def create_rois(self):
# Delete pre-existing ROIs (except those which are manually contoured) in sorted order:
group = self.grouped_rois()
for key in sorted(iter(group)):
for roi in group[key]:
PMF.delete_matching_roi_except_manually_contoured(
self.pm, self.ss, roi)
group = self.grouped_rois()
# Create ROIs (in reverse sorted order):
for key in reversed(sorted(iter(group))):
for roi in group[key]:
# Only create ROI if it doesn't already exist:
if not PMF.has_roi(self.pm, roi.name):
if roi.__class__.__name__ == 'ROI':
if roi.model:
PMF.create_model_roi(self.pm, self.examination,
roi)
else:
PMF.create_empty_roi(self.pm, roi)
elif roi.__class__.__name__ == 'ROIExpanded':
PMF.create_expanded_roi(self.pm, self.examination,
self.ss, roi)
elif roi.__class__.__name__ == 'ROIAlgebra':
PMF.create_algebra_roi(self.pm, self.examination,
self.ss, roi)
elif roi.__class__.__name__ == 'ROIWall':
PMF.create_wall_roi(self.pm, self.examination, self.ss,
roi)
def roi_overlap(pm, examination, ss, roi1, roi2, threshold):
subtraction = ROI.ROIAlgebra(roi1.name + '-' + roi2.name,
'Undefined',
'Black',
sourcesA=[roi1],
sourcesB=[roi2],
operator='Subtraction')
# In the rare case that this ROI already exists, delete it (to avoid a crash):
PMF.delete_roi(pm, subtraction.name)
PMF.create_algebra_roi(pm, examination, ss, subtraction)
# Is overlapping volume less than threshold?
overlap = False
if ss.RoiGeometries[roi1.name].GetRoiVolume() - ss.RoiGeometries[
subtraction.name].GetRoiVolume() > threshold:
overlap = True
PMF.delete_roi(pm, subtraction.name)
return overlap
def create_roi_subtraction(pm, examination, ss, roi1, roi2, subtraction_name,
threshold):
overlap = False
if has_named_roi_with_contours(
ss, roi1.name) and has_named_roi_with_contours(ss, roi2.name):
subtraction = ROI.ROIAlgebra(subtraction_name,
'Undefined',
'Black',
sourcesA=[roi1],
sourcesB=[roi2],
operator='Subtraction')
# In the rare case that this ROI already exists, delete it (to avoid a crash):
PMF.delete_roi(pm, subtraction.name)
PMF.create_algebra_roi(pm, examination, ss, subtraction)
# Is overlapping volume less than threshold?
if has_named_roi_with_contours(ss, subtraction.name):
if ss.RoiGeometries[roi1.name].GetRoiVolume() - ss.RoiGeometries[
subtraction.name].GetRoiVolume() > threshold:
overlap = True
else:
GUIF.handle_missing_roi_for_derived_rois(subtraction_name, roi2.name)
return overlap
def create_expanded_and_intersected_volume(pm, examination, ss, source_roi,
intersect_roi, expanded_roi_name,
threshold_volume):
# Volume of source roi
volume1 = ss.RoiGeometries[source_roi.name].GetRoiVolume()
# Estimated radius of source roi calculated from volume (assuming perfect sphere)
radius1 = math.pow((volume1 * 3) / (4 * math.pi), 1.0 / 3.0)
# Estimated radius of expanded roi calculated from thresgold volume (assuming perfect sphere)
radius2 = math.pow((threshold_volume * 3) / (4 * math.pi), 1.0 / 3.0)
# Expansion radius
r = round(radius2 - radius1, 1)
# Expanded roi object
expanded_roi = ROI.ROIAlgebra(expanded_roi_name,
'Undefined',
'Black',
sourcesA=[source_roi],
sourcesB=[intersect_roi],
operator='Intersection',
marginsA=MARGIN.Expansion(r, r, r, r, r, r),
marginsB=MARGINS.zero)
# Deletes roi if it already exists in RayStation
PMF.delete_roi(pm, expanded_roi.name)
# Create ROI in RayStation
PMF.create_algebra_roi(pm, examination, ss, expanded_roi)
PMF.create_posterior_half(pm, examination, ss, ROIS.rectum, ROIS.dorso_rectum)
if selected_oar_list.get(ROIS.cornea_l) and selected_oar_list.get(ROIS.retina_l):
PMF.create_retina_and_cornea(pm, examination, ss, ROIS.lens_l, ROIS.box_l, ROIS.eye_l, ROIS.retina_l, ROIS.cornea_l)
elif selected_oar_list.get(ROIS.retina_l) and not selected_oar_list.get(ROIS.cornea_l):
PMF.create_retina(pm, examination, ss, ROIS.lens_l, ROIS.box_l, ROIS.eye_l, ROIS.retina_l)
elif selected_oar_list.get(ROIS.cornea_l) and not selected_oar_list.get(ROIS.retina_l):
PMF.create_cornea(pm, examination, ss, ROIS.lens_l, ROIS.box_l, ROIS.eye_l, ROIS.cornea_l)
if selected_oar_list.get(ROIS.cornea_r) and selected_oar_list.get(ROIS.retina_r):
PMF.create_retina_and_cornea(pm, examination, ss, ROIS.lens_r, ROIS.box_r, ROIS.eye_r, ROIS.retina_r, ROIS.cornea_r)
elif selected_oar_list.get(ROIS.retina_r) and not selected_oar_list.get(ROIS.cornea_r):
PMF.create_retina(pm, examination, ss, ROIS.lens_r, ROIS.box_r, ROIS.eye_r, ROIS.retina_r)
elif selected_oar_list.get(ROIS.cornea_r) and not selected_oar_list.get(ROIS.retina_r):
PMF.create_cornea(pm, examination, ss, ROIS.lens_r, ROIS.box_r, ROIS.eye_r, ROIS.cornea_r)
# Create ROIs:
for roi in reversed(list(selected_oar_list)):
# Only create ROI if it doesn't already exist:
if not PMF.has_roi(pm, roi.name):
if roi.__class__.__name__ == 'ROI':
if roi.model:
PMF.create_model_roi(pm, examination, roi)
else:
PMF.create_empty_roi(pm, roi)
elif roi.__class__.__name__ == 'ROIExpanded':
PMF.create_expanded_roi(pm, examination, ss, roi)
elif roi.__class__.__name__ == 'ROIAlgebra':
PMF.create_algebra_roi(pm, examination, ss, roi)
elif roi.__class__.__name__ == 'ROIWall':
PMF.create_wall_roi(pm, examination, ss, roi)
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)
