def py_planning_item():
plan_dict = PyDicomParser(filename=rp).GetPlan()
rs_dvh = os.path.join(DATA_DIR, 'RS_dvh.dcm')
structures_tmp1 = PyDicomParser(filename=rs_dvh).GetStructures()
criteria1 = pd.read_excel(file_path, sheet_name='calc_dvh')
rt_case_tmp = RTCase("H&N", 123, structures_tmp1, criteria1)
dose_values = PyDicomParser(filename=rd).get_dose_matrix()
grid = PyDicomParser(filename=rd).get_grid_3d()
dose = Dose3D(dose_values, grid, pq.Gy)
dvh_calc = DVHCalculator(rt_case_tmp, calculation_options)
return PyPlanningItem(plan_dict, rt_case_tmp, dose, dvh_calc)
def test_dvh_comparisson(setup_calculation_options):
# Given plan DVH
folder = os.path.join(DATA_DIR, 'lungSBRT')
# parse DICOM folder
# plan_folder = r'C:\Users\Victor\Dropbox\Plan_Competition_Project\tests\tests_data\pinnacle\plan1'
plan_folder = r'C:\Users\Victor\Dropbox\Plan_Competition_Project\tests\tests_data\pinnacle\plan2'
dcm_files, flag = get_participant_folder_data(plan_folder)
rs_dvh = os.path.join(folder, 'RS.dcm')
plan_dict = PyDicomParser(filename=dcm_files['rtplan']).GetPlan()
# structures = PyDicomParser(filename=dcm_files['rtss']).GetStructures()
structures = PyDicomParser(filename=rs_dvh).GetStructures()
rd_dcm = PyDicomParser(filename=dcm_files['rtdose'])
dose = rd_dcm.get_dose_3d()
# criteria file
file_path = os.path.join(folder, 'Scoring_criteria.xlsx')
criteria = pd.read_excel(file_path, sheet_name='BiLateralLungSBRTCase')
# setup RT case
rt_case_tmp = RTCase("SBRT", 123, structures, criteria)
dvh_calc = DVHCalculator(rt_case_tmp, setup_calculation_options)
# when calculate DVH using pyplanScoring
planning_obj = PyPlanningItem(plan_dict, rt_case_tmp, dose, dvh_calc)
planning_obj.calculate_dvh()
# for k, v in planning_obj.dvh_data.items():
# plot_dvh(v, v['name'])
# plt.show()
# # Calculating the score
plan_eval = PlanEvaluation()
plan_eval.criteria = criteria
df = plan_eval.eval_plan(planning_obj)
raw_score = df['Raw score'].sum()
pass
def test_lung_case_dvh(setup_calculation_options):
# Given plan DVH
folder = os.path.join(DATA_DIR, 'lungSBRT')
# DVH reference
dvh_ref_path = os.path.join(folder,
'PlanIQ DVH - Ahmad Plan - March 12.txt')
plan_dvh = read_planiq_dvh(dvh_ref_path)
# parse DICOM folder
rp = os.path.join(folder, 'RP.dcm')
rs_dvh = os.path.join(folder, 'RS.dcm')
rd = os.path.join(folder, 'RD.dcm')
plan_dict = PyDicomParser(filename=rp).GetPlan()
structures = PyDicomParser(filename=rs_dvh).GetStructures()
dose_values = PyDicomParser(filename=rd).get_dose_matrix()
grid = PyDicomParser(filename=rd).get_grid_3d()
dose = Dose3D(dose_values, grid, pq.Gy)
# criteria file
file_path = os.path.join(folder, 'Scoring_criteria.xlsx')
criteria = pd.read_excel(file_path, sheet_name='BiLateralLungSBRTCase')
# setup RT case
rt_case_tmp = RTCase("SBRT", 123, structures, criteria)
dvh_calc = DVHCalculator(rt_case_tmp, setup_calculation_options)
# when calculate DVH using pyplanScoring
planning_obj = PyPlanningItem(plan_dict, rt_case_tmp, dose, dvh_calc)
planning_obj.calculate_dvh()
# compare against PLANIQ
for roi_number, dvh in planning_obj.dvh_data.items():
name = dvh['name']
plot_dvh_comp(dvh, plan_dvh, name, folder)
# plt.show()
# compare clinical DVH data
#
dvhs = PyDicomParser(filename=rd).GetDVHs()
# compare against Eclipse DVH
dvh_calculated = planning_obj.dvh_data
res = {}
res_eclipse = {}
for roi_number in dvhs.keys():
if roi_number in dvh_calculated:
dvh_calc = dvh_calculated[roi_number]
planiq_dvh = plan_dvh[structures[roi_number]['name']]
x_calc = np.arange(len(dvh_calc['data'])) * float(
dvh_calc['scaling'])
x_planiq = plan_dvh.index
