ptv()
# Check if the last CT has been set as primary, and display a warning if not.
success = TS_C.TSCase(case).last_examination_used_test()
if not success:
GUIF.handle_primary_is_not_most_recent_ct()
# Setup and run GUI:
my_window = Tk()
(region_code, fraction_dose, nr_fractions, initials,
total_dose) = GUIF.collect_fractionation_choices(my_window)
# Load list of region codes and corresponding region names and get the region name for our particular region code (raise error if a name is not retrieved):
regions = REGIONS.RegionList(
"C:\\temp\\raystation-scripts\\settings\\regions.tsv")
region_text = regions.get_text(region_code)
assert region_text != None
# For SBRT brain or lung, if there are multiple targets, an extra form appear where
# the user has to type region code of the other targets.
# FIXME: Bruke funksjon for test fx?
# FIXME: Vurder hvor denne koden bør ligge.
target = None
palliative_choices = None
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
if nr_targets > 1:
if region_code in RC.brain_codes + RC.lung_codes:
if PF.is_stereotactic(nr_fractions, fraction_dose):
region_codes = GUIF.multiple_beamset_form(ss, Toplevel())
GUIF.check_region_codes(region_code, region_codes)
elif region_code in RC.palliative_codes:
# For palliative cases with multiple targets:
palliative_choices = GUIF.palliative_beamset_form(
ss, Toplevel())
if palliative_choices[0] in [
'sep_beamset_iso', 'sep_beamset_sep_iso'
]:
region_codes = GUIF.multiple_beamset_form(ss, Toplevel())
GUIF.check_region_codes(region_code, region_codes)
if SSF.has_roi_with_shape(ss, ROIS.ctv1.name):
target = ROIS.ctv1.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv2.name):
target = ROIS.ctv2.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv3.name):
target = ROIS.ctv3.name
elif SSF.has_roi_with_shape(ss, ROIS.ctv4.name):
target = ROIS.ctv4.name
elif palliative_choices[0] == 'sep_plan':
target = palliative_choices[1]
# Set up plan, making sure the plan name does not already exist. If the plan name exists, (1), (2), (3) etc is added behind the name.
plan = CF.create_plan(case, examination, region_text)
# Check that the number of fractions and fraction dose is among those expected for the given region code
GUIF.check_input(ss, region_code, nr_fractions, fraction_dose)
# Set planners initials
plan.PlannedBy = initials
# Set dose grid, 0.2x0.2x0.2 cm3 for stereotactic treatments and for prostate and 0.3x03x0.3 cm3 otherwise
PF.set_dose_grid(plan, region_code, nr_fractions, fraction_dose)
my_window = Toplevel()
# Determine which technique and optimization choices which will appear in the form
results = GUIF.determine_choices(region_code, nr_fractions,
fraction_dose, my_window, [])
# Chosen technique value, 'VMAT' or 'Conformal'
technique = results[0]
# Chosen technique name, 'VMAT' or '3D-CRT'
technique_name = results[1]
# Chosen optimization value
opt = results[2]
# Determine prescription target:
if not target:
roi_dict = SSF.create_roi_dict(ss)
target = SSF.determine_target(ss, roi_dict, nr_fractions,
fraction_dose)
# Translate the couch in the longitudinal direction according to the target position
if SSF.has_roi_with_shape(ss, ROIS.couch.name):
PMF.translate_couch_long(pm, ss, examination, target)
# Create 'Mask_PTV' for partial brain and stereotactic brain
if region_code in RC.brain_codes and region_code not in RC.brain_whole_codes:
if nr_targets > 1:
targets = [ROIS.ptv1, ROIS.ptv2, ROIS.ptv3, ROIS.ptv4]
for i in range(nr_targets):
SSF.create_expanded_and_intersected_volume(
pm, examination, ss, targets[i], ROIS.external,
ROIS.mask_ptv.name + str(i + 1), 1600)
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name +
str(i + 1),
IsVisible=False)
else:
SSF.create_expanded_and_intersected_volume(
pm, examination, ss, ROIS.ptv, ROIS.external,
ROIS.mask_ptv.name, 1600)
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name,
IsVisible=False)
# Create 'Mask_PTV' for stereotactic lung
if region_code in RC.lung_codes and PF.is_stereotactic(
nr_fractions, fraction_dose):
if nr_targets > 1:
targets = [ROIS.ptv1, ROIS.ptv2, ROIS.ptv3]
for i in range(nr_targets):
created = SSF.create_roi_subtraction(
pm, examination, ss, targets[i], ROIS.chestwall,
ROIS.mask_ptv.name + str(i + 1), 0)
if created:
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name +
str(i + 1),
IsVisible=False)
else:
created = SSF.create_roi_subtraction(pm, examination, ss,
ROIS.ptv, ROIS.chestwall,
ROIS.mask_ptv.name, 0)
if created:
patient.SetRoiVisibility(RoiName=ROIS.mask_ptv.name,
IsVisible=False)
# Determine name of the body contour ('External' or 'Body'):
external = SSF.body_roi_name(ss)
if not external:
GUIF.handle_missing_external()
