def create_dwi_pipeline(name="proc_dwi"):
inputnode = pe.Node(interface=util.IdentityInterface(fields=['dwi', "bvecs", "bvals"]), name="inputnode")
preprocess = create_dwi_preprocess_pipeline()
estimate_bedpost = create_bedpostx_pipeline()
dtifit = pe.Node(interface=fsl.DTIFit(),name='dtifit')
pipeline = pe.Workflow(name=name)
pipeline.connect([(inputnode, preprocess, [("dwi", "inputnode.dwi")]),
(preprocess, dtifit, [('eddycorrect.outputnode.eddy_corrected','dwi'),
("bet.mask_file", "mask")]),
(inputnode, dtifit, [("bvals","bvals"),
("bvecs", "bvecs")]),
(preprocess, estimate_bedpost, [('eddycorrect.outputnode.eddy_corrected','inputnode.dwi'),
("bet.mask_file", "inputnode.mask")]),
(inputnode, estimate_bedpost, [("bvals","inputnode.bvals"),
("bvecs", "inputnode.bvecs")]),
])
return pipeline
def test_create_bedpostx_pipeline():
fsl_course_dir = os.path.abspath('fsl_course_data')
mask_file = os.path.join(fsl_course_dir, "fdt/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fdt/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fdt/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fdt/subj1/data.nii.gz")
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.dwi = dwi_file
nipype_bedpostx.inputs.inputnode.mask = mask_file
nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file
nipype_bedpostx.inputs.inputnode.bvals = bvals_file
nipype_bedpostx.inputs.xfibres.n_fibres = 2
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 1000
nipype_bedpostx.inputs.xfibres.n_jumps = 1250
nipype_bedpostx.inputs.xfibres.sample_every = 25
with warnings.catch_warnings():
warnings.simplefilter("ignore")
original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(), name="original_bedpostx")
original_bedpostx.inputs.dwi = dwi_file
original_bedpostx.inputs.mask = mask_file
original_bedpostx.inputs.bvecs = bvecs_file
original_bedpostx.inputs.bvals = bvals_file
original_bedpostx.inputs.environ['FSLPARALLEL'] = ""
original_bedpostx.inputs.fibres = 2
original_bedpostx.inputs.weight = 1
original_bedpostx.inputs.burn_period = 1000
original_bedpostx.inputs.jumps = 1250
original_bedpostx.inputs.sampling = 25
test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test")
test_f2 = pe.Node(util.AssertEqual(), name="mean_f2_test")
test_th1 = pe.Node(util.AssertEqual(), name="mean_th1_test")
test_th2 = pe.Node(util.AssertEqual(), name="mean_th2_test")
test_ph1 = pe.Node(util.AssertEqual(), name="mean_ph1_test")
test_ph2 = pe.Node(util.AssertEqual(), name="mean_ph2_test")
pipeline = pe.Workflow(name="test_bedpostx")
pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_")
def pickFirst(l):
return l[0]
def pickSecond(l):
return l[1]
pipeline.connect([(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples", pickFirst), "volume1")]),
(nipype_bedpostx, test_f2, [(("outputnode.mean_fsamples", pickSecond), "volume1")]),
(nipype_bedpostx, test_th1, [(("outputnode.mean_thsamples", pickFirst), "volume1")]),
(nipype_bedpostx, test_th2, [(("outputnode.mean_thsamples", pickSecond), "volume1")]),
(nipype_bedpostx, test_ph1, [(("outputnode.mean_phsamples", pickFirst), "volume1")]),
(nipype_bedpostx, test_ph2, [(("outputnode.mean_phsamples", pickSecond), "volume1")]),
(original_bedpostx, test_f1, [(("mean_fsamples", pickFirst), "volume2")]),
(original_bedpostx, test_f2, [(("mean_fsamples", pickSecond), "volume2")]),
(original_bedpostx, test_th1, [(("mean_thsamples", pickFirst), "volume2")]),
(original_bedpostx, test_th2, [(("mean_thsamples", pickSecond), "volume2")]),
(original_bedpostx, test_ph1, [(("mean_phsamples", pickFirst), "volume2")]),
(original_bedpostx, test_ph2, [(("mean_phsamples", pickSecond), "volume2")])
])
pipeline.run(plugin='Linear')
shutil.rmtree(pipeline.base_dir)
def test_create_bedpostx_pipeline():
fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA'])
mask_file = os.path.join(fsl_course_dir, "fdt2/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fdt2/subj1/data.nii.gz")
z_min = 62
z_size = 2
slice_mask = pe.Node(fsl.ExtractROI(x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=z_min,
z_size=z_size), name="slice_mask")
slice_mask.inputs.in_file = mask_file
slice_dwi = pe.Node(fsl.ExtractROI(x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=z_min,
z_size=z_size), name="slice_dwi")
slice_dwi.inputs.in_file = dwi_file
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file
nipype_bedpostx.inputs.inputnode.bvals = bvals_file
nipype_bedpostx.inputs.xfibres.n_fibres = 1
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 0
nipype_bedpostx.inputs.xfibres.n_jumps = 1
nipype_bedpostx.inputs.xfibres.sample_every = 1
nipype_bedpostx.inputs.xfibres.cnlinear = True
nipype_bedpostx.inputs.xfibres.seed = 0
nipype_bedpostx.inputs.xfibres.model = 2
with warnings.catch_warnings():
warnings.simplefilter("ignore")
original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(), name="original_bedpostx")
original_bedpostx.inputs.bvecs = bvecs_file
original_bedpostx.inputs.bvals = bvals_file
original_bedpostx.inputs.environ['FSLPARALLEL'] = ""
original_bedpostx.inputs.n_fibres = 1
original_bedpostx.inputs.fudge = 1
original_bedpostx.inputs.burn_in = 0
original_bedpostx.inputs.n_jumps = 1
original_bedpostx.inputs.sample_every = 1
original_bedpostx.inputs.seed = 0
original_bedpostx.inputs.model = 2
test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test")
pipeline = pe.Workflow(name="test_bedpostx")
pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_")
pipeline.connect([(slice_mask, original_bedpostx, [("roi_file", "mask")]),
(slice_mask, nipype_bedpostx, [("roi_file", "inputnode.mask")]),
(slice_dwi, original_bedpostx, [("roi_file", "dwi")]),
(slice_dwi, nipype_bedpostx, [("roi_file", "inputnode.dwi")]),
(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples",list_to_filename), "volume1")]),
(original_bedpostx, test_f1, [("mean_fsamples", "volume2")]),
])
pipeline.run(plugin='Linear')
shutil.rmtree(pipeline.base_dir)
#PBS -l nodes=1:ppn=1
#PBS -l pmem=600mb
#PBS -l walltime=4:00:00
#PBS -e ''' + base_dir + '''my_job.err
#PBS -o ''' + base_dir + '''my_job.log
module load python/2.7.6
module load nipype/0.8
module load fsl/5.0.5
'''
with open(os.path.join(base_dir, JOB_TEMPLATE_NAME), "w") as temp_file:
temp_file.write(template)
# ---------------Creating nipype workflow for bedpostx-------------------
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.dwi = os.path.join(input_dir, 'data.nii')
nipype_bedpostx.inputs.inputnode.mask = os.path.join(
input_dir, 'nodif_brain_mask.nii')
nipype_bedpostx.inputs.inputnode.bvecs = os.path.join(input_dir, 'bvecs')
nipype_bedpostx.inputs.inputnode.bvals = os.path.join(input_dir, 'bvals')
nipype_bedpostx.inputs.xfibres.n_fibres = 1
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 1000
nipype_bedpostx.inputs.xfibres.n_jumps = 1250
nipype_bedpostx.inputs.xfibres.sample_every = 25
workflow = nipype.pipeline.engine.Workflow(name=OUTPUT_DIR)
workflow.add_nodes([nipype_bedpostx])
workflow.base_dir = base_dir
def test_create_bedpostx_pipeline():
fsl_course_dir = os.path.abspath(os.environ['FSL_COURSE_DATA'])
mask_file = os.path.join(fsl_course_dir,
"fdt2/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fdt2/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fdt2/subj1/data.nii.gz")
z_min = 62
z_size = 2
slice_mask = pe.Node(fsl.ExtractROI(x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=z_min,
z_size=z_size),
name="slice_mask")
slice_mask.inputs.in_file = mask_file
slice_dwi = pe.Node(fsl.ExtractROI(x_min=0,
x_size=-1,
y_min=0,
