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else: # scan already selected above
sequence = scan.Sequence[:scan.Sequence.find('N3') - 2]
original = False
if not qc_passed:
if scan.Sequence == 'MP-RAGE':
original_img_seq = 'MPR'
else: # 'MP-RAGE REPEAT'
original_img_seq = 'MPR-R'
processing_seq = qc_prev_sequence[qc_prev_sequence.find(';'):qc_prev_sequence.find('N3') - 2]
sequence = original_img_seq + processing_seq
# print sequence
sequence = replace_sequence_chars(sequence)
qc = mri_quality_subj[mri_quality_subj.LONIUID == 'S' + str(scan.SeriesID)]
if qc.shape[0] > 0 and qc.iloc[0].PASS != 1:
# TODO - LOG THIS
print 'QC found but NOT passed, NONONONO'
print 'Subject ' + subject_id + ' - Series: ' + str(scan.SeriesID) + ' - Study: ' + str(scan.StudyID)
return {'Subject_ID': subject_id,
'VISCODE': timepoint,
'Visit': visit_str,
'Sequence': sequence,
'Scan_Date': scan.ScanDate,
'Study_ID': str(scan.StudyID),
'Series_ID': str(scan.SeriesID),
'Field_Strength': scan.MagStrength,
'Original': original}
& (subject_pet_meta['Scan Date'] == qc_visit.EXAMDATE)]
if original_pet_meta.shape[0] < 1:
# TODO Log somewhere subjects with problems
print 'NO Screening: Subject - ' + subj + ' for visit ' + qc_visit.VISCODE2
continue
original_image = original_pet_meta.iloc[0]
averaged_pet_meta = subject_pet_meta[(subject_pet_meta['Sequence'] == 'Co-registered, Averaged') & (
subject_pet_meta['Series ID'] == original_image['Series ID'])]
if averaged_pet_meta.shape[0] < 1:
sel_image = original_image
original = True
else:
sel_image = averaged_pet_meta.iloc[0]
original = False
visit = sel_image.Visit
sequence = replace_sequence_chars(sel_image.Sequence)
date = sel_image['Scan Date']
study_id = sel_image['Study ID']
series_id = sel_image['Series ID']
image_id = sel_image['Image ID']
row_to_append = pd.DataFrame(
[[subj, qc_visit.VISCODE2, str(visit), sequence, date, str(study_id), str(series_id), str(image_id),
original]],
columns=pet_fdg_col)
pet_fdg_df = pet_fdg_df.append(row_to_append, ignore_index=True)
images = pet_fdg_df
# count = 0
# total = images.shape[0]
is_dicom = []
image_folders = []
lambda x: ((x.lower().find('mprage') > -1) | (x.lower().find('mp-rage') > -1) | (
x.lower().find('mp rage') > -1)) & (x.find('2') < 0)))
cond_spgr = ((mprage_meta_subj_orig.Visit == visit_str) & mprage_meta_subj_orig.Sequence.map(
lambda x: (x.lower().find('spgr') > -1) & (x.lower().find('acc') < 0)))
filtered_scan = mprage_meta_subj_orig[cond_mprage | cond_spgr]
if filtered_scan.shape[0] < 1:
# TODO - LOG THIS
print 'NO MPRAGE Meta2: ' + subject_id + ' for visit ' + timepoint + ' - ' + visit_str
return None
scan = select_scan_qc_adni2(filtered_scan, mayo_mri_qc_subj, preferred_field_strength)
sequence = replace_sequence_chars(scan.Sequence)
return {'Subject_ID': subject_id,
'VISCODE': timepoint,
'Visit': visit_str,
'Sequence': sequence,
'Scan_Date': scan.ScanDate,
'Study_ID': str(scan.StudyID),
'Series_ID': str(scan.SeriesID),
'Field_Strength': scan.MagStrength,
'Original': True}
def dti_image(subject_id, timepoint, visit_str, ida_meta_scans, mri_qc_subj, enhanced):
from clinica.bids.converters.adni_utils import replace_sequence_chars
sel_image = select_image_qc(list(ida_meta_scans.IMAGEUID), mri_qc_subj)
if sel_image is None:
return None
sel_scan = ida_meta_scans[ida_meta_scans.IMAGEUID == sel_image].iloc[0]
sequence = sel_scan.Sequence
sequence = replace_sequence_chars(sequence)
image_dict = {'Subject_ID': subject_id,
'VISCODE': timepoint,
'Visit': visit_str,
'Sequence': sequence,
'Scan_Date': sel_scan['Scan Date'],
'Study_ID': str(int(sel_scan.LONISID)),
'Series_ID': str(int(sel_scan.LONIUID)),
'Image_ID': str(int(sel_scan.IMAGEUID)),
'Field_Strength': sel_scan.MagStrength,
'Scanner': sel_scan.Scanner,
'Enhanced': enhanced}
return image_dict
& (subject_pet_meta['Scan Date'] == qc_visit.EXAMDATE)]
if original_pet_meta.shape[0] < 1:
# TODO Log somewhere subjects with problems
print 'NO Screening: Subject - ' + subj + ' for visit ' + qc_visit.VISCODE2
continue
original_image = original_pet_meta.iloc[0]
averaged_pet_meta = subject_pet_meta[(subject_pet_meta['Sequence'] == 'AV45 Co-registered, Averaged') & (
subject_pet_meta['Series ID'] == original_image['Series ID'])]
if averaged_pet_meta.shape[0] < 1:
sel_image = original_image
original = True
else:
sel_image = averaged_pet_meta.iloc[0]
original = False
visit = sel_image.Visit
sequence = replace_sequence_chars(sel_image.Sequence)
date = sel_image['Scan Date']
study_id = sel_image['Study ID']
series_id = sel_image['Series ID']
image_id = sel_image['Image ID']
row_to_append = pd.DataFrame(
[[subj, qc_visit.VISCODE2, str(visit), sequence, date, str(study_id), str(series_id), str(image_id),
original]],
columns=pet_av45_col)
pet_av45_df = pet_av45_df.append(row_to_append, ignore_index=True)
images = pet_av45_df
# count = 0
# total = images.shape[0]
is_dicom = []
image_folders = []