def init_input_node(t1w, recon_all_args, output_dir):
"""Initialize the pipeline.
This function will:
- Extract <image_id> (e.g. sub-CLNC01_ses-M00) T1w filename;
- Check FOV of T1w;
- Create SUBJECTS_DIR for recon-all (otherwise, the command won't run);
- Print begin execution message.
"""
import os
import errno
from clinica.utils.filemanip import get_subject_id
from clinica.utils.freesurfer import check_flags
from clinica.utils.ux import print_begin_image
# Extract <image_id>
image_id = get_subject_id(t1w)
# Check flags for T1w
flags = check_flags(t1w, recon_all_args)
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
subjects_dir = os.path.join(output_dir, image_id)
try:
os.makedirs(subjects_dir)
except OSError as e:
if e.errno != errno.EEXIST: # EEXIST: folder already exists
raise e
print_begin_image(image_id, ['ReconAllArgs'], [flags])
return image_id, t1w, flags, subjects_dir
def init_input_node(pet_nii):
import datetime
import nibabel as nib
from colorama import Fore
from clinica.utils.filemanip import get_subject_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(pet_nii)
# Check that the PET file is a 3D volume
img = nib.load(pet_nii)
if len(img.shape) == 4:
now = datetime.datetime.now().strftime("%H:%M:%S")
error_msg = (
f"{Fore.RED}[{now}] Error: Clinica does not handle 4D volumes "
f"for {image_id.replace('_', ' | ')}{Fore.RESET}")
cprint(error_msg)
raise NotImplementedError(error_msg)
# Print begin message
print_begin_image(image_id)
return pet_nii
def init_input_node(t1w, recon_all_args, output_dir):
"""Initialize the pipeline.
This function will:
- Extract <image_id> (e.g. sub-CLNC01_ses-M00) T1w filename;
- Check FOV of T1w;
- Create SUBJECTS_DIR for recon-all (otherwise, the command won't run);
- Print begin execution message.
"""
import os
from clinica.utils.filemanip import get_subject_id
from clinica.utils.freesurfer import check_flags
from clinica.utils.ux import print_begin_image
# Extract <image_id>
image_id = get_subject_id(t1w)
# Check flags for T1w
flags = check_flags(t1w, recon_all_args)
# Create SUBJECTS_DIR for recon-all (otherwise, the command won't run)
subjects_dir = os.path.join(output_dir, image_id)
os.makedirs(subjects_dir, exist_ok=True)
print_begin_image(image_id, ["ReconAllArgs"], [flags])
return image_id, t1w, flags, subjects_dir
def atlas_statistics(in_image, atlas_list):
"""
For each atlas name provided it calculates for the input image the mean for each region in the atlas and saves it to a TSV file.
Args:
in_image: A Nifti image
atlas_list: List of names of atlas to be applied
Returns:
List of paths to TSV files
"""
from os.path import abspath, join
from nipype.utils.filemanip import split_filename
from clinica.utils.atlas import AtlasAbstract
from clinica.utils.statistics import statistics_on_atlas
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_end_image
subject_id = get_subject_id(in_image)
orig_dir, base, ext = split_filename(in_image)
atlas_classes = AtlasAbstract.__subclasses__()
atlas_statistics_list = []
for atlas in atlas_list:
for atlas_class in atlas_classes:
if atlas_class.get_name_atlas() == atlas:
out_atlas_statistics = abspath(
join('./' + base + '_space-' + atlas +
'_map-graymatter_statistics.tsv'))
statistics_on_atlas(in_image, atlas_class(),
out_atlas_statistics)
atlas_statistics_list.append(out_atlas_statistics)
print_end_image(subject_id)
return atlas_statistics_list
def print_end_pipeline(t1w, final_file):
"""
Display end message for <subject_id> when <final_file> is connected.