# Determine the machine name from the size of the target volume, only one target is taken into consideration here.
# For those situations where you have two targets and you want to have separate isocenters, then you what to evaluate the targets separately.
if target in [
ROIS.ctv1.name, ROIS.ctv2.name, ROIS.ctv3.name, ROIS.ctv4.name
] and palliative_choices[0] in ['sep_beamset_sep_iso', 'sep_plan']:
energy_name = SSF.determine_energy_single_target(ss, target)
elif region_code in RC.breast_codes:
energy_name = '6'
else:
# Determine the machine name from the size of the target volume:
energy_name = SSF.determine_energy(ss, target)
# Create the name of the beamset
beam_set_name = BSF.label(region_code, fraction_dose, nr_fractions,
technique)
# Create primary beam set:
beam_set = PF.create_beam_set(plan, beam_set_name, examination,
technique, nr_fractions)
# Add prescription:
BSF.add_prescription(beam_set, nr_fractions, fraction_dose, target)
# Determine the point which will be our isocenter:
if nr_targets > 1:
if palliative_choices and palliative_choices[0] in [
'sep_beamset_iso', 'beamset'
]:
# Consider all targets when determining isocenter:
isocenter = SSF.determine_isocenter(examination,
ss,
region_code,
technique_name,
target,
external,
multiple_targets=True)
else:
# Set isocenter for PTV1:
isocenter = SSF.determine_isocenter(examination, ss,
region_code,
technique_name, target,
external)
else:
# Consider all targets when determining isocenter:
isocenter = SSF.determine_isocenter(examination,
ss,
region_code,
technique_name,
target,
external,
multiple_targets=True)
# Determine if this patient has any previous beams in Mosaiq (which impacts which beam number is to be used with this plan):
beam_nr = 1
if self.mq_patient:
beam_nr = self.mq_patient.next_available_field_number()
# Setup beams or arcs
nr_beams = BEAMS.setup_beams(ss,
examination,
beam_set,
isocenter,
region_code,
fraction_dose,
technique_name,
energy_name,
beam_index=beam_nr)