y_size=-1,
z_min=z_min,
z_size=z_size),
name="slice_dwi")
slice_dwi.inputs.in_file = dwi_file
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file
nipype_bedpostx.inputs.inputnode.bvals = bvals_file
nipype_bedpostx.inputs.xfibres.n_fibres = 1
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 0
nipype_bedpostx.inputs.xfibres.n_jumps = 1
nipype_bedpostx.inputs.xfibres.sample_every = 1
nipype_bedpostx.inputs.xfibres.cnlinear = True
nipype_bedpostx.inputs.xfibres.seed = 0
with warnings.catch_warnings():
warnings.simplefilter("ignore")
original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(),
name="original_bedpostx")
original_bedpostx.inputs.bvecs = bvecs_file
original_bedpostx.inputs.bvals = bvals_file
original_bedpostx.inputs.environ['FSLPARALLEL'] = ""
original_bedpostx.inputs.n_fibres = 1
original_bedpostx.inputs.fudge = 1
original_bedpostx.inputs.burn_in = 0
original_bedpostx.inputs.n_jumps = 1
original_bedpostx.inputs.sample_every = 1
original_bedpostx.inputs.seed = 0
test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test")
pipeline = pe.Workflow(name="test_bedpostx")
pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_")
pipeline.connect([
(slice_mask, original_bedpostx, [("roi_file", "mask")]),
(slice_mask, nipype_bedpostx, [("roi_file", "inputnode.mask")]),
(slice_dwi, original_bedpostx, [("roi_file", "dwi")]),
(slice_dwi, nipype_bedpostx, [("roi_file", "inputnode.dwi")]),
(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples",
list_to_filename), "volume1")]),
(original_bedpostx, test_f1, [("mean_fsamples", "volume2")]),
])
pipeline.run(plugin='Linear')
shutil.rmtree(pipeline.base_dir)
def test_create_bedpostx_pipeline():
fsl_course_dir = os.path.abspath('fsl_course_data')
mask_file = os.path.join(fsl_course_dir,
"fdt/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fdt/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fdt/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fdt/subj1/data.nii.gz")
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.dwi = dwi_file
nipype_bedpostx.inputs.inputnode.mask = mask_file
nipype_bedpostx.inputs.inputnode.bvecs = bvecs_file
nipype_bedpostx.inputs.inputnode.bvals = bvals_file
nipype_bedpostx.inputs.xfibres.n_fibres = 2
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 1000
nipype_bedpostx.inputs.xfibres.n_jumps = 1250
nipype_bedpostx.inputs.xfibres.sample_every = 25
with warnings.catch_warnings():
warnings.simplefilter("ignore")
original_bedpostx = pe.Node(interface=fsl.BEDPOSTX(),
name="original_bedpostx")
original_bedpostx.inputs.dwi = dwi_file
original_bedpostx.inputs.mask = mask_file
original_bedpostx.inputs.bvecs = bvecs_file
original_bedpostx.inputs.bvals = bvals_file
original_bedpostx.inputs.environ['FSLPARALLEL'] = ""
original_bedpostx.inputs.fibres = 2
original_bedpostx.inputs.weight = 1
original_bedpostx.inputs.burn_period = 1000
original_bedpostx.inputs.jumps = 1250
original_bedpostx.inputs.sampling = 25
test_f1 = pe.Node(util.AssertEqual(), name="mean_f1_test")
test_f2 = pe.Node(util.AssertEqual(), name="mean_f2_test")
test_th1 = pe.Node(util.AssertEqual(), name="mean_th1_test")
test_th2 = pe.Node(util.AssertEqual(), name="mean_th2_test")
test_ph1 = pe.Node(util.AssertEqual(), name="mean_ph1_test")
test_ph2 = pe.Node(util.AssertEqual(), name="mean_ph2_test")
pipeline = pe.Workflow(name="test_bedpostx")
pipeline.base_dir = tempfile.mkdtemp(prefix="nipype_test_bedpostx_")
def pickFirst(l):
return l[0]
def pickSecond(l):
return l[1]
pipeline.connect([
(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples", pickFirst),
"volume1")]),
(nipype_bedpostx, test_f2, [(("outputnode.mean_fsamples", pickSecond),
"volume1")]),
(nipype_bedpostx, test_th1, [(("outputnode.mean_thsamples", pickFirst),