"""
from clinica.utils.ux import print_end_image
from clinica.utils.filemanip import get_subject_id
print_end_image(get_subject_id(t1w))
def init_input_node(t1w, dwi, bvec, bval, dwi_json):
"""Initialize the pipeline."""
from clinica.utils.dwi import bids_dir_to_fsl_dir, check_dwi_volume
from clinica.utils.filemanip import extract_metadata_from_json, get_subject_id
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(t1w)
# Check that the number of DWI, bvec & bval are the same
check_dwi_volume(dwi, bvec, bval)
# Read metadata from DWI JSON file:
[total_readout_time, phase_encoding_direction] = extract_metadata_from_json(
dwi_json, ["TotalReadoutTime", "PhaseEncodingDirection"]
)
phase_encoding_direction = bids_dir_to_fsl_dir(phase_encoding_direction)
# Print begin message
print_begin_image(
image_id,
["TotalReadoutTime", "PhaseEncodingDirection"],
[str(total_readout_time), phase_encoding_direction],
)
return (
image_id,
t1w,
dwi,
bvec,
bval,
total_readout_time,
phase_encoding_direction,
)
def init_input_node(pet):
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(pet)
print_begin_image(image_id)
return pet
def init_input_node(t1w):
"""Extract "sub-<participant_id>_ses-<session_label>" from <t1w> and print begin message."""
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_begin_image
subject_id = get_subject_id(t1w)
print_begin_image(subject_id)
return subject_id, t1w
def init_input_node(pet_nii):
import nibabel as nib
from clinica.utils.filemanip import get_subject_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(pet_nii)
# Check that the PET file is a 3D volume
img = nib.load(pet_nii)
if len(img.shape) == 4:
error_msg = f"Clinica does not handle 4D volumes for {image_id.replace('_', ' | ')}"
cprint(error_msg, lvl="error")
raise NotImplementedError(error_msg)
# Print begin message
print_begin_image(image_id)
return pet_nii
def print_begin_pipeline(in_bids_or_caps_file: str) -> None:
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_begin_image
print_begin_image(get_subject_id(in_bids_or_caps_file))
def init_input_node(dwi, bvec, bval, dwi_json, fmap_magnitude, fmap_phasediff,
fmap_phasediff_json):
"""Initialize pipeline (read JSON, check files and print begin message)."""
import datetime
import nibabel as nib
from clinica.utils.dwi import bids_dir_to_fsl_dir, check_dwi_volume
from clinica.utils.filemanip import extract_metadata_from_json, get_subject_id
from clinica.utils.stream import cprint
from clinica.utils.ux import print_begin_image
# Extract image ID
image_id = get_subject_id(dwi)
# Check that the number of DWI, bvec & bval are the same
try:
check_dwi_volume(dwi, bvec, bval)
except ValueError as e:
now = datetime.datetime.now().strftime("%H:%M:%S")
error_msg = (
f"[{now}] Error: Number of DWIs, b-vals and b-vecs mismatch for {image_id.replace('_', ' | ')}"
)
cprint(error_msg, lvl="error")
raise ValueError(e)
# Check that PhaseDiff and magnitude1 have the same header
# Otherwise, FSL in FugueExtrapolationFromMask will crash
img_phasediff = nib.load(fmap_phasediff)
img_magnitude = nib.load(fmap_magnitude)
if img_phasediff.shape != img_magnitude.shape:
now = datetime.datetime.now().strftime("%H:%M:%S")
error_msg = (
f"[{now}] Error: Headers of PhaseDiff and Magnitude1 are not the same "
f"for {image_id.replace('_', ' | ')} ({img_phasediff.shape} vs {img_magnitude.shape})"
)
cprint(error_msg, lvl="error")
raise NotImplementedError(error_msg)
# Read metadata from DWI JSON file:
[total_readout_time,
phase_encoding_direction] = extract_metadata_from_json(
dwi_json, ["TotalReadoutTime", "PhaseEncodingDirection"])
phase_encoding_direction = bids_dir_to_fsl_dir(phase_encoding_direction)
# Read metadata from PhaseDiff JSON file:
[echo_time1,
echo_time2] = extract_metadata_from_json(fmap_phasediff_json,
["EchoTime1", "EchoTime2"])
delta_echo_time = abs(echo_time2 - echo_time1)
# Print begin message
print_begin_image(
image_id,
["TotalReadoutTime", "PhaseEncodingDirection", "DeltaEchoTime"],
[
str(total_readout_time), phase_encoding_direction,
str(delta_echo_time)
],
)
return (
image_id,
dwi,
bvec,
bval,
total_readout_time,
phase_encoding_direction,
fmap_magnitude,
fmap_phasediff,
delta_echo_time,
)
def print_end_pipeline(in_bids_or_caps_file, final_file_1, final_file_2):
from clinica.utils.filemanip import get_subject_id
from clinica.utils.ux import print_end_image
print_end_image(get_subject_id(in_bids_or_caps_file))