# For SBRT brain or lung, if there are multiple targets, create beam sets for all targets
# FIXME: Bruke funksjon for test fx?
if nr_targets > 1:
if region_code in RC.brain_codes + RC.lung_codes and region_code not in RC.brain_whole_codes:
if PF.is_stereotactic(nr_fractions, fraction_dose):
PF.create_additional_stereotactic_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
fraction_dose,
nr_fractions,
external,
energy_name,
nr_existing_beams=nr_beams)
elif region_code in RC.palliative_codes:
# Palliative cases with multiple targets:
if palliative_choices[0] in [
'sep_beamset_iso', 'sep_beamset_sep_iso'
]:
if palliative_choices[0] == 'sep_beamset_iso':
PF.create_additional_palliative_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
fraction_dose,
nr_fractions,
external,
energy_name,
nr_existing_beams=nr_beams,
isocenter=isocenter)
else:
PF.create_additional_palliative_beamsets_prescriptions_and_beams(
plan,
examination,
ss,
region_codes,
fraction_dose,
nr_fractions,
external,
energy_name,
nr_existing_beams=nr_beams)
# If there is a 2Gy x 8 boost for breast patients
if SSF.has_roi_with_shape(
ss, ROIS.ctv_sb.name) and SSF.has_roi_with_shape(
ss, ROIS.ptv_c.name) and region_code in RC.breast_codes:
PF.create_breast_boost_beamset(
ss,
plan,
examination,
isocenter,
region_code,
ROIS.ctv_sb.name,
background_dose=int(round(fraction_dose * nr_fractions)))
# Make sure that the original beam set (not this boost beam set) is loaded in the GUI:
infos = plan.QueryBeamSetInfo(
Filter={'Name': '^' + beam_set_name + '$'})
plan.LoadBeamSet(BeamSetInfo=infos[0])
# Determines and sets up isodoses based on region code and fractionation
CF.determine_isodoses(case, ss, region_code, nr_fractions,
fraction_dose)
# Determine site
site = SF.site(pm, examination, ss, plan, nr_fractions, total_dose,
region_code, target, technique_name)
# Set up Clinical Goals:
es = plan.TreatmentCourse.EvaluationSetup
CG.setup_clinical_goals(ss, es, site, total_dose, nr_fractions, target)
# Loads the plan, done after beam set is created, as this is the only way the CT-images appears in Plan Design and Plan Optimization when the plan is loaded
CF.load_plan(case, plan)
# Set up beams and optimization for breast patients
if technique_name == 'VMAT' and region_code in RC.breast_reg_codes:
# Use robust optimization for VMAT breast:
OBJF.set_robustness_breast(plan, region_code)
elif technique_name == '3D-CRT' and region_code in RC.breast_codes:
if region_code in RC.breast_reg_codes:
BSF.set_up_beams_and_optimization_for_regional_breast(
plan, beam_set, ROIS.ptv_c.name, region_code)
else:
BSF.set_up_beams_and_optimization_for_tangential_breast(
plan, beam_set, plan.PlanOptimizations[0],
target.replace("C", "P") + "c")
# If there is a 2Gy x 8 boost for breast patients
if SSF.has_roi_with_shape(
ss, ROIS.ctv_sb.name) and SSF.has_roi_with_shape(
ss, ROIS.ptv_c.name):
BSF.set_up_beams_and_optimization_for_tangential_breast(
plan, plan.BeamSets[1], plan.PlanOptimizations[1],
ROIS.ptv_sbc.name)
# Set up treat and protect for stereotactic lung
#if region_code in RC.lung_codes and PF.is_stereotactic(nr_fractions, fraction_dose):
# BSF.set_up_treat_and_protect_for_stereotactic_lung(beam_set, target, 0.5)
# Run first optimization on each beam set:
for po in plan.PlanOptimizations:
po.OptimizationParameters.DoseCalculation.ComputeFinalDose = True
# Set 'Constrain leaf motion' to 0.3 for stereotactic patients
if PF.is_stereotactic(nr_fractions, fraction_dose):