cmp = CurveCompare(x_planiq, planiq_dvh, x_calc, dvh_calc['data'])
# cmp.plot_results("PlanIQ", "PyPlanScoring", structures[roi_number]['name'])
# plt.show()
res[structures[roi_number]['name']] = cmp.stats_paper
# Eclipse
eclipse_dvh = dvhs[roi_number]
x_eclipse = np.arange(len(eclipse_dvh['data'])) * float(
eclipse_dvh['scaling'])
cmp1 = CurveCompare(x_eclipse, eclipse_dvh['data'], x_calc,
dvh_calc['data'])
# cmp.plot_results("PlanIQ", "PyPlanScoring", structures[roi_number]['name'])
res_eclipse[structures[roi_number]['name']] = cmp1.stats_paper
res_plan_iq = pd.DataFrame.from_dict(res)
res_plan_iq.to_csv(os.path.join(folder, "PyPlanScoringXPlanIQ.csv"))
res_eclipsedf = pd.DataFrame.from_dict(res_eclipse)
res_eclipsedf.to_csv(os.path.join(folder, "PyPlanScoringXEclipse.csv"))
piq = [
res_plan_iq.min().min(),
res_plan_iq.max().max(),
res_plan_iq.mean().mean()
]
ecl = [
res_eclipsedf.min().min(),
res_eclipsedf.max().max(),
res_eclipsedf.mean().mean()
]
# # # then Get dvh data and compare with plan_data
# for roi_number, dvh in planning_obj.dvh_data.items():
# name = dvh['name']
# plot_dvh_comp(dvh, plan_dvh, name, folder)
#
# plt.show()
#
# # compare it abainst eclipse data
# # compare with TPS DVH
dvhs = PyDicomParser(filename=rd).GetDVHs()
# compare against Eclipse DVH
dvh_calculated = planning_obj.dvh_data
for roi_number in dvhs.keys():
if roi_number in dvh_calculated:
plot_dvh_comp1(dvh_calculated[roi_number], dvhs[roi_number],
structures[roi_number]['name'], folder)
# plt.show()
# # lung SBRT case
#
# # Calculating the score
plan_eval = PlanEvaluation()
plan_eval.criteria = criteria
df = plan_eval.eval_plan(planning_obj)
assert df['Raw score'].sum() == 88.612
def setup_case(self, rs_file_path, file_path, sheet_name):
structures = PyDicomParser(filename=rs_file_path).GetStructures()
self._plan_eval.read(file_path, sheet_name=sheet_name)
# todo implement setup case by id
self._case = RTCase(sheet_name, 1, structures,
self._plan_eval.criteria)
rtss = r'/home/victor/Dropbox/Plan_Competition_Project/tests/tests_data/ptv_based_SRS/RS.1.2.246.352.205.5644208623194161296.369175407993399980.dcm'
rt_dose = '/home/victor/Dropbox/Plan_Competition_Project/tests/tests_data/ptv_based_SRS/RD.1.2.246.352.71.7.584747638204.1898898.20180324182516.dcm'
ini_file = '/home/victor/Dropbox/Plan_Competition_Project/gui/cases/BrainMetSRSCase/PyPlanScoring.ini'
setup_calculation_options = get_calculation_options(ini_file)
structures = PyDicomParser(filename=rtss).GetStructures()
rd_dcm = PyDicomParser(filename=rt_dose)
dose = rd_dcm.get_dose_3d()
plan_dict = {}
# criteria file
file_path = '/home/victor/Dropbox/Plan_Competition_Project/tests/tests_data/ptv_based_SRS/Scoring_criteria.xlsx'
criteria = pd.read_excel(file_path, sheet_name='BrainSRS')
# setup RT case
rt_case_tmp = RTCase("SRS", 123, structures, criteria)
dvh_calc = DVHCalculator(rt_case_tmp, setup_calculation_options)
# when calculate DVH using pyplanScoring
planning_obj = PyPlanningItem(plan_dict, rt_case_tmp, dose, dvh_calc)
planning_obj.calculate_dvh()
# # Calculating the score
plan_eval = PlanEvaluation()
plan_eval.criteria = criteria
df = plan_eval.eval_plan(planning_obj)
score = df['Raw score'].sum()
# compare DVHs
# TPS
# dvhs = rd_dcm.GetDVHs()
def dvh_calculator():
rs_dvh = os.path.join(DATA_DIR, 'RS_dvh.dcm')
structures_tmp1 = PyDicomParser(filename=rs_dvh).GetStructures()
rt_case_tmp = RTCase("H&N", 123, structures_tmp1, criteria)
return DVHCalculator(rt_case_tmp, calculation_options)
def rt_case():
"""
Return RT case object
:return:
"""
return RTCase("H&N", 123, structures_tmp, criteria)