def compute_pib_pet_paths(source_dir, csv_dir, dest_dir, subjs_list):
"""Compute the paths to the PIB PET images and store them in a tsv file
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
Returns:
images: a dataframe with all the paths to the PET images that will be converted into BIDS
"""
import pandas as pd
import os
from os import path
from clinica.iotools.converters.adni_to_bids.adni_utils import get_images_pet, find_image_path
pet_pib_col = [
'Phase', 'Subject_ID', 'VISCODE', 'Visit', 'Sequence', 'Scan_Date',
'Study_ID', 'Series_ID', 'Image_ID', 'Original'
]
pet_pib_df = pd.DataFrame(columns=pet_pib_col)
pet_pib_dfs_list = []
# Loading needed .csv files
pibqc = pd.read_csv(path.join(csv_dir, 'PIBQC.csv'),
sep=',',
low_memory=False)
pet_meta_list = pd.read_csv(path.join(csv_dir, 'PET_META_LIST.csv'),
sep=',',
low_memory=False)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list['Subject'] == subj]
if subject_pet_meta.empty:
continue
# QC for PIB PET images
pet_qc_subj = pibqc[(pibqc.PASS == 1) & (pibqc.RID == int(subj[-4:]))]
sequences_preprocessing_step = ['PIB Co-registered, Averaged']
subj_dfs_list = get_images_pet(subj,
pet_qc_subj,
subject_pet_meta,
pet_pib_col,
'PIB-PET',
sequences_preprocessing_step,
viscode_field="VISCODE")
if subj_dfs_list:
pet_pib_dfs_list += subj_dfs_list
if pet_pib_dfs_list:
pet_pib_df = pd.concat(pet_pib_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = []
# Removing known exceptions from images to convert
if not pet_pib_df.empty:
error_ind = pet_pib_df.index[pet_pib_df.apply(lambda x: (
(x.Subject_ID, x.VISCODE) in conversion_errors),
axis=1)]
pet_pib_df.drop(error_ind, inplace=True)
images = find_image_path(pet_pib_df, source_dir, 'PIB', 'I', 'Image_ID')
pib_csv_path = path.join(dest_dir, 'conversion_info')
if not os.path.exists(pib_csv_path):
os.mkdir(pib_csv_path)
images.to_csv(path.join(pib_csv_path, 'pib_pet_paths.tsv'),
sep='\t',
index=False)
return images
def compute_pib_pet_paths(source_dir, csv_dir, dest_dir, subjs_list,
conversion_dir):
"""Compute the paths to the PIB PET images and store them in a TSV file.
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
conversion_dir: path to the TSV files including the paths to original images
Returns:
images: a dataframe with all the paths to the PET images that will be converted into BIDS
"""
from os import path
import pandas as pd
from clinica.iotools.converters.adni_to_bids.adni_utils import (
find_image_path,
get_images_pet,
)
pet_pib_col = [
"Phase",
"Subject_ID",
"VISCODE",
"Visit",
"Sequence",
"Scan_Date",
"Study_ID",
"Series_ID",
"Image_ID",
"Original",
]
pet_pib_df = pd.DataFrame(columns=pet_pib_col)
pet_pib_dfs_list = []
# Loading needed .csv files
pibqc = pd.read_csv(path.join(csv_dir, "PIBQC.csv"),
sep=",",
low_memory=False)
pet_meta_list = pd.read_csv(path.join(csv_dir, "PET_META_LIST.csv"),
sep=",",
low_memory=False)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == subj]
if subject_pet_meta.empty:
continue
# QC for PIB PET images
pet_qc_subj = pibqc[(pibqc.PASS == 1) & (pibqc.RID == int(subj[-4:]))]
sequences_preprocessing_step = ["PIB Co-registered, Averaged"]
subj_dfs_list = get_images_pet(
subj,
pet_qc_subj,
subject_pet_meta,
pet_pib_col,
"PIB-PET",
sequences_preprocessing_step,
viscode_field="VISCODE",
)
if subj_dfs_list:
pet_pib_dfs_list += subj_dfs_list
if pet_pib_dfs_list:
pet_pib_df = pd.concat(pet_pib_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = []
# Removing known exceptions from images to convert
if not pet_pib_df.empty:
error_ind = pet_pib_df.index[pet_pib_df.apply(lambda x: (
(x.Subject_ID, x.VISCODE) in conversion_errors),
axis=1)]
pet_pib_df.drop(error_ind, inplace=True)
images = find_image_path(pet_pib_df, source_dir, "PIB", "I", "Image_ID")
images.to_csv(path.join(conversion_dir, "pib_pet_paths.tsv"),
sep="\t",
index=False)
return images
def compute_av45_fbb_pet_paths(
source_dir, csv_dir, dest_dir, subjs_list, conversion_dir
):
"""Compute the paths to the AV45 and Florbetaben PET images and store them in a TSV file.
