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}")