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bids_conversion.py
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bids_conversion.py
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import os
import shutil
import glob
import json
import re
# This script should be run from the directory containing the input sites directories
project_root = './'
sites = ["MGH", "MNI", "MSSM", "NTNU", "UCL", "CRMBM", "MPI"]
subject_input_names = ["SubD", "SubL", "SubR", "Spinoza6"]
human_dataset_id = "ds005025"
phantom_dataset_id = "ds005090"
def copy_scan(input_path, output_path, scan_additional=""):
nii_path = input_path
json_path = input_path.replace(".nii.gz", ".json")
output_scan_path = output_path + ".nii.gz"
if scan_additional != "":
output_scan_path_tokens = output_scan_path.split("_")
output_scan_path_tokens.insert(len(output_scan_path_tokens) - 1, json_additional)
output_scan_path = "_".join(output_scan_path_tokens)
shutil.copy2(nii_path, output_scan_path)
shutil.copy2(json_path, output_path + ".json")
def json_attribute(attribute_name, json_file_path):
with open(json_file_path) as f:
json_data = json.load(f)
if not attribute_name in json_data: return None
return json_data[attribute_name]
# Output directories are all generated in the outputs folder
output_path_root = os.path.join(project_root, "outputs")
if os.path.exists(output_path_root): shutil.rmtree(output_path_root)
os.makedirs(output_path_root)
participants_tsv_text_human = "participant_id\tspecies\tage\tsex\tpathology\tinstitution\tfield\n"
participants_tsv_text_phantom = participants_tsv_text_human
for site in sites:
# Input directories are named SITE-original, for example "MGH-original"
input_site_path = os.path.join(project_root, site + "-original")
for subject in subject_input_names:
input_path = os.path.join(input_site_path, subject)
assert(os.path.exists(input_path))
subject_index = subject_input_names.index(subject)
dataset = ""
if subject == "Spinoza6":
dataset = phantom_dataset_id
subject = "sub-" + site
participants_tsv_text_phantom += "\t".join([subject, "phantom", "n/a", "n/a", "HC", site, "7T"]) + "\n"
else:
dataset = human_dataset_id
subject = "sub-" + site + str(subject_index + 1)
participants_tsv_text_human += "\t".join([subject, "homo sapiens", "n/a", "n/a", "HC", site, "7T"]) + "\n"
output_path = os.path.join(output_path_root, dataset, subject)
output_anat_path = os.path.join(output_path, "anat")
output_fmap_path = os.path.join(output_path, "fmap")
os.makedirs(output_anat_path)
os.makedirs(output_fmap_path)
for dir_path in [dir[0] for dir in os.walk(input_path)]:
dir_basename = os.path.basename(dir_path)
if dir_basename == "COILQA_SAG_LARGE":
sag_large_files = sorted(glob.glob(os.path.join(dir_path, "*.nii.gz")))
snr_input_path = sag_large_files[1 if len(sag_large_files) > 1 else 0]
copy_scan(snr_input_path, os.path.join(output_fmap_path, subject + "_acq-coilQaSagLarge_SNR"))
elif dir_basename == "COILQA_SAG_SMALL":
gfactor_input_path = sorted(glob.glob(os.path.join(dir_path, "*nii.gz")))[0]
copy_scan(gfactor_input_path, os.path.join(output_fmap_path, subject + "_acq-coilQaSagSmall_GFactor"))
elif dir_basename == "COILQA_TRA":
gfactor_input_path = sorted(glob.glob(os.path.join(dir_path, "*nii.gz")))[0]
copy_scan(gfactor_input_path, os.path.join(output_fmap_path, subject + "_acq-coilQaTra_GFactor"))
elif dir_basename in (dream_directory_names := ["DREAM_MEDIUM", "DREAM", "DREAM_MEDIUM_066", "DREAM_MEDIUM_HWLIMIT"]):
dream_file_paths = sorted(glob.glob(os.path.join(dir_path, "*nii.gz")))
dream_allowed_scan_types = {
"Reference Voltage Map": "refv",
"Flipangle Map": "famp",
"REFVOLTMAP": "refv",
"B1MAP": "famp",
"Transmitter Reference Map (Volt)": "refv",
"flip angle map": "famp",
}
dream_acq_voltage_token = {
"DREAM_MEDIUM_066": "0.66",
"DREAM_MEDIUM_HWLIMIT": "1.5"
}
for dream_file_path in dream_file_paths:
dream_json_path = dream_file_path.replace(".nii.gz", ".json")
if json_attribute("NonlinearGradientCorrection", dream_json_path): continue
scan_type = json_attribute("ImageComments", dream_json_path)
if scan_type is None:
scan_type = json_attribute("ImageType", dream_json_path)[-1]
