def test_JistIntensityMp2rageMasking_outputs():
output_map = dict(outMasked=dict(),
outMasked2=dict(),
outSignal=dict(),
outSignal2=dict(),
)
outputs = JistIntensityMp2rageMasking.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def create_mp2rage_pipeline(name='mp2rage'):
# workflow
mp2rage = Workflow('mp2rage')
# inputnode
inputnode = Node(util.IdentityInterface(fields=['inv2', 'uni', 't1map']),
name='inputnode')
# outputnode
outputnode = Node(
util.IdentityInterface(fields=[
'uni_masked',
'background_mask',
'uni_stripped',
#'skullstrip_mask',
#'uni_reoriented'
]),
name='outputnode')
# remove background noise
background = Node(JistIntensityMp2rageMasking(outMasked=True,
outMasked2=True,
outSignal2=True),
name='background')
# skullstrip
strip = Node(MedicAlgorithmSPECTRE2010(outStripped=True,
outMask=True,
outOriginal=True,
inOutput='true',
inFind='true',
inMMC=4),
name='strip')
# connections
mp2rage.connect([
(inputnode, background, [('inv2', 'inSecond'),
('t1map', 'inQuantitative'),
('uni', 'inT1weighted')]),
(background, strip, [('outMasked2', 'inInput')]),
(background, outputnode, [('outMasked2', 'uni_masked'),
('outSignal2', 'background_mask')]),
(
strip,
outputnode,
[
('outStripped', 'uni_stripped'),
#('outMask', 'skullstrip_mask'),
#('outOriginal','uni_reoriented')
])
])
return mp2rage
def test_JistIntensityMp2rageMasking_outputs():
output_map = dict(
outMasked=dict(),
outMasked2=dict(),
outSignal=dict(),
outSignal2=dict(),
)
outputs = JistIntensityMp2rageMasking.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_JistIntensityMp2rageMasking_inputs():
input_map = dict(args=dict(argstr='%s',
),
environ=dict(nohash=True,
usedefault=True,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
inBackground=dict(argstr='--inBackground %s',
),
inMasking=dict(argstr='--inMasking %s',
),
inQuantitative=dict(argstr='--inQuantitative %s',
),
inSecond=dict(argstr='--inSecond %s',
),
inSkip=dict(argstr='--inSkip %s',
),
inT1weighted=dict(argstr='--inT1weighted %s',
),
null=dict(argstr='--null %s',
),
outMasked=dict(argstr='--outMasked %s',
hash_files=False,
),
outMasked2=dict(argstr='--outMasked2 %s',
hash_files=False,
),
outSignal=dict(argstr='--outSignal %s',
hash_files=False,
),
outSignal2=dict(argstr='--outSignal2 %s',
hash_files=False,
),
terminal_output=dict(mandatory=True,
nohash=True,
),
xDefaultMem=dict(argstr='-xDefaultMem %d',
),
xMaxProcess=dict(argstr='-xMaxProcess %d',
usedefault=True,
),
xPrefExt=dict(argstr='--xPrefExt %s',
),
)
inputs = JistIntensityMp2rageMasking.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_JistIntensityMp2rageMasking_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
inBackground=dict(argstr='--inBackground %s', ),
inMasking=dict(argstr='--inMasking %s', ),
inQuantitative=dict(argstr='--inQuantitative %s', ),
inSecond=dict(argstr='--inSecond %s', ),
inSkip=dict(argstr='--inSkip %s', ),
inT1weighted=dict(argstr='--inT1weighted %s', ),
null=dict(argstr='--null %s', ),
outMasked=dict(
argstr='--outMasked %s',
hash_files=False,
),
outMasked2=dict(
argstr='--outMasked2 %s',
hash_files=False,
),
outSignal=dict(
argstr='--outSignal %s',
hash_files=False,
),
outSignal2=dict(
argstr='--outSignal2 %s',
hash_files=False,
),
terminal_output=dict(
mandatory=True,
nohash=True,
),
xDefaultMem=dict(argstr='-xDefaultMem %d', ),
xMaxProcess=dict(
argstr='-xMaxProcess %d',
usedefault=True,
),
xPrefExt=dict(argstr='--xPrefExt %s', ),
)
inputs = JistIntensityMp2rageMasking.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def convert_scanner_data(population, afs_dir, data_dumpdir):
