def rotation_wrapper(subj, side):
"""function calling all necessary steps; i.e. this function when called when initiating script is used to gather
all data, whereas for special functionality other modules can be called/used"""
input_folder = cfg['folders']['nifti']
CTimaging_orig, CTimaging_trans, _ = PrepareData.general_data(
subj, input_folder, side)
rotation, UnitVector = PrepareData.lead_details(
subj, input_folder, side, rotation=Configuration.rotation_dict_mod())
angles, intensities_angle, vector, peak, markerfft, valleys, finalpeak = \
[{k: [] for k in rotation['slices'].keys()} for _ in range(7)] # Pre-allocate data
for lvl in rotation['slices'].keys():
angles[lvl], intensities_angle[lvl], vector[lvl], peak[lvl], markerfft[lvl], valleys[lvl], finalpeak[lvl] = \
PrepareData.get_intensities(lvl, rotation['slices'][lvl], CTimaging_trans)
rotation['vector'] = vector
rotation['markerfft'] = markerfft['marker']
rotContact_coords = dict([(k, r)
for k, r in rotation['coordinates'].items()
if k.startswith('level')])
rotation = PrepareData.rotation_estimate(
UnitVector, angles['marker'], finalpeak['marker'], intensities_angle,
rotContact_coords, valleys['marker'], CTimaging_orig, rotation)
return rotation
def function_wrapper(subj, side, lead_data=''):
"""function calling all necessary steps; i.e. this function when called when initiating script is used to gather
all data, whereas for special functionality other modules can be called/used. The steps are as follows:
a) load_LeadData, b) get_CTimaging, c) axialSlices (cf. lead_details) per level, d) get_intensities,
e) rotation_estimate """
input_folder = cfg['folders']['nifti'] # default NIFTI-folder
if not lead_data:
lead_data, _, _ = General.load_leadData(subj,
input_folder=input_folder,
side=side)
if lead_data['model'] not in ('Boston Vercise Directional', 'St Jude 6172',
'St Jude 6173'):
print("\n\t...Rotation not required given 'non-directional' leads")
rotation = {'angle': -math.pi / 10}
return rotation # rotation not necessary for non-directional leads
CTimaging_orig, CTimaging_trans = General.get_CTimaging(lead_data)
rotation, UnitVector = General.lead_details(
subj,
input_folder,
side=side,
x_offset=[0] * 3,
level_names=['marker', 'level1', 'level2'],
rotation=Configuration.rotation_dict_mod(),
single_lead=lead_data,
CTimaging_orig=CTimaging_orig,
CTimaging_trans=CTimaging_trans)
angles, intensities_angle, vector, peak, markerfft, valleys, finalpeak = \
[{k: [] for k in rotation['slices'].keys()} for _ in range(7)] # Pre-allocate data
for lvl in rotation['slices'].keys():
angles[lvl], intensities_angle[lvl], vector[lvl], peak[lvl], markerfft[lvl], valleys[lvl], finalpeak[lvl] = \
General.get_intensities(lvl, rotation['slices'][lvl], CTimaging_trans)
rotation['vector'] = vector
rotation['markerfft'] = markerfft['marker']
rotContact_coords = dict([(k, r)
for k, r in rotation['coordinates'].items()
if k.startswith('level')])
rotation = General.rotation_estimate(UnitVector, angles['marker'],
finalpeak['marker'],
intensities_angle, rotContact_coords,
valleys['marker'], CTimaging_orig,
rotation)
return rotation
def lead_details(subj,
input_folder,
side='right',
x_offset=[0] * 3,
level_names=['marker', 'level1', 'level2'],
rotation='',
single_lead='',
CTimaging_orig='',
CTimaging_trans=''):
"""wrapper to retrieve default data for coordinates, slices, fitvolumes and boxes
which are sorted in a sequential fashion; enabling changes as desired at later stages"""
# Pre-allocate space and get 'empty' rotation dictionary if no input was provided
if not rotation:
rotation = Configuration.rotation_dict_mod()
if not single_lead:
single_lead, _, _ = General.load_leadData(
subj, input_folder=input_folder, side=side)
if not CTimaging_orig or not CTimaging_trans:
CTimaging_orig, CTimaging_trans = General.get_CTimaging(
single_lead)
slices, boxes, fitvolumes, marker_coord, directional_coord, UnitVector = [
dict() for _ in range(6)
]
for idx, k in enumerate(level_names):
slices[k], boxes[k], fitvolumes[k], directional_coord[k], UnitVector = \
Specific.AxialSlices_levelwise(single_lead, CTimaging_trans, CTimaging_orig, x_offset=x_offset[idx],
extractradius=30, part2lookfor=k, level_dist=int(idx))
