def convert_adni_tau_pet(
source_dir, csv_dir, dest_dir, conversion_dir, subjs_list=None, mod_to_update=False
):
"""Convert Tau PET images of ADNI into BIDS format.
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
conversion_dir: path to the TSV files including the paths to original images
subjs_list: subjects list
mod_to_update: If True, pre-existing images in the BIDS directory will be erased and extracted again.
"""
from os import path
import pandas as pd
from clinica.iotools.converters.adni_to_bids.adni_utils import paths_to_bids
from clinica.utils.stream import cprint
if not subjs_list:
adni_merge_path = path.join(csv_dir, "ADNIMERGE.csv")
adni_merge = pd.read_csv(adni_merge_path, sep=",", low_memory=False)
subjs_list = list(adni_merge.PTID.unique())
cprint(
f"Calculating paths of TAU PET images. Output will be stored in {conversion_dir}."
)
images = compute_tau_pet_paths(
source_dir, csv_dir, dest_dir, subjs_list, conversion_dir
)
cprint("Paths of TAU PET images found. Exporting images into BIDS ...")
paths_to_bids(images, dest_dir, "tau", mod_to_update=mod_to_update)
cprint(msg="TAU PET conversion done.", lvl="debug")
def convert_adni_pib_pet(source_dir, csv_dir, dest_dir, subjs_list=None):
"""Convert PIB PET images of ADNI into BIDS format
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
"""
import pandas as pd
from os import path
from clinica.utils.stream import cprint
from clinica.iotools.converters.adni_to_bids.adni_utils import paths_to_bids
from colorama import Fore
if subjs_list is None:
adni_merge_path = path.join(csv_dir, 'ADNIMERGE.csv')
adni_merge = pd.read_csv(adni_merge_path, sep=',', low_memory=False)
subjs_list = list(adni_merge.PTID.unique())
cprint(
'Calculating paths of PIB PET images. Output will be stored in %s.' %
path.join(dest_dir, 'conversion_info'))
images = compute_pib_pet_paths(source_dir, csv_dir, dest_dir, subjs_list)
cprint('Paths of PIB PET images found. Exporting images into BIDS ...')
paths_to_bids(images, dest_dir, 'pib')
cprint(Fore.GREEN + 'PIB PET conversion done.' + Fore.RESET)
def convert_adni_t1(source_dir,
csv_dir,
dest_dir,
conversion_dir,
subjs_list=None,
mod_to_update=False):
"""Convert T1 MR images of ADNI into BIDS format.
Args:
source_dir: path to the ADNI directory
csv_dir: path to the clinical data directory
dest_dir: path to the destination BIDS directory
conversion_dir: path to the TSV files including the paths to original images
subjs_list: subjects list
mod_to_update: If True, pre-existing images in the BIDS directory will be erased and extracted again.
"""
from os import path
from colorama import Fore
from pandas.io import parsers
from clinica.iotools.converters.adni_to_bids.adni_utils import paths_to_bids
from clinica.utils.stream import cprint
if subjs_list is None:
adni_merge_path = path.join(csv_dir, "ADNIMERGE.csv")
adni_merge = parsers.read_csv(adni_merge_path,
sep=",",
low_memory=False)
subjs_list = list(adni_merge.PTID.unique())
cprint(
f"Calculating paths of T1 images. Output will be stored in {conversion_dir}."
)
images = compute_t1_paths(source_dir, csv_dir, dest_dir, subjs_list,
conversion_dir)
cprint("Paths of T1 images found. Exporting images into BIDS ...")
paths_to_bids(images, dest_dir, "t1", mod_to_update=mod_to_update)
cprint(f"{Fore.GREEN}T1 conversion done.{Fore.RESET}")