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
conversion_dir: path to the TSV files including the paths to original images
Returns:
images: a dataframe with all the paths to the PET images that will be converted into BIDS
"""
from os import path
import pandas as pd
from clinica.iotools.converters.adni_to_bids.adni_utils import (
find_image_path,
get_images_pet,
)
pet_amyloid_col = [
"Phase",
"Subject_ID",
"VISCODE",
"Visit",
"Sequence",
"Scan_Date",
"Study_ID",
"Series_ID",
"Image_ID",
"Original",
"Tracer",
]
pet_amyloid_df = pd.DataFrame(columns=pet_amyloid_col)
pet_amyloid_dfs_list = []
# Loading needed .csv files
av45qc = pd.read_csv(path.join(csv_dir, "AV45QC.csv"), sep=",", low_memory=False)
amyqc = pd.read_csv(path.join(csv_dir, "AMYQC.csv"), sep=",", low_memory=False)
pet_meta_list = pd.read_csv(
path.join(csv_dir, "PET_META_LIST.csv"), sep=",", low_memory=False
)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == subj]
if subject_pet_meta.empty:
continue
# QC for AV45 PET images for ADNI 1, GO and 2
av45_qc_subj = av45qc[(av45qc.PASS == 1) & (av45qc.RID == int(subj[-4:]))]
# QC for Amyloid PET images for ADNI 3
amy_qc_subj = amyqc[(amyqc.SCANQLTY == 1) & (amyqc.RID == int(subj[-4:]))]
amy_qc_subj.insert(0, "EXAMDATE", amy_qc_subj.SCANDATE.to_list())
# Concatenating visits in both QC files
amyloid_qc_subj = pd.concat(
[av45_qc_subj, amy_qc_subj], axis=0, ignore_index=True, sort=False
)
sequences_preprocessing_step = [
"AV45 Co-registered, Averaged",
"FBB Co-registered, Averaged",
]
subj_dfs_list = get_images_pet(
subj,
amyloid_qc_subj,
subject_pet_meta,
pet_amyloid_col,
"Amyloid-PET",
sequences_preprocessing_step,
)
if subj_dfs_list:
pet_amyloid_dfs_list += subj_dfs_list
if pet_amyloid_dfs_list:
pet_amyloid_df = pd.concat(pet_amyloid_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = [ # Eq_1
("128_S_2220", "m48"),
# Several output images
("098_S_4275", "m84"),
]
# Removing known exceptions from images to convert
if not pet_amyloid_df.empty:
error_ind = pet_amyloid_df.index[
pet_amyloid_df.apply(
lambda x: ((x.Subject_ID, x.VISCODE) in conversion_errors), axis=1
)
]
pet_amyloid_df.drop(error_ind, inplace=True)
images = find_image_path(pet_amyloid_df, source_dir, "Amyloid", "I", "Image_ID")
images.to_csv(
path.join(conversion_dir, "amyloid_pet_paths.tsv"), sep="\t", index=False
)
return images
def compute_fdg_pet_paths(source_dir, csv_dir, dest_dir, subjs_list):
"""Compute the paths to the FDG PET images and store them in a tsv file
