def check_dicom(mse):
dcm = ""
dicom = glob("/working/henry_temp/PBR/dicoms/{}/E*/".format(mse))
if len(dicom) == 1:
dicom = dicom[0]
dicom2 = ""
elif len(dicom) == 2:
dicom = dicom[0]
dicom2 = dicom[1]
else:
dicom = ""
dicom2 = ""
dicoms = [dicom, dicom2]
for d in dicoms:
try:
cmd = ["dti_info", dicom]
proc = Popen(cmd, stdout=PIPE)
lines = [
description_renamer(" ".join(l.decode("utf-8").split()[1:-1]))
for l in proc.stdout.readlines()[8:]
]
if "C2_3_2Fl_seg_psir_TI_PSIR" in lines:
dcm = True
else:
continue
except:
pass
return dcm
def get_modality(mse):
num = mse.split("mse")[-1]
cmd = ["ms_dcm_exam_info", "-t", num]
proc = Popen(cmd, stdout=PIPE)
lines = [description_renamer(" ".join(l.decode("utf-8").split()[1:-1])) for l in proc.stdout.readlines()[8:]]
print("******These are the sequence names coming from the dicom images*****")
for items in lines:
print(items)
print("********************************************************************")
sequences = ["T1", "T2", "FLAIR", "T1_Gad"]
for sq in sequences:
print("checking for...", sq)
sequence_name = ""
nii_type = sq
if nii_type:
try:
sequence_name = filter_files(lines, nii_type, heuristic)[0]
print(sequence_name, "This is the {0}".format(sq))
except:
print(mse, "No {0} in dicom identified by the heuristic...Please check sequnces and heuristic".format(sq))
pass
if len(sequence_name) > 1:
print("checking for sequence names in nifti and align folders")
check_for_sq_names(mse, sq, "/nii/", sequence_name)
check_for_sq_names(mse, sq, "/alignment/", sequence_name)
get_nifti(mse)
get_align(mse)
def get_modality(mse, nii_type="T1"):
output = pd.DataFrame()
num = mse.split("mse")[-1]
cmd = ["ms_dcm_exam_info", "-t", num]
proc = Popen(cmd, stdout=PIPE)
lines = [description_renamer(" ".join(l.decode("utf-8").split()[1:-1])) for l in proc.stdout.readlines()[8:]]
files = filter_files(lines, "T1", heuristic)
output["nii"] = files
output["mse"] = mse
return output
def get_modality(mse, nii_type="T1"):
output = pd.DataFrame()
num = mse.split("mse")[-1]
cmd = ["ms_dcm_exam_info", "-t", num]
proc = Popen(cmd, stdout=PIPE)
lines = [description_renamer(" ".join(l.decode("utf-8").split()[1:-1])) for l in proc.stdout.readlines()[8:]]
if nii_type:
files = filter_files(lines, nii_type, heuristic)
output["nii"] = files
else:
output["nii"] = lines
output["mse"] = mse
return output
def get_modality(mse, sequence):
num = mse.split("mse")[-1]
cmd = ["ms_dcm_exam_info", "-t", num]
proc = Popen(cmd, stdout=PIPE)
lines = [description_renamer(" ".join(l.decode("utf-8").split()[1:-1])) for l in proc.stdout.readlines()[8:]]
sequence_name = ""
nii_type = sequence
if nii_type:
try:
sequence_name = filter_files(lines, nii_type, heuristic)[0]
print(sequence_name, "This is the {0}".format(sequence))
except:
pass
return sequence_name
def check_pacman(mse):
pacman = ""
num = mse.split("mse")[-1]
#print("ms_dcm_exam_info", "-t", num, "-D")
cmd = ["ms_dcm_exam_info", "-t", num, "-D"]
proc = Popen(cmd, stdout=PIPE)
lines = [
description_renamer(" ".join(l.decode("utf-8").split()[1:-1]))
for l in proc.stdout.readlines()[8:]
]
if "C2_3_2Fl_seg_psir_TI_PSIR" in lines:
pacman = True
elif "C2-3_2Fl_seg_psir_TI_PSIR" in lines:
pacman = True
else:
pacman = False
return pacman
def get_modality(mse):
