def test_MedicAlgorithmSPECTRE2010_inputs():
input_map = dict(
args=dict(argstr="%s"),
environ=dict(nohash=True, usedefault=True),
ignore_exception=dict(nohash=True, usedefault=True),
inApply=dict(argstr="--inApply %s"),
inAtlas=dict(argstr="--inAtlas %s"),
inBackground=dict(argstr="--inBackground %f"),
inCoarse=dict(argstr="--inCoarse %f"),
inCost=dict(argstr="--inCost %s"),
inDegrees=dict(argstr="--inDegrees %s"),
inFind=dict(argstr="--inFind %s"),
inFine=dict(argstr="--inFine %f"),
inImage=dict(argstr="--inImage %s"),
inInhomogeneity=dict(argstr="--inInhomogeneity %s"),
inInitial=dict(argstr="--inInitial %d"),
inInitial2=dict(argstr="--inInitial2 %f"),
inInput=dict(argstr="--inInput %s"),
inMMC=dict(argstr="--inMMC %d"),
inMMC2=dict(argstr="--inMMC2 %d"),
inMaximum=dict(argstr="--inMaximum %f"),
inMinimum=dict(argstr="--inMinimum %f"),
inMinimum2=dict(argstr="--inMinimum2 %f"),
inMultiple=dict(argstr="--inMultiple %d"),
inMultithreading=dict(argstr="--inMultithreading %s"),
inNumber=dict(argstr="--inNumber %d"),
inNumber2=dict(argstr="--inNumber2 %d"),
inOutput=dict(argstr="--inOutput %s"),
inOutput2=dict(argstr="--inOutput2 %s"),
inOutput3=dict(argstr="--inOutput3 %s"),
inOutput4=dict(argstr="--inOutput4 %s"),
inOutput5=dict(argstr="--inOutput5 %s"),
inRegistration=dict(argstr="--inRegistration %s"),
inResample=dict(argstr="--inResample %s"),
inRun=dict(argstr="--inRun %s"),
inSkip=dict(argstr="--inSkip %s"),
inSmoothing=dict(argstr="--inSmoothing %f"),
inSubsample=dict(argstr="--inSubsample %s"),
inUse=dict(argstr="--inUse %s"),
null=dict(argstr="--null %s"),
outFANTASM=dict(argstr="--outFANTASM %s", hash_files=False),
outMask=dict(argstr="--outMask %s", hash_files=False),
outMidsagittal=dict(argstr="--outMidsagittal %s", hash_files=False),
outOriginal=dict(argstr="--outOriginal %s", hash_files=False),
outPrior=dict(argstr="--outPrior %s", hash_files=False),
outSegmentation=dict(argstr="--outSegmentation %s", hash_files=False),
outSplitHalves=dict(argstr="--outSplitHalves %s", hash_files=False),
outStripped=dict(argstr="--outStripped %s", hash_files=False),
outd0=dict(argstr="--outd0 %s", hash_files=False),
terminal_output=dict(nohash=True),
xDefaultMem=dict(argstr="-xDefaultMem %d"),
xMaxProcess=dict(argstr="-xMaxProcess %d", usedefault=True),
xPrefExt=dict(argstr="--xPrefExt %s"),
)
inputs = MedicAlgorithmSPECTRE2010.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 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_MedicAlgorithmSPECTRE2010_outputs():
output_map = dict(outFANTASM=dict(),
outMask=dict(),
outMidsagittal=dict(),
outOriginal=dict(),
outPrior=dict(),
outSegmentation=dict(),
outSplitHalves=dict(),
outStripped=dict(),
outd0=dict(),
)
outputs = MedicAlgorithmSPECTRE2010.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_MedicAlgorithmSPECTRE2010_outputs():
output_map = dict(
outFANTASM=dict(),
outMask=dict(),
outMidsagittal=dict(),
outOriginal=dict(),
outPrior=dict(),
outSegmentation=dict(),
outSplitHalves=dict(),
outStripped=dict(),
outd0=dict(),
)
outputs = MedicAlgorithmSPECTRE2010.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_MedicAlgorithmSPECTRE2010_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
inApply=dict(argstr='--inApply %s', ),
inAtlas=dict(argstr='--inAtlas %s', ),
inBackground=dict(argstr='--inBackground %f', ),
inCoarse=dict(argstr='--inCoarse %f', ),
inCost=dict(argstr='--inCost %s', ),
inDegrees=dict(argstr='--inDegrees %s', ),
inFind=dict(argstr='--inFind %s', ),
inFine=dict(argstr='--inFine %f', ),
inImage=dict(argstr='--inImage %s', ),
inInhomogeneity=dict(argstr='--inInhomogeneity %s', ),
inInitial=dict(argstr='--inInitial %d', ),
inInitial2=dict(argstr='--inInitial2 %f', ),
inInput=dict(argstr='--inInput %s', ),
inMMC=dict(argstr='--inMMC %d', ),
inMMC2=dict(argstr='--inMMC2 %d', ),
inMaximum=dict(argstr='--inMaximum %f', ),
inMinimum=dict(argstr='--inMinimum %f', ),
inMinimum2=dict(argstr='--inMinimum2 %f', ),
inMultiple=dict(argstr='--inMultiple %d', ),
inMultithreading=dict(argstr='--inMultithreading %s', ),
inNumber=dict(argstr='--inNumber %d', ),
inNumber2=dict(argstr='--inNumber2 %d', ),
inOutput=dict(argstr='--inOutput %s', ),
inOutput2=dict(argstr='--inOutput2 %s', ),
inOutput3=dict(argstr='--inOutput3 %s', ),
inOutput4=dict(argstr='--inOutput4 %s', ),
inOutput5=dict(argstr='--inOutput5 %s', ),
inRegistration=dict(argstr='--inRegistration %s', ),
inResample=dict(argstr='--inResample %s', ),
inRun=dict(argstr='--inRun %s', ),
inSkip=dict(argstr='--inSkip %s', ),
inSmoothing=dict(argstr='--inSmoothing %f', ),
inSubsample=dict(argstr='--inSubsample %s', ),
inUse=dict(argstr='--inUse %s', ),
null=dict(argstr='--null %s', ),
outFANTASM=dict(
argstr='--outFANTASM %s',
hash_files=False,
),
outMask=dict(
argstr='--outMask %s',
hash_files=False,
),
outMidsagittal=dict(
argstr='--outMidsagittal %s',
hash_files=False,
),
outOriginal=dict(
argstr='--outOriginal %s',
hash_files=False,
),
outPrior=dict(
argstr='--outPrior %s',
hash_files=False,
),
outSegmentation=dict(
argstr='--outSegmentation %s',
hash_files=False,
),
outSplitHalves=dict(
argstr='--outSplitHalves %s',
hash_files=False,
),
outStripped=dict(
argstr='--outStripped %s',
hash_files=False,
),
outd0=dict(
argstr='--outd0 %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 = MedicAlgorithmSPECTRE2010.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()