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
Returns:
images: a dataframe with all the paths to the PET images that will be converted into BIDS
"""
import pandas as pd
import os
from os import path
from clinica.iotools.converters.adni_to_bids.adni_utils import get_images_pet, find_image_path
pet_fdg_col = ['Phase', 'Subject_ID', 'VISCODE', 'Visit', 'Sequence', 'Scan_Date', 'Study_ID',
'Series_ID', 'Image_ID', 'Original']
pet_fdg_df = pd.DataFrame(columns=pet_fdg_col)
pet_fdg_dfs_list = []
# Loading needed .csv files
petqc = pd.read_csv(path.join(csv_dir, 'PETQC.csv'), sep=',', low_memory=False)
petqc3 = pd.read_csv(path.join(csv_dir, 'PETC3.csv'), sep=',', low_memory=False)
pet_meta_list = pd.read_csv(path.join(csv_dir, 'PET_META_LIST.csv'), sep=',', low_memory=False)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list['Subject'] == subj]
if subject_pet_meta.empty:
continue
# QC for FDG PET images for ADNI 1, GO and 2
pet_qc_1go2_subj = petqc[(petqc.PASS == 1) & (petqc.RID == int(subj[-4:]))]
# QC for FDG PET images for ADNI 3
pet_qc3_subj = petqc3[(petqc3.SCANQLTY == 1) & (petqc3.RID == int(subj[-4:]))]
pet_qc3_subj.insert(0, 'EXAMDATE', pet_qc3_subj.SCANDATE.to_list())
# Concatenating visits in both QC files
pet_qc_subj = pd.concat([pet_qc_1go2_subj, pet_qc3_subj], axis=0, ignore_index=True, sort=False)
sequences_preprocessing_step = ['Co-registered, Averaged']
subj_dfs_list = get_images_pet(subj, pet_qc_subj, subject_pet_meta, pet_fdg_col, 'FDG-PET',
sequences_preprocessing_step)
if subj_dfs_list:
pet_fdg_dfs_list += subj_dfs_list
if pet_fdg_dfs_list:
# Concatenating dataframes into one
pet_fdg_df = pd.concat(pet_fdg_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = [ # NONAME.nii
('031_S_0294', 'bl'),
('037_S_1421', 'm36'),
('037_S_1078', 'm36'),
# Empty folders
('941_S_1195', 'm48'),
('005_S_0223', 'm12')]
# Removing known exceptions from images to convert
if not pet_fdg_df.empty:
error_ind = pet_fdg_df.index[pet_fdg_df.apply(lambda x: ((x.Subject_ID, x.VISCODE) in conversion_errors),
axis=1)]
pet_fdg_df.drop(error_ind, inplace=True)
images = find_image_path(pet_fdg_df, source_dir, 'FDG', 'I', 'Image_ID')
fdg_csv_path = path.join(dest_dir, 'conversion_info')
if not os.path.exists(fdg_csv_path):
os.mkdir(fdg_csv_path)
images.to_csv(path.join(fdg_csv_path, 'fdg_pet_paths.tsv'), sep='\t', index=False)
return images
def compute_tau_pet_paths(source_dir, csv_dir, dest_dir, subjs_list, conversion_dir):
"""Compute the paths to Tau PET images.