acp = po.OptimizationParameters.TreatmentSetupSettings[
0].SegmentConversion.ArcConversionProperties
acp.UseMaxLeafTravelDistancePerDegree = True
acp.MaxLeafTravelDistancePerDegree = 0.3
po.RunOptimization()
# Start adaptive optimization if indicated
if opt == 'oar':
OBJF.adapt_optimization_oar(ss, plan, site.oar_objectives,
region_code)
if region_code in RC.breast_codes:
if region_code in RC.breast_reg_codes:
# Need to close leafs behind jaw for breast regional patients
BSF.close_leaves_behind_jaw_for_regional_breast(beam_set)
# Create 2.5 cm margin to air for breast patient planned with a 3D-CRT technique for robustness purpuses
BSF.create_margin_air_for_3dcrt_breast(ss, beam_set,
region_code)
# Compute dose
beam_set.ComputeDose(DoseAlgorithm='CCDose')
# Auto scale to prescription
for po in plan.PlanOptimizations:
po.AutoScaleToPrescription = True
# Load plan
CF.load_plan(case, plan)
# Save
patient.Save()
def create_lung_stereotactic_objectives(ss, plan, region_code, total_dose):
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
for i in range(0, nr_targets):
OF.fall_off(ss,
plan,
ROIS.external.name,
total_dose * 100,
total_dose * 100 / 2,
3,
5,
beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.external.name,
total_dose * 130,
15,
beam_set_index=i)
OF.max_dose(ss, plan, ROIS.skin.name, 30 * 100, 10, beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.spinal_canal.name,
13 * 100,
5,
beam_set_index=i)
OF.max_dvh(ss,
plan,
ROIS.chestwall.name,
30 * 100,
2,
100,
beam_set_index=i)
OF.max_eud(ss,
plan,
ROIS.lungs.name,
4.5 * 100,
1,
1,
beam_set_index=i)
if region_code in RC.lung_r_codes:
OF.max_eud(ss,
plan,
ROIS.lung_r.name,
6.5 * 100,
1,
3,
beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.ribs_r.name,
total_dose * 120,
10,
beam_set_index=i)
else:
OF.max_eud(ss,
plan,
ROIS.lung_l.name,
6.5 * 100,
1,
3,
beam_set_index=i)
OF.max_dose(ss,
plan,
ROIS.ribs_l.name,
total_dose * 120,
10,
beam_set_index=i)
if nr_targets == 1:
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100, 250)
OF.fall_off(ss, plan, ROIS.wall_ptv.name, total_dose * 100,
0.7 * total_dose * 100, 0.8, 5)
else:
for i in range(0, nr_targets):
OF.min_dose(ss,
plan,
ROIS.ptv.name + str(i + 1),
total_dose * 100,
250,
beam_set_index=i)
OF.fall_off(ss,
plan,
"zPTV" + str(i + 1) + "_Wall",
total_dose * 100,
0.7 * total_dose * 100,
0.8,
5,
beam_set_index=i)
def create_brain_objectives(pm, examination, ss, plan, total_dose,
nr_fractions):
if nr_fractions in [1, 3]: # Stereotactic brain
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
for i in range(0, nr_targets):
OF.max_eud(ss,
plan,
ROIS.brain_ptv.name,
0.08 * total_dose * 100,
1.3,
1,
beam_set_index=i)
OF.max_eud(ss,
plan,
ROIS.brain_ptv.name,
0.06 * total_dose * 100,
1,
1,
beam_set_index=i)
OF.fall_off(ss,
plan,
ROIS.body.name,
total_dose * 100,
total_dose * 100 / 2,
0.8,
25,
beam_set_index=i)
if nr_targets == 1: # one target
OF.min_dose(ss,
plan,
ROIS.ptv.name,
total_dose * 100,
200,
beam_set_index=0)
OF.fall_off(ss,
plan,
ROIS.z_ptv_wall.name,
total_dose * 100,
0.7 * total_dose * 100,
0.6,
25,
beam_set_index=0)
else:
for i in range(0, nr_targets):
OF.min_dose(ss,
plan,
ROIS.ptv.name + str(i + 1),
total_dose * 100,
200,
beam_set_index=i)
OF.fall_off(ss,
plan,
"zPTV" + str(i + 1) + "_Wall",
total_dose * 100,
0.7 * total_dose * 100,
0.6,
25,
beam_set_index=i)
else: # Partial brain