count = 0
for subject in population:
count += 1
print '========================================================================================'
print '%s- Dicom Conversion and Anatomical Preprocessing for subject %s_%s' % (
count, subject, afs_dir[-2])
print '========================================================================================'
for folder in os.listdir(afs_dir):
if folder.startswith('p'):
'===================================================================================================='
' DICOM to NIFTI '
'===================================================================================================='
#set dicom dir
if os.path.isdir(
os.path.join(afs_dir, folder, subject, 'DICOM')):
dicom_dir = os.path.join(afs_dir, folder, subject, 'DICOM')
if os.path.isdir(os.path.join(data_dumpdir, subject)):
pass
else:
os.makedirs(os.path.join(data_dumpdir, subject))
subject_dir = os.path.join(data_dumpdir, subject)
# create output out dir
try:
os.makedirs(os.path.join(subject_dir, 'NIFTI'))
except OSError:
nifti_dir = str(os.path.join(subject_dir, 'NIFTI'))
nifti_dir = str(os.path.join(subject_dir, 'NIFTI'))
# convert dicoms to niftis
# ensure conversion hasnt been completed before
if os.path.isfile(
os.path.join(nifti_dir, 'MP2RAGE_UNI.nii')):
print 'Dicom Conversion already completed...... moving on'
else:
print 'Converting DICOM to NIFTI'
convert_cmd = [
'isisconv', '-in',
'%s' % dicom_dir, '-out',
'%s/%s_S{sequenceNumber}_{sequenceDescription}_{echoTime}.nii'
% (nifti_dir, subject), '-rf', 'dcm', '-wdialect',
'fsl'
]
print subprocess.list2cmdline(convert_cmd)
subprocess.call(convert_cmd)
#rename outputs
for file in os.listdir(nifti_dir):
if 'mp2rage_p3_602B_INV1_2.98' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'MP2RAGE_INV1.nii')))
elif 'mp2rage_p3_602B_INV2_2.98' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'MP2RAGE_INV2.nii')))
elif 'mp2rage_p3_602B_DIV_Images_2.98' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'MP2RAGE_DIV.nii')))
elif 'mp2rage_p3_602B_T1_Images_2.98' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'MP2RAGE_T1MAPS.nii')))
elif 'mp2rage_p3_602B_UNI_Images_2.98' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'MP2RAGE_UNI.nii')))
elif 'resting' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(os.path.join(nifti_dir, 'REST.nii')))
elif 'mbep2d_se_52' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(os.path.join(nifti_dir,
'REST_SE.nii')))
elif 'se_invpol_52' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'REST_SE_INVPOL.nii')))
elif 'bvec' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(
os.path.join(nifti_dir,
'DWI_BVEC.bvec')))
elif 'AP_unwarp_diff' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(os.path.join(nifti_dir, 'DWI_AP.nii')))
elif 'PA_unwarp_diff' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(os.path.join(nifti_dir, 'DWI_PA.nii')))
elif 'mbep2d_diff_80' in file:
os.rename(
str(os.path.join(nifti_dir, file)),
str(os.path.join(nifti_dir, 'DWI.nii')))
# remove irrelevent files to conserve space
irrelvent_lst = [
'AAH', 'AX', 'ax', 'Cor', 'cor', 'COR', 'hip',
'Hip', 'slab', 'Modus', 'acpc', 'DUMMY',
'dummy', 'short', 'SLAB'
]
try:
for string in irrelvent_lst:
if string in file:
os.remove(
str(os.path.join(nifti_dir, file)))
except OSError:
print 'cant delete file %s' % str(
os.path.join(nifti_dir, file))
'===================================================================================================='