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
conversion_dir: path to the TSV files including the paths to original images
Returns: pandas Dataframe containing the path for each Tau PET image
"""
from os import path
import pandas as pd
from clinica.iotools.converters.adni_to_bids.adni_utils import (
find_image_path,
get_images_pet,
)
pet_tau_col = [
"Phase",
"Subject_ID",
"VISCODE",
"Visit",
"Sequence",
"Scan_Date",
"Study_ID",
"Series_ID",
"Image_ID",
"Original",
]
pet_tau_df = pd.DataFrame(columns=pet_tau_col)
pet_tau_dfs_list = []
# Loading needed .csv files
tauqc = pd.read_csv(path.join(csv_dir, "TAUQC.csv"), sep=",", low_memory=False)
tauqc3 = pd.read_csv(path.join(csv_dir, "TAUQC3.csv"), sep=",", low_memory=False)
pet_meta_list = pd.read_csv(
path.join(csv_dir, "PET_META_LIST.csv"), sep=",", low_memory=False
)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == subj]
if subject_pet_meta.empty:
continue
# QC for TAU PET images for ADNI 2
tau_qc2_subj = tauqc[(tauqc.SCANQLTY == 1) & (tauqc.RID == int(subj[-4:]))]
# QC for TAU PET images for ADNI 3
tau_qc3_subj = tauqc3[(tauqc3.SCANQLTY == 1) & (tauqc3.RID == int(subj[-4:]))]
# Concatenating visits in both QC files
tau_qc_subj = pd.concat(
[tau_qc2_subj, tau_qc3_subj], axis=0, ignore_index=True, sort=False
)
tau_qc_subj.rename(columns={"SCANDATE": "EXAMDATE"}, inplace=True)
sequences_preprocessing_step = ["AV1451 Co-registered, Averaged"]
subj_dfs_list = get_images_pet(
subj,
tau_qc_subj,
subject_pet_meta,
pet_tau_col,
"TAU-PET",
sequences_preprocessing_step,
)
if subj_dfs_list:
pet_tau_dfs_list += subj_dfs_list
if pet_tau_dfs_list:
pet_tau_df = pd.concat(pet_tau_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = [("098_S_4275", "m84")] # Multiple output images
# Removing known exceptions from images to convert
if not pet_tau_df.empty:
error_ind = pet_tau_df.index[
pet_tau_df.apply(
lambda x: ((x.Subject_ID, x.VISCODE) in conversion_errors), axis=1
)
]
pet_tau_df.drop(error_ind, inplace=True)
# Checking for images paths in filesystem
images = find_image_path(pet_tau_df, source_dir, "TAU", "I", "Image_ID")
images.to_csv(path.join(conversion_dir, "tau_pet_paths.tsv"), sep="\t", index=False)
return images
def compute_tau_pet_paths(source_dir, csv_dir, dest_dir, subjs_list):
"""
Compute the paths to Tau PET images
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
Returns: pandas Dataframe containing the path for each Tau PET image
"""
import pandas as pd
import os
from os import path
from clinica.iotools.converters.adni_to_bids.adni_utils import get_images_pet, find_image_path
pet_tau_col = [
'Phase', 'Subject_ID', 'VISCODE', 'Visit', 'Sequence', 'Scan_Date',
'Study_ID', 'Series_ID', 'Image_ID', 'Original'
]
pet_tau_df = pd.DataFrame(columns=pet_tau_col)
pet_tau_dfs_list = []
# Loading needed .csv files
tauqc = pd.read_csv(path.join(csv_dir, 'TAUQC.csv'),
sep=',',
low_memory=False)
tauqc3 = pd.read_csv(path.join(csv_dir, 'TAUQC3.csv'),
sep=',',
low_memory=False)
pet_meta_list = pd.read_csv(path.join(csv_dir, 'PET_META_LIST.csv'),
sep=',',
low_memory=False)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list['Subject'] == subj]
if subject_pet_meta.empty:
continue
# QC for TAU PET images for ADNI 2
tau_qc2_subj = tauqc[(tauqc.SCANQLTY == 1)
