def get_total_treatment_volume_of_study(study_instance_uid):
"""
Calculate combined PTV for the provided study_instance_uid
"""
ptv_coordinates_strings = query('dvhs', 'roi_coord_string',
"study_instance_uid = '%s' and roi_type like 'PTV%%'" % study_instance_uid)
ptvs = [roi_form.get_planes_from_string(ptv[0]) for ptv in ptv_coordinates_strings]
return roi_geom.union(ptvs)
def get_total_treatment_volume_of_study(study_instance_uid, ptvs=None):
"""
Calculate combined PTV for the provided study_instance_uid
"""
condition = "study_instance_uid = '%s' and roi_type like 'PTV%%'" % study_instance_uid
if ptvs:
condition += " and roi_name in ('%s')" % "','".join(ptvs)
ptv_coordinates_strings = query('dvhs', 'roi_coord_string', condition)
ptvs = [
roi_form.get_planes_from_string(ptv[0])
for ptv in ptv_coordinates_strings
]
return roi_geom.union(ptvs)
def min_distances(study_instance_uid, roi_name, pre_calc=None):
"""
Recalculate the min, mean, median, and max PTV distances an roi based on data in the SQL DB.
Optionally provide coordinates of combined PTV, return from get_treatment_volume_coord
"""
oar_coordinates_string = query('dvhs', 'roi_coord_string',
"study_instance_uid = '%s' and roi_name = '%s'" % (study_instance_uid, roi_name))
treatment_volume_coord = pre_calc
if treatment_volume_coord is None:
with DVH_SQL() as cnx:
ptv_coordinates_strings = cnx.query('dvhs', 'roi_coord_string',
"study_instance_uid = '%s' and roi_type like 'PTV%%'"
% study_instance_uid)
ptvs = [roi_form.get_planes_from_string(ptv[0]) for ptv in ptv_coordinates_strings]
treatment_volume_coord = roi_form.get_roi_coordinates_from_planes(roi_geom.union(ptvs))
oar_coordinates = roi_form.get_roi_coordinates_from_string(oar_coordinates_string[0][0])
treatment_volume_coord = sample_roi(treatment_volume_coord)
oar_coordinates = sample_roi(oar_coordinates)
try:
data = roi_geom.min_distances_to_target(oar_coordinates, treatment_volume_coord)
except MemoryError:
try:
treatment_volume_coord = sample_roi(treatment_volume_coord, max_point_count=3000)
oar_coordinates = sample_roi(oar_coordinates, max_point_count=3000)
data = roi_geom.min_distances_to_target(oar_coordinates, treatment_volume_coord)
except MemoryError as e:
print("Memory Error: ", e)
print('Error reported for %s with study_instance_uid %s' % (roi_name, study_instance_uid))
print('Skipping PTV distance and DTH calculations for this ROI.')
data = None
except Exception as e:
print('Error: ', e)
print('Error reported for %s with study_instance_uid %s' % (roi_name, study_instance_uid))
print('Skipping PTV distance and DTH calculations for this ROI.')
data = None
if data is not None:
try:
dth = roi_geom.dth(data)
dth_string = ','.join(['%.3f' % num for num in dth])
data_map = {'dist_to_ptv_min': round(float(np.min(data)), 2),
'dist_to_ptv_mean': round(float(np.mean(data)), 2),
'dist_to_ptv_median': round(float(np.median(data)), 2),
'dist_to_ptv_max': round(float(np.max(data)), 2),
'dth_string': dth_string}
except MemoryError as e:
print("Memory Error: ", e)
print('Error reported for %s with study_instance_uid %s' % (roi_name, study_instance_uid))
print('Skipping PTV distance and DTH calculations for this ROI.')
data_map = None
if data_map:
for key, value in data_map.items():
update_dvhs_table(study_instance_uid, roi_name, key, value)