' Denoising MPRAGE Anatomical '
'===================================================================================================='
if os.path.isfile(
os.path.join(nifti_dir, 'MP2RAGE_BRAIN.nii')):
print 'MP2RAGE already deskulled............... moving on'
else:
print 'Deskulling MP2RAGE'
try:
mp2rage_uni = locate('MP2RAGE_UNI.nii', nifti_dir)
mp2rage_inv2 = locate('MP2RAGE_INV2.nii',
nifti_dir)
mp2rage_t1maps = locate('MP2RAGE_T1MAPS.nii',
nifti_dir)
except OSError:
continue
try:
os.makedirs(
os.path.join(nifti_dir, 'MIPAV_OUTPUTS'))
except OSError:
mipav_dir = str(
os.path.join(nifti_dir, 'MIPAV_OUTPUTS'))
mipav_dir = str(
os.path.join(nifti_dir, 'MIPAV_OUTPUTS'))
os.chdir(mipav_dir)
t1_threshold = fsl.Threshold()
t1_threshold.inputs.in_file = mp2rage_t1maps
t1_threshold.inputs.thresh = 1
t1_threshold.inputs.args = '-uthr 4000'
t1_threshold.run()
mp2rage_t1maps_thr = locate(
'MP2RAGE_T1MAPS_thresh.nii.gz', mipav_dir)
anat_denoise = JistIntensityMp2rageMasking(
outMasked=True,
outMasked2=True,
outSignal2=True,
inSkip='true')
anat_denoise.inputs.inQuantitative = mp2rage_t1maps_thr
anat_denoise.inputs.inT1weighted = mp2rage_uni
anat_denoise.inputs.inSecond = mp2rage_inv2
anat_denoise.run()
uni_denoised = locate('outMasked2.nii', mipav_dir)
'===================================================================================================='
' Deskulling '
'===================================================================================================='
anat_deskull = MedicAlgorithmSPECTRE2010(
inAtlas=str(
'/afs/cbs.mpg.de/software/cbstools/3.0/jist-cruise/Atlas/spectre/oasis-3-v2.txt'
),
#inInitial = 5,
#inInitial2 = 0.35 ,
#inMinimum = 0.1,
#inSmoothing = 0.02,
#inBackground = 0.001,
outOriginal=True,
#inOutput = 'true',
outStripped=True,
outMask=True,
inFind='true',
xMaxProcess=0,
inMMC=2,
inMMC2=2)
anat_deskull.inputs.inInput = uni_denoised
anat_deskull.run()
shutil.move(
str(os.path.join(mipav_dir, 'outStripped.nii')),
str(os.path.join(nifti_dir, 'MP2RAGE_BRAIN.nii')))
if os.path.isfile(
os.path.join(nifti_dir, 'MP2RAGE_BRAIN.nii')):
brain = os.path.join(nifti_dir, 'MP2RAGE_BRAIN.nii')
print 'Path = %s' % brain
print '========================================================================================'
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
========================================
sMRI: USing CBS Tools for skullstripping
========================================
This simple workflow uses SPECTRE2010 algorithm to skullstrip an MP2RAGE anatomical scan.
"""
import nipype.pipeline.engine as pe
from nipype.interfaces.mipav.developer import JistIntensityMp2rageMasking, MedicAlgorithmSPECTRE2010
wf = pe.Workflow("skullstripping")
mask = pe.Node(JistIntensityMp2rageMasking(), name="masking")
mask.inputs.inSecond = "/Users/filo/7t_trt/niftis/sub001/session_1/MP2RAGE_INV2.nii.gz"
mask.inputs.inQuantitative = "/Users/filo/7t_trt/niftis/sub001/session_1/MP2RAGE_UNI.nii.gz"
mask.inputs.inT1weighted = "/Users/filo/7t_trt/niftis/sub001/session_1/MP2RAGE_T1.nii.gz"
mask.inputs.outMasked = True
mask.inputs.outMasked2 = True
mask.inputs.outSignal = True
mask.inputs.outSignal2 = True
skullstrip = pe.Node(MedicAlgorithmSPECTRE2010(), name="skullstrip")
skullstrip.inputs.outStripped = True
skullstrip.inputs.xDefaultMem = 6000
wf.connect(mask, 'outMasked', skullstrip, 'inInput')
wf.run()
def create_mp2rage_pipeline(name='mp2rage'):
# workflow
mp2rage = Workflow('mp2rage')
# inputnode
inputnode=Node(util.IdentityInterface(fields=['inv2',
'uni',
't1map']),
name='inputnode')