& (tauqc.RID == int(subj[-4:]))]
# QC for TAU PET images for ADNI 3
tau_qc3_subj = tauqc3[(tauqc3.SCANQLTY == 1)
& (tauqc3.RID == int(subj[-4:]))]
# Concatenating visits in both QC files
tau_qc_subj = pd.concat([tau_qc2_subj, tau_qc3_subj],
axis=0,
ignore_index=True,
sort=False)
tau_qc_subj.rename(columns={"SCANDATE": "EXAMDATE"}, inplace=True)
sequences_preprocessing_step = ['AV1451 Co-registered, Averaged']
subj_dfs_list = get_images_pet(subj, tau_qc_subj, subject_pet_meta,
pet_tau_col, 'TAU-PET',
sequences_preprocessing_step)
if subj_dfs_list:
pet_tau_dfs_list += subj_dfs_list
if pet_tau_dfs_list:
pet_tau_df = pd.concat(pet_tau_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = [ # Multiple output images
('098_S_4275', 'm84')
]
# Removing known exceptions from images to convert
if not pet_tau_df.empty:
error_ind = pet_tau_df.index[pet_tau_df.apply(lambda x: (
(x.Subject_ID, x.VISCODE) in conversion_errors),
axis=1)]
pet_tau_df.drop(error_ind, inplace=True)
# Checking for images paths in filesystem
images = find_image_path(pet_tau_df, source_dir, 'TAU', 'I', 'Image_ID')
tau_csv_path = path.join(dest_dir, 'conversion_info')
if not os.path.exists(tau_csv_path):
os.mkdir(tau_csv_path)
images.to_csv(path.join(tau_csv_path, 'tau_pet_paths.tsv'),
sep='\t',
index=False)
return images
def compute_fdg_pet_paths(source_dir, csv_dir, dest_dir, subjs_list, conversion_dir):
"""Compute the paths to the FDG PET images and store them in a TSV file.
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
conversion_dir: path to the TSV files including the paths to original images
Returns:
images: a dataframe with all the paths to the PET images that will be converted into BIDS
"""
from os import path
import pandas as pd
from clinica.iotools.converters.adni_to_bids.adni_utils import (
find_image_path,
get_images_pet,
)
pet_fdg_col = [
"Phase",
"Subject_ID",
"VISCODE",
"Visit",
"Sequence",
"Scan_Date",
"Study_ID",
"Series_ID",
"Image_ID",
"Original",
]
pet_fdg_df = pd.DataFrame(columns=pet_fdg_col)
pet_fdg_dfs_list = []
# Loading needed .csv files
petqc = pd.read_csv(path.join(csv_dir, "PETQC.csv"), sep=",", low_memory=False)
petqc3 = pd.read_csv(path.join(csv_dir, "PETC3.csv"), sep=",", low_memory=False)
pet_meta_list = pd.read_csv(
path.join(csv_dir, "PET_META_LIST.csv"), sep=",", low_memory=False
)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list["Subject"] == subj]
if subject_pet_meta.empty:
continue
# QC for FDG PET images for ADNI 1, GO and 2
pet_qc_1go2_subj = petqc[(petqc.PASS == 1) & (petqc.RID == int(subj[-4:]))]
# QC for FDG PET images for ADNI 3
pet_qc3_subj = petqc3[(petqc3.SCANQLTY == 1) & (petqc3.RID == int(subj[-4:]))]
pet_qc3_subj.insert(0, "EXAMDATE", pet_qc3_subj.SCANDATE.to_list())
# Concatenating visits in both QC files
pet_qc_subj = pd.concat(
[pet_qc_1go2_subj, pet_qc3_subj], axis=0, ignore_index=True, sort=False
)
sequences_preprocessing_step = ["Co-registered, Averaged"]
subj_dfs_list = get_images_pet(
subj,
pet_qc_subj,
subject_pet_meta,
pet_fdg_col,
"FDG-PET",
sequences_preprocessing_step,
)
if subj_dfs_list:
pet_fdg_dfs_list += subj_dfs_list
if pet_fdg_dfs_list:
# Concatenating dataframes into one
pet_fdg_df = pd.concat(pet_fdg_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = [ # NONAME.nii