num = mse.split("mse")[-1]
cmd = ["ms_dcm_exam_info", "-t", num, "-D"]
proc = Popen(cmd, stdout=PIPE)
lines = [
description_renamer(" ".join(l.decode("utf-8").split()[1:-1]))
for l in proc.stdout.readlines()[8:]
]
sequences = ["T2_spine"]
for sq in sequences:
print("checking for...", sq)
nii_type = sq
if nii_type:
series_name = filter_files(lines, nii_type, heuristic)
if len(series_name) > 0:
series_name = series_name[0]
print(series_name, " is the {0}".format(sq))
else:
print(
mse,
"No {0} in dicom identified by the heuristic...Please check sequnces and heuristic"
.format(sq))
def get_modalityt1(mse, nii_type="FLAIR"):
output = pd.DataFrame(index=None)
num = mse.split("mse")[-1]
cmd = ["ms_dcm_exam_info", "-t", num]
proc = Popen(cmd, stdout=PIPE)
#lines = [l.decode("utf-8").split() for l in proc.stdout.readlines()[8:]]
lines = [
description_renamer(" ".join(l.decode("utf-8").split()[1:-1]))
for l in proc.stdout.readlines()[8:]
]
#print(lines, "lines")
if nii_type:
#print(nii_type)
files = filter_files(lines, nii_type, heuristic)
#print(mse, "t1 exists")
output["nii"] = files
else:
output["nii"] = lines
print(mse, "NO T1")
output["mse"] = mse
return output
def get_scanner_info(c, out):
df = pd.read_csv("{}".format(c))
for idx in range(len(df)):
msid = df.loc[idx, 'msid']
mse = df.loc[idx, 'mse']
print(msid, mse)
if os.path.exists(get_align(mse)):
with open(get_align(mse)) as data_file:
data = json.load(data_file)
if len(data["t1_files"]) > 0:
t1_file = data["t1_files"][-1].split('/')[-1]
df.loc[idx, "T1"] = t1_file
if len(data["t2_files"]) > 0:
t2_file = data["t2_files"][-1].split('/')[-1]
df.loc[idx, "T2"] = t2_file
if len(data["flair_files"]) > 0:
flair_file = data["flair_files"][-1].split('/')[-1]
df.loc[idx, "FLAIR"] = flair_file
num = mse.split("mse")[-1]
print("ms_dcm_exam_info", "-t", num, "-D")
cmd = ["ms_dcm_exam_info", "-t", num, "-D"]
proc = Popen(cmd, stdout=PIPE)
lines = [
description_renamer(" ".join(l.decode("utf-8").split()[1:-1]))
for l in proc.stdout.readlines()[8:]
]
sequences = ["T1", "T2", "FLAIR", "T1_Gad"]
for sq in sequences:
nii_type = sq
if nii_type:
try:
sequence_name = filter_files(lines, nii_type,
heuristic)[-1]
df.loc[idx, format(sq)] = sequence_name
print(sequence_name, " is the {0}".format(sq))
except:
pass
try:
dicom = glob(
"/working/henry_temp/PBR/dicoms/{0}/E*/*/*.DCM".format(mse))[0]
cmd = ["dcmdump", dicom]
proc = Popen(cmd, stdout=PIPE)
output = [
l.decode("utf-8").split() for l in proc.stdout.readlines()[:]
]
x = [
"SoftwareVersions", "BodyPartExamined", "StationName",
"TransmitCoilName", "ReceiveCoilName"
]
for line in output:
for flag in x:
if flag in line:
dicom_info = str(
str(line[2:]).split(']')[0].split('[')
[2:]).replace('[', "").replace(']', '').replace(
'"', '').replace("'", "")
print(flag, ":", dicom_info)
if flag == "SoftwareVersions":
df.loc[idx, "SoftwareVersions"] = dicom_info
if flag == "BodyPartExamined":
df.loc[idx, "BodyPartExamined"] = dicom_info
if flag == "StationName":
df.loc[idx, "StationName"] = dicom_info
if flag == "TransmitCoilName":
df.loc[idx, "TransmitCoilName"] = dicom_info
if flag == "ReceiveCoilName":
df.loc[idx, "ReceiveCoilName"] = dicom_info
except:
df.loc[idx, "dicom"] = "no dicom in working directory"
pass
df.to_csv("{0}".format(out))