# outputnode
outputnode=Node(util.IdentityInterface(fields=['uni_masked',
'background_mask',
'uni_stripped',
'skullstrip_mask',
]),
name='outputnode')
#get filenames from list
def return_list_element(x):
x_file=x[0]
return x_file
get_uni= Node(util.Function(input_names=["x"],
output_names=["x_file"],
function = return_list_element), name="get_uni")
get_t1= Node(util.Function(input_names=["x"],
output_names=["x_file"],
function = return_list_element), name="get_t1")
get_inv2= Node(util.Function(input_names=["x"],
output_names=["x_file"],
function = return_list_element), name="get_inv2")
# remove background noise
background = Node(JistIntensityMp2rageMasking(outMasked=True,
outMasked2=True,
outSignal2=True),
name='background')
# skullstrip
# strip = Node(MedicAlgorithmSPECTRE2010(outStripped=True,
# outMask=True,
# outOriginal=True,
# inOutput='true',
# inFind='true',
# inMMC=4
# ),
# name='strip')
strip = Node(JistBrainMp2rageSkullStripping(outBrain=True, outMasked2=True),name='strip')
# connections
mp2rage.connect([(inputnode, get_uni, [('uni','x')]),
(inputnode, get_t1, [('t1map','x')]),
(inputnode, get_inv2, [('inv2','x')]),
(get_inv2, background, [('x_file', 'inSecond')]),
(get_t1, background, [('x_file', 'inQuantitative')]),
(get_uni, background, [('x_file','inT1weighted')]),
(background, strip, [('outMasked2','inT1weighted')]),
(get_inv2, strip, [('x_file', 'inSecond')]),
(background, outputnode, [('outMasked2','uni_masked'),
('outSignal2','background_mask')]),
(strip, outputnode, [('outMasked2','uni_stripped'),
('outBrain', 'skullstrip_mask')
])
])
return mp2rage
def create_structural(subject, working_dir, data_dir, freesurfer_dir, out_dir):
'''
Workflow to run brackground masking and then freesurfer recon-all
on "lowres" MP2RAGE data
'''
# main workflow
struct_preproc = Workflow(name='mp2rage_preproc')
struct_preproc.base_dir = working_dir
struct_preproc.config['execution'][
'crashdump_dir'] = struct_preproc.base_dir + "/crash_files"
# select files
templates = {
'inv2': 'raw/mp2rage/inv2.nii.gz',
't1map': 'raw/mp2rage/t1map.nii.gz',
'uni': 'raw/mp2rage/uni.nii.gz'
}
selectfiles = Node(nio.SelectFiles(templates, base_directory=data_dir),
name="selectfiles")
# mp2rage background masking
background = Node(JistIntensityMp2rageMasking(outMasked=True,
outMasked2=True,
outSignal2=True),
name='background')
# workflow to run freesurfer reconall
# function to replace / in subject id string with a _
def sub_id(sub_id):
return sub_id.replace('/', '_')
recon_all = Node(fs.ReconAll(args='-nuiterations 7 -no-isrunning'),
name="recon_all")
recon_all.plugin_args = {'submit_specs': 'request_memory = 9000'}
recon_all.inputs.subjects_dir = freesurfer_dir
recon_all.inputs.subject_id = sub_id(subject)
#sink to store files
sink = Node(nio.DataSink(base_directory=out_dir,
parameterization=False,
substitutions=[('outStripped', 'uni_stripped'),
('outMasked2', 'uni_masked'),
('outSignal2', 'background_mask'),
('outOriginal', 'uni_reoriented'),
('outMask', 'skullstrip_mask'),
('transform_Warped',
'T1_brain2std')]),
name='sink')
# connections
struct_preproc.connect([
(selectfiles, background, [('inv2', 'inSecond'),
('t1map', 'inQuantitative'),
('uni', 'inT1weighted')]),
(background, recon_all, [('outMasked2', 'T1files')]),
(background, sink, [('outMasked2', 'preprocessed.mp2rage.@uni_masked'),
('outSignal2',
'preprocessed.mp2rage.@background_mask')]),
])
#struct_preproc.write_graph(dotfilename='struct_preproc.dot', graph2use='colored', format='pdf', simple_form=True)
return struct_preproc