("031_S_0294", "bl"),
("037_S_1421", "m36"),
("037_S_1078", "m36"),
# Empty folders
("941_S_1195", "m48"),
("005_S_0223", "m12"),
]
# Removing known exceptions from images to convert
if not pet_fdg_df.empty:
error_ind = pet_fdg_df.index[
pet_fdg_df.apply(
lambda x: ((x.Subject_ID, x.VISCODE) in conversion_errors), axis=1
)
]
pet_fdg_df.drop(error_ind, inplace=True)
images = find_image_path(pet_fdg_df, source_dir, "FDG", "I", "Image_ID")
images.to_csv(path.join(conversion_dir, "fdg_pet_paths.tsv"), sep="\t", index=False)
return images
def compute_av45_fbb_pet_paths(source_dir, csv_dir, dest_dir, subjs_list):
"""
Compute the paths to the AV45 and Florbetaben PET images and store them in a tsv file
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
subjs_list: subjects list
Returns:
images: a dataframe with all the paths to the PET images that will be converted into BIDS
"""
import pandas as pd
import os
from os import path
from clinica.iotools.converters.adni_to_bids.adni_utils import get_images_pet, find_image_path
pet_amyloid_col = [
'Phase', 'Subject_ID', 'VISCODE', 'Visit', 'Sequence', 'Scan_Date',
'Study_ID', 'Series_ID', 'Image_ID', 'Original', 'Tracer'
]
pet_amyloid_df = pd.DataFrame(columns=pet_amyloid_col)
pet_amyloid_dfs_list = []
# Loading needed .csv files
av45qc = pd.read_csv(path.join(csv_dir, 'AV45QC.csv'),
sep=',',
low_memory=False)
amyqc = pd.read_csv(path.join(csv_dir, 'AMYQC.csv'),
sep=',',
low_memory=False)
pet_meta_list = pd.read_csv(path.join(csv_dir, 'PET_META_LIST.csv'),
sep=',',
low_memory=False)
for subj in subjs_list:
# PET images metadata for subject
subject_pet_meta = pet_meta_list[pet_meta_list['Subject'] == subj]
if subject_pet_meta.empty:
continue
# QC for AV45 PET images for ADNI 1, GO and 2
av45_qc_subj = av45qc[(av45qc.PASS == 1)
& (av45qc.RID == int(subj[-4:]))]
# QC for Amyloid PET images for ADNI 3
amy_qc_subj = amyqc[(amyqc.SCANQLTY == 1)
& (amyqc.RID == int(subj[-4:]))]
amy_qc_subj.insert(0, 'EXAMDATE', amy_qc_subj.SCANDATE.to_list())
# Concatenating visits in both QC files
amyloid_qc_subj = pd.concat([av45_qc_subj, amy_qc_subj],
axis=0,
ignore_index=True,
sort=False)
sequences_preprocessing_step = [
'AV45 Co-registered, Averaged', 'FBB Co-registered, Averaged'
]
subj_dfs_list = get_images_pet(subj, amyloid_qc_subj, subject_pet_meta,
pet_amyloid_col, 'Amyloid-PET',
sequences_preprocessing_step)
if subj_dfs_list:
pet_amyloid_dfs_list += subj_dfs_list
if pet_amyloid_dfs_list:
pet_amyloid_df = pd.concat(pet_amyloid_dfs_list, ignore_index=True)
# Exceptions
# ==========
conversion_errors = [ # Eq_1
('128_S_2220', 'm48'),
# Several output images
('098_S_4275', 'm84')
]
# Removing known exceptions from images to convert
if not pet_amyloid_df.empty:
error_ind = pet_amyloid_df.index[pet_amyloid_df.apply(lambda x: (
(x.Subject_ID, x.VISCODE) in conversion_errors),
axis=1)]
pet_amyloid_df.drop(error_ind, inplace=True)
images = find_image_path(pet_amyloid_df, source_dir, 'Amyloid', 'I',
'Image_ID')
amyloid_csv_path = path.join(dest_dir, 'conversion_info')
if not os.path.exists(amyloid_csv_path):
os.mkdir(amyloid_csv_path)
images.to_csv(path.join(amyloid_csv_path, 'amyloid_pet_paths.tsv'),
sep='\t',
index=False)
return images