OF.max_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 1.05, 80)
OF.fall_off(ss, plan, ROIS.external.name, total_dose * 100,
total_dose * 100 / 2, 1.5, 30)
OF.max_dose(ss, plan, ROIS.external.name, total_dose * 100 * 1.05, 30)
# Objectives for prioritized OARs:
OF.max_dose(
ss, plan, ROIS.brainstem_surface.name,
(TOL.brainstem_surface_v003_adx.equivalent(nr_fractions) * 100) -
50, 60)
OF.max_dose(
ss, plan, ROIS.brainstem_core.name,
(TOL.brainstem_core_v003_adx.equivalent(nr_fractions) * 100) - 50,
80)
OF.max_dose(
ss, plan, ROIS.optic_chiasm.name,
(TOL.optic_chiasm_v003_adx.equivalent(nr_fractions) * 100) - 50,
40)
OF.max_dose(ss, plan, ROIS.optic_nrv_l.name,
(TOL.optic_nrv_v003_adx.equivalent(nr_fractions) * 100) -
50, 20)
OF.max_dose(ss, plan, ROIS.optic_nrv_r.name,
(TOL.optic_nrv_v003_adx.equivalent(nr_fractions) * 100) -
50, 20)
prioritized_oars = [
ROIS.brainstem_core, ROIS.brainstem_surface, ROIS.optic_chiasm,
ROIS.optic_nrv_l, ROIS.optic_nrv_r
]
tolerances = [
TOL.brainstem_core_v003_adx, TOL.brainstem_surface_v003_adx,
TOL.optic_chiasm_v003_adx, TOL.optic_nrv_v003_adx,
TOL.optic_nrv_v003_adx
]
conflict_oars = []
for i in range(len(prioritized_oars)):
if tolerances[i].equivalent(nr_fractions) < total_dose * 0.95:
conflict_oars.append(prioritized_oars[i])
# Setup of min and uniform doses depends on presence of critical overlaps or not:
if len(conflict_oars) > 0:
# Create subtraction and intersect ROIs for planning of conflicting sites:
ctv_oars = ROI.ROIAlgebra(ROIS.ctv_oars.name,
ROIS.ctv_oars.type,
ROIS.ctv.color,
sourcesA=[ROIS.ctv],
sourcesB=conflict_oars,
operator='Subtraction',
marginsA=MARGINS.zero,
marginsB=MARGINS.uniform_2mm_expansion)
ptv_oars = ROI.ROIAlgebra(ROIS.ptv_oars.name,
ROIS.ptv_oars.type,
ROIS.ptv.color,
sourcesA=[ROIS.ptv],
sourcesB=conflict_oars,
operator='Subtraction',
marginsA=MARGINS.zero,
marginsB=MARGINS.uniform_2mm_expansion)
ptv_and_oars = ROI.ROIAlgebra(ROIS.ptv_and_oars.name,
ROIS.ptv_and_oars.type,
ROIS.other_ptv.color,
sourcesA=[ROIS.ptv],
sourcesB=conflict_oars,
operator='Intersection')
rois = [ctv_oars, ptv_oars, ptv_and_oars]
PMF.delete_matching_rois(pm, rois)
for i in range(len(rois)):
PMF.create_algebra_roi(pm, examination, ss, rois[i])
PMF.exclude_roi_from_export(pm, rois[i].name)
# Create objectives for the subtraction/intersect ROIs:
OF.uniform_dose(
ss, plan, ROIS.ptv_and_oars.name,
(tolerances[0].equivalent(nr_fractions) * 100 - 50), 5
) # (Note that this assumes our OARs have the same tolerance dose...)
OF.uniform_dose(ss, plan, ROIS.ctv_oars.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv_oars.name, total_dose * 100 * 0.95,
150)
else:
OF.uniform_dose(ss, plan, ROIS.ctv.name, total_dose * 100, 30)
OF.min_dose(ss, plan, ROIS.ptv.name, total_dose * 100 * 0.95, 150)
# Setup of objectives for less prioritized OARs:
other_oars = [
ROIS.cochlea_l, ROIS.cochlea_r, ROIS.hippocampus_l,
ROIS.hippocampus_r, ROIS.lens_l, ROIS.lens_r, ROIS.lacrimal_l,
ROIS.lacrimal_r, ROIS.retina_l, ROIS.retina_r, ROIS.cornea_r,
ROIS.cornea_l, ROIS.pituitary
]
tolerances = [
TOL.cochlea_mean_tinnitus, TOL.cochlea_mean_tinnitus,
TOL.hippocampus_v40, TOL.hippocampus_v40, TOL.lens_v003_adx,
TOL.lens_v003_adx, TOL.lacrimal_mean, TOL.lacrimal_mean,
TOL.retina_v003_adx, TOL.retina_v003_adx, TOL.cornea_v003_adx,
TOL.cornea_v003_adx, TOL.pituitary_mean
]
for i in range(len(other_oars)):
if SSF.has_named_roi_with_contours(ss, other_oars[i].name):