else:
scan_type = scan_type.split(";")[0]
if scan_type not in dream_allowed_scan_types and scan_type: continue
scan_type = dream_allowed_scan_types[scan_type]
voltage_token = ""
if dir_basename in dream_acq_voltage_token:
voltage_token = "-" + dream_acq_voltage_token[dir_basename]
destination_path = os.path.join(output_fmap_path, subject + "_acq-" + scan_type + voltage_token + "_TB1DREAM")
def get_fov(json_path):
desc_tokens = json_attribute("ProtocolName", json_path).split("_")
for token in desc_tokens:
if token.startswith("FOV"):
return int(token[3:])
return None
if os.path.exists(destination_path + ".nii.gz"):
previous_fov = get_fov(destination_path + ".json")
new_fov = get_fov(dream_json_path)
if previous_fov == None:
print("Could not find FOV token", dream_json_path)
continue
if new_fov > previous_fov: continue
copy_scan(dream_file_path, destination_path)
elif dir_basename == "GRE":
gre_file_paths = sorted(glob.glob(os.path.join(dir_path, "*nii.gz")))
for gre_file_path in gre_file_paths:
gre_filename = os.path.basename(gre_file_path)
gre_filename_tokens = gre_filename.split("_")
channel_name = gre_filename_tokens[-1].split(".")[0]
if not "ph" in gre_filename:
if ("RX" in channel_name or channel_name.startswith("i") or channel_name.startswith("c")):
channel_number = re.findall(r"\d+", channel_name)[0]
copy_scan(gre_file_path, os.path.join(output_anat_path, subject + "_rec-uncombined" + channel_number + "_T2starw"))
elif json_attribute("NonlinearGradientCorrection", gre_file_path.replace(".nii.gz", ".json")) == True:
copy_scan(gre_file_path, os.path.join(output_anat_path, subject + "_T2starw"))
elif dir_basename == "MP2RAGE":
mp2rage_file_paths = sorted(glob.glob(os.path.join(dir_path, "*nii.gz")))
mp2rage_types = ["INV1", "INV2", "UNI"]
mp2rage_type_names = ["inv-1", "inv-2", "UNIT1"]
for i in range(len(mp2rage_file_paths)):
series_desc = json_attribute("SeriesDescription", mp2rage_file_paths[i].replace(".nii.gz", ".json"))
for mp2rage_type_index in range(len(mp2rage_types)):
mp2rage_type = mp2rage_types[mp2rage_type_index]
if mp2rage_type in series_desc:
json_additional = "part-mag"
suffix = "_MP2RAGE"
if mp2rage_type == "UNI":
json_additional = ""
suffix = ""
mp2rage_type = mp2rage_type_names[mp2rage_type_index]
copy_scan(mp2rage_file_paths[i], os.path.join(output_anat_path, subject + "_" + mp2rage_type + suffix), json_additional)
elif dir_basename == "TFL_B1_C3C4" or dir_basename == "TFL" or dir_basename == "TFL_B1_OPT":
tfl_file_paths = sorted(glob.glob(os.path.join(dir_path, "*nii.gz")))
anat_found = False
famp_found = False
for tfl_file_path in tfl_file_paths:
tfl_json_path = tfl_file_path.replace(".nii.gz", ".json")
if json_attribute("NonlinearGradientCorrection", tfl_json_path): continue
image_comments = json_attribute("ImageComments", tfl_json_path)
if image_comments is None: continue
if "anatomical image" in image_comments:
copy_scan(tfl_file_path, os.path.join(output_fmap_path, subject + "_acq-anat_TB1TFL"))
anat_found = True
elif "flip angle map" in image_comments:
copy_scan(tfl_file_path, os.path.join(output_fmap_path, subject + "_acq-famp_TB1TFL"))
famp_found = True
if not famp_found:
copy_scan(tfl_file_paths[[5, 0, 1][subject_index]], os.path.join(output_fmap_path, subject + "_acq-famp_TB1TFL"))
with open(os.path.join(output_path_root, human_dataset_id, "participants.tsv"), "w") as f:
f.write(participants_tsv_text_human)
with open(os.path.join(output_path_root, phantom_dataset_id, "participants.tsv"), "w") as f:
f.write(participants_tsv_text_phantom)
shutil.copy2(os.path.join(project_root, ".bidsignore"), os.path.join(output_path_root, human_dataset_id, ".bidsignore"))
shutil.copy2(os.path.join(project_root, ".bidsignore"), os.path.join(output_path_root, phantom_dataset_id, ".bidsignore"))
shutil.copy2(os.path.join(project_root, "dataset_description-human.json"), os.path.join(output_path_root, human_dataset_id, "dataset_description.json"))
shutil.copy2(os.path.join(project_root, "dataset_description-phantom.json"), os.path.join(output_path_root, phantom_dataset_id, "dataset_description.json"))