# according to original script, slices [artifact_marker, artifact_dirX] MUST be flipped. Rationale for this
# is not quite clear; cf. https://github.com/netstim/leaddbs/blob/master/ea_orient_main.m lines 138ff.
for k, v in slices.items():
slices[k] = np.fliplr(v)
# Update and return rotation dictionary for further processing
for level in level_names:
rotation['coordinates'][level] = directional_coord[level]
rotation['plot_box'][level] = boxes[level]
rotation['slices'][level] = slices[level]
rotation['fitvolumes'][level] = fitvolumes[level]
return rotation, UnitVector
def PaCER_script(subjects, inputfolder=''):
"""wrapper script for all steps included in the PaCER algorithm"""
print("\nLead detection of {} subject(s)".format(len(subjects)))
inputfolder = cfg['folders']['nifti'] if not inputfolder else inputfolder # select default input folder
LW = LeadWorks() # load the class including necessary functions
# Look for data files containing CT imaging including the brainMask and load this into workspace
available_files = FileOperations.get_filelist_as_tuple(inputdir=inputfolder, subjects=subjects)
regex2lookfor = 'reg_' + 'run[0-9]', 'brainmask_'
file_id_CTimaging = [file_tuple for file_tuple in available_files
if re.search(r'\w.({}).'.format(regex2lookfor[0]), file_tuple[0], re.IGNORECASE)
and file_tuple[0].endswith('.nii') and 'CT' in file_tuple[0]]
file_id_brainMask = [file_tuple for file_tuple in available_files
if re.search(r'\w.({}).'.format(regex2lookfor[1]), file_tuple[0], re.IGNORECASE)
and file_tuple[0].endswith('.nii')]
if any(t > 2 for t in [len(k) for k in file_id_CTimaging]):
print("More than one files for imaging or brainmask available. Please double-check!")
return
if not file_id_brainMask:
warnings.warn(message="\tNo brain mask was found, trying to obtain a mask using ANTSpyNET routines")
regex2lookforT1 = cfg['preprocess']['normalisation']['prefix'] + 'run'
file_id_T1 = [file_tuple for file_tuple in available_files
if re.search(r'\w.({}).'.format(regex2lookforT1), file_tuple[0], re.IGNORECASE)
and 't1' in file_tuple[0] and file_tuple[0].endswith('.nii')]
if not file_id_T1:
Output.msg_box(text='No T1-sequence imaging available. BrainMask extraction impossible.',
title='T1 sequences missing")')
return
else:
T1imaging = ants.image_read(file_id_T1[0][0])
file_id_brainMask = Imaging.create_brainmask(input_folder=inputfolder, subj=''.join(subjects),
registered_images=T1imaging)
file_id_brainMask = [file_id_brainMask] if type(file_id_brainMask) == tuple else file_id_brainMask
fileID = list(FileOperations.inner_join(file_id_brainMask, file_id_CTimaging)) # joins all to single list
metal_threshold = int(cfg['lead_detection']['PaCER']['metal_threshold'])
elecModels, intensityProfiles, skelSkalms = LW.electrodeEstimation(fileID[0], threshold=metal_threshold)
elecModels, skelSkalms, intensityProfiles, _ = \
LeadProperties.estimate_hemisphere(elecModels, intensityProfiles, skelSkalms) # returns hemisphere from coords.
filename_save = os.path.join(os.path.join(inputfolder, subjects[0]), 'elecModels_' + subjects[0] + '.pkl')
with open(filename_save, "wb") as f:
pickle.dump(elecModels, f)
pickle.dump(intensityProfiles, f)
pickle.dump(skelSkalms, f)
sides = ['left', 'right']
rotation_default, rotation_mod = [{k: [] for k in sides} for _ in range(2)]
for s in sides:
rotation_default[s] = function_wrapper(subj=subjects[0], side=s)
rotation_mod[s] = Configuration.rotation_dict_mod() # creates an empty array to save modified data later
filename_save = os.path.join(os.path.join(inputfolder, subjects[0]), 'rotation_' + subjects[0] + '.pkl')
with open(filename_save, "wb") as f:
pickle.dump(rotation_default, f)
pickle.dump(rotation_mod, f)
print("Finished with lead detection!")
# TODO: it does not return to the empty command line.
return