weight = None
# Conflict with dose?
if tolerances[i].equivalent(nr_fractions) < total_dose * 0.95:
# Conflict with dose:
if not SSF.roi_overlap(pm, examination, ss, ROIS.ptv,
other_oars[i], 2):
if ROIF.roi_vicinity_approximate(
SSF.rg(ss, ROIS.ptv.name),
SSF.rg(ss, other_oars[i].name), 2):
# OAR is close, but not overlapping:
weight = 2
else:
weight = 20
else:
# No conflict with dose:
weight = 20
# Create objective if indicated:
if weight:
if other_oars[i].name in [
ROIS.cochlea_r.name, ROIS.cochlea_l.name,
ROIS.lacrimal_l.name, ROIS.lacrimal_r.name,
ROIS.hippocampus_l.name, ROIS.hippocampus_r.name
]:
OF.max_eud(
ss, plan, other_oars[i].name,
tolerances[i].equivalent(nr_fractions) * 100 - 50,
1, weight)
else:
OF.max_dose(
ss, plan, other_oars[i].name,
(tolerances[i].equivalent(nr_fractions) * 100) -
50, weight)
else:
GUIF.handle_missing_roi_for_objective(other_oars[i].name)
class Plan(object):
def __init__(self, patient, case, mq_patient):
self.patient = patient
self.case = case
self.mq_patient = mq_patient
# Load patient model, examination and structure set:
pm = case.PatientModel
examination = get_current("Examination")
ss = PMF.get_structure_set(pm, examination)
# Determine if a target volume is present (raises error if not):
if not PMF.has_defined_ctv_or_ptv(pm, examination):
GUIF.handle_missing_ctv_or_ptv()
# Check if the last CT has been set as primary, and display a warning if not:
success = TS_C.TSCase(case).last_examination_used_test()
if not success:
GUIF.handle_primary_is_not_most_recent_ct()
# Setup and run GUI:
my_window = Tk()
(region_code, fraction_dose, nr_fractions, initials, total_dose) = GUIF.collect_fractionation_choices(my_window)
# Load list of region codes and corresponding region names, and get the region name for our particular region code (raise error if a name is not retrieved):
regions = REGIONS.RegionList("C:\\temp\\raystation-scripts\\settings\\regions.tsv")
region_text = regions.get_text(region_code)
assert region_text != None
# Establish the number of target volumes:
nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
# Create the prescription object:
prescription = PRES.create_prescription(total_dose, nr_fractions, region_code)
# Validate the prescription:
valid = PRES.validate_prescription(prescription, region_code)
if not valid:
GUIF.handle_invalid_prescription(prescription, region_code, region_text)
# For SBRT brain or lung, if there are multiple targets, display an extra form
# where the user can specify the region code(s) of the other target(s).
target, palliative_choices, region_codes = GUIF.collect_target_strategy_and_region_codes(ss, nr_targets, region_code, prescription)
# Set up plan, making sure the plan name does not already exist. If the plan name exists, (1), (2), (3) etc is added behind the name:
plan = CF.create_plan(case, examination, region_text)
# Set planners initials:
plan.PlannedBy = initials
my_window = Toplevel()
# Determine which technique and optimization choices which will appear in the form:
results = GUIF.determine_choices(region_code, prescription, my_window, [])
# Chosen technique value ('VMAT' or 'Conformal'):
technique = results[0]
# Chosen technique name ('VMAT' or '3D-CRT'):
technique_name = results[1]
# Chosen optimization value:
opt = results[2]
# Determine the prescription target volume:
if not target:
roi_dict = SSF.create_roi_dict(ss)
target = SSF.determine_target(ss, roi_dict, prescription)
# Create 'Mask_PTV' for partial brain and stereotactic brain:
if region_code in RC.brain_codes and region_code not in RC.brain_whole_codes:
PMF.create_mask_ptv_brain(patient, pm, examination, ss, nr_targets)
# Create 'Mask_PTV' for stereotactic lung:
if region_code in RC.lung_codes and prescription.is_stereotactic():
PMF.create_mask_ptv_lung(patient, pm, examination, ss, nr_targets)
# Determine name of the body contour ('External' or 'Body'):
external = SSF.body_roi_name(ss)
if not external:
GUIF.handle_missing_external()
# Determine the energy quality from the size of the target volume (note that only one target is taken into consideration here).
# For those situations where you have two targets and you want to have separate isocenters, then you what to evaluate the targets separately.
if target in [ROIS.ctv1.name, ROIS.ctv2.name, ROIS.ctv3.name, ROIS.ctv4.name] and palliative_choices[0] in ['sep_beamset_sep_iso', 'sep_plan']:
energy_name = SSF.determine_energy_single_target(ss, target)
elif region_code in RC.breast_codes:
energy_name = '6'
else:
# Determine the energy quality from the size of the target volume:
energy_name = SSF.determine_energy(ss, target)
# Create beamset name:
beam_set_name = BSF.label(region_code, prescription, technique)
# Create primary beam set:
beam_set = PF.create_beam_set(plan, beam_set_name, examination, technique, prescription.nr_fractions)
# Add prescription:
BSF.add_prescription(beam_set, prescription, target)
# Set beam set dose grid:
BSF.set_dose_grid(beam_set, region_code, prescription)
# Determine the point which will be our isocenter:
if nr_targets > 1:
if palliative_choices and palliative_choices[0] in ['sep_beamset_iso', 'beamset']:
# Consider all targets when determining isocenter:
isocenter = SSF.determine_isocenter(examination, ss, region_code, technique_name, target, external, multiple_targets = True)
else:
# Set isocenter for PTV1:
isocenter = SSF.determine_isocenter(examination, ss, region_code, technique_name, target, external)
else:
# Consider all targets when determining isocenter:
isocenter = SSF.determine_isocenter(examination, ss, region_code, technique_name, target, external, multiple_targets = True)
# Determine if this patient has any previous beams in Mosaiq (which impacts which beam number is to be used with this plan):
beam_nr = 1
if self.mq_patient:
beam_nr = self.mq_patient.next_available_field_number()
# Setup beams (or arcs):
nr_beams = BEAMS.setup_beams(ss, examination, beam_set, isocenter, region_code, prescription.fraction_dose, technique_name, energy_name, beam_index=beam_nr)
last_beam_index = beam_nr + nr_beams - 1
# For SBRT brain or lung, if there are multiple targets, create beam sets for all targets:
if nr_targets > 1:
if region_code in RC.brain_codes + RC.lung_codes and region_code not in RC.brain_whole_codes:
if prescription.is_stereotactic():
PF.create_additional_stereotactic_beamsets_prescriptions_and_beams(plan, examination, ss, region_codes, prescription, external, energy_name, nr_existing_beams = last_beam_index)
elif region_code in RC.palliative_codes:
# Palliative cases with multiple targets:
if palliative_choices[0] in ['sep_beamset_iso', 'sep_beamset_sep_iso']:
if palliative_choices[0] == 'sep_beamset_iso':
PF.create_additional_palliative_beamsets_prescriptions_and_beams(plan, examination, ss, region_codes, prescription, external, energy_name, nr_existing_beams = last_beam_index, isocenter = isocenter)
else:
PF.create_additional_palliative_beamsets_prescriptions_and_beams(plan, examination, ss, region_codes, prescription, external, energy_name, nr_existing_beams = last_beam_index)
# Creates a 2 Gy x 8 boost beam set for breast patients, if indicated:
if SSF.breast_boost_is_indicated(ss, region_code):
PF.create_breast_boost_beamset(ss, plan, examination, isocenter, region_code, ROIS.ctv_sb.name, background_dose=int(round(prescription.total_dose)))
# Make sure that the original beam set (not this boost beam set) is loaded in the GUI:
infos = plan.QueryBeamSetInfo(Filter={'Name':'^'+beam_set_name+'$'})
plan.LoadBeamSet( BeamSetInfo=infos[0])
# Determines and sets up isodoses based on region code and fractionation:
CF.determine_isodoses(case, ss, region_code, prescription)
# Determine site:
site = SF.site(pm, examination, ss, plan, prescription, region_code, target, technique_name)
# Set up Clinical Goals:
es = plan.TreatmentCourse.EvaluationSetup
CG.setup_clinical_goals(ss, es, site, prescription, target)
# Loads the plan (done after beam set is created, as this is the only way the CT-images appears in Plan Design and Plan Optimization when the plan is loaded):
CF.load_plan(case, plan)
# Set up beams and optimization for breast patients:
if technique_name == 'VMAT' and region_code in RC.breast_reg_codes:
# Use robust optimization for VMAT breast:
OBJF.set_robustness_breast(plan, region_code)
elif technique_name == '3D-CRT' and region_code in RC.breast_codes:
if region_code in RC.breast_reg_codes:
BSF.set_up_beams_and_optimization_for_regional_breast(plan, beam_set, ROIS.ptv_c.name, region_code)
else:
BSF.set_up_beams_and_optimization_for_tangential_breast(plan, beam_set, plan.PlanOptimizations[0], target.replace("C", "P")+"c")
# Configures the 2 Gy x 8 boost for breast patients, if indicated:
if SSF.breast_boost_is_indicated(ss, region_code):
BSF.set_up_beams_and_optimization_for_tangential_breast(plan, plan.BeamSets[1], plan.PlanOptimizations[1], ROIS.ptv_sbc.name)
# Set up treat and protect for stereotactic lung:
#if region_code in RC.lung_codes and prescription.is_stereotactic():
# BSF.set_up_treat_and_protect_for_stereotactic_lung(beam_set, target, 0.5)
# Run first optimization on each beam set:
for plan_optimization in plan.PlanOptimizations:
# Optimization parameters to be used on all cases (3D-CRT and VMAT):
plan_optimization.OptimizationParameters.DoseCalculation.ComputeFinalDose = True
# Configure optimization parameters for VMAT only:
if "Arc" in plan_optimization.OptimizedBeamSets[0].DeliveryTechnique:
optimization_parameters = OPT.optimization_parameters(region_code, prescription.fraction_dose)
optimization_parameters.apply_to(plan_optimization)
# Run the optimization (may crash if GPU for computation is not available):
try:
plan_optimization.RunOptimization()
except:
GUIF.handle_failed_dose_computation()
# Start adaptive optimization if indicated:
if opt == 'oar':
try:
OBJF.adapt_optimization_oar(ss, plan, site.oar_objectives, region_code)
except:
GUIF.handle_failed_dose_computation()
if region_code in RC.breast_codes and technique_name != 'VMAT':
# Modify leaves for the open fields in non-VMAT breast:
if region_code in RC.breast_reg_codes:
# Close leaves behind jaw for breast regional patients:
BSF.close_leaves_behind_jaw_for_regional_breast(beam_set)
# Create 2.5 cm margin to air for breast patient planned with a 3D-CRT technique (for robustness purpuses):
BSF.create_margin_air_for_3dcrt_breast(ss, beam_set, region_code)
# Compute dose:
try:
beam_set.ComputeDose(DoseAlgorithm = 'CCDose')
except:
GUIF.handle_failed_dose_computation()
# Auto scale to prescription:
for plan_optimization in plan.PlanOptimizations:
plan_optimization.AutoScaleToPrescription = True
# Load plan:
CF.load_plan(case, plan)
# Save:
patient.Save()
def create_widgets(self, ss):
# Parameter for width of the label in characters
w = 13
# Parameter that limit the number of characters in each line by setting this option to the desired number
wl = 300
# Create label
self.l1 = Label(
self,
text="Regionkode, flere målvolum (må være forskjellig!)",
font=("TkDefaultFont", 10),
anchor=W,
width=36,
bg='white')
# Number of targets (PTVs)
self.nr_targets = SSF.determine_nr_of_indexed_ptvs(ss)
# Declear list to hold the input text
self.textbox = []
# Create the number of textboxes and labels based on number of PTVs
for i in range(self.nr_targets - 1):
# Label
l2 = Label(self,
text="PTV" + str(i + 2) + ":",
font=("TkDefaultFont", 10),
width=w,
bg='white')
# Textbox entry
entry = Entry(self,
textvariable="",
width=13,
bg='white',
font=("TkDefaultFont", 10))
# Place label and entry
l2.grid(row=i + 1, column=0, padx=15, pady=10)
entry.grid(row=i + 1, column=1, padx=15, pady=10)
# Store entry in list
self.textbox.append(entry)
# Create OK and Cancel buttons
self.okButton = Button(self,
text="OK",
command=self.ok_clicked,
font=("TkDefaultFont", 10),
bg='white')
self.quitButton = Button(self,
text="Cancel",
command=self.cancel_clicked,
font=("TkDefaultFont", 10),
bg='white')
# Place label
self.l1.grid(row=0, column=0, padx=10, pady=10, columnspan=2)
# Place OK and Cancel buttons
self.okButton.grid(row=self.nr_targets,
column=0,
padx=20,
pady=15,
ipadx=10,
sticky=E)
self.quitButton.grid(row=self.nr_targets,
column=1,
padx=20,
pady=15,
sticky=W)