"volume1")]),
(nipype_bedpostx, test_th2, [(("outputnode.mean_thsamples",
pickSecond), "volume1")]),
(nipype_bedpostx, test_ph1, [(("outputnode.mean_phsamples", pickFirst),
"volume1")]),
(nipype_bedpostx, test_ph2, [(("outputnode.mean_phsamples",
pickSecond), "volume1")]),
(original_bedpostx, test_f1, [(("mean_fsamples", pickFirst), "volume2")
]),
(original_bedpostx, test_f2, [(("mean_fsamples", pickSecond),
"volume2")]),
(original_bedpostx, test_th1, [(("mean_thsamples", pickFirst),
"volume2")]),
(original_bedpostx, test_th2, [(("mean_thsamples", pickSecond),
"volume2")]),
(original_bedpostx, test_ph1, [(("mean_phsamples", pickFirst),
"volume2")]),
(original_bedpostx, test_ph2, [(("mean_phsamples", pickSecond),
"volume2")])
])
pipeline.run(plugin='Linear')
shutil.rmtree(pipeline.base_dir)
[['subject_id', 'base_name']]), (bet, dtifit, [('mask_file', 'mask')])])
"""
Setup for Tracktography
-----------------------
Here we will create a workflow to enable probabilistic tracktography
and hard segmentation of the seed region
"""
tractography = pe.Workflow(name='tractography')
tractography.base_dir = os.path.abspath('fsl_dti_tutorial')
"""
estimate the diffusion parameters: phi, theta, and so on
"""
bedpostx = create_bedpostx_pipeline()
bedpostx.get_node("xfibres").iterables = ("n_fibres", [1, 2])
flirt = pe.Node(interface=fsl.FLIRT(), name='flirt')
flirt.inputs.in_file = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
flirt.inputs.dof = 12
"""
perform probabilistic tracktography
"""
probtrackx = pe.Node(interface=fsl.ProbTrackX(), name='probtrackx')
probtrackx.inputs.mode = 'seedmask'
probtrackx.inputs.c_thresh = 0.2
probtrackx.inputs.n_steps = 2000
probtrackx.inputs.step_length = 0.5
probtrackx.inputs.n_samples = 5000
#PBS -l pmem=600mb
#PBS -l walltime=4:00:00
#PBS -e ''' + base_dir + '''my_job.err
#PBS -o ''' + base_dir + '''my_job.log
module load python/2.7.6
module load nipype/0.8
module load fsl/5.0.5
'''
with open(os.path.join(base_dir, JOB_TEMPLATE_NAME), "w") as temp_file:
temp_file.write(template)
# ---------------Creating nipype workflow for bedpostx-------------------
nipype_bedpostx = create_bedpostx_pipeline("nipype_bedpostx")
nipype_bedpostx.inputs.inputnode.dwi = os.path.join(input_dir, 'data.nii')
nipype_bedpostx.inputs.inputnode.mask = os.path.join(
input_dir, 'nodif_brain_mask.nii')
nipype_bedpostx.inputs.inputnode.bvecs = os.path.join(input_dir, 'bvecs')
nipype_bedpostx.inputs.inputnode.bvals = os.path.join(input_dir, 'bvals')
nipype_bedpostx.inputs.xfibres.n_fibres = 1
nipype_bedpostx.inputs.xfibres.fudge = 1
nipype_bedpostx.inputs.xfibres.burn_in = 1000
nipype_bedpostx.inputs.xfibres.n_jumps = 1250
nipype_bedpostx.inputs.xfibres.sample_every = 25
workflow = nipype.pipeline.engine.Workflow(name = OUTPUT_DIR)
workflow.add_nodes([nipype_bedpostx])
workflow.base_dir = base_dir
"""
anat2b0 = pe.Node(interface=fsl.FLIRT(dof=6), name="anat2b0")
anat2b0.inputs.cost_func = "mutualinfo" # between-modality
mni2anat = pe.Node(interface=fsl.FLIRT(dof=12), name="mni2anat")
mni2anat.inputs.in_file = fsl.Info.standard_image("MNI152_T1_2mm_brain.nii.gz")
mni2anat.inputs.cost_func = "corratio"
"""
register MNI152-->b0 using matrixes we have got above
"""
xfmconcat = pe.Node(interface=fsl.ConvertXFM(concat_xfm=True), name="xfmconcat")
"""
estimate the diffusion parameters: phi, theta, and so on
"""
bedpostx = create_bedpostx_pipeline(name="bedpostx")
bedpostx.inputs.xfibres.n_fibres = 2
preTract = pe.Workflow(name="preTract")
preTract.base_dir = workingDir
preTract.connect(
[
(infosource, datasource, [("subject_id", "subject_id"), (("subject_id", subjrlf), "template_args")]),
(datasource, fslroi, [("dwi", "in_file")]),
(datasource, eddycorrect, [("dwi", "inputnode.in_file")]),
(fslroi, bet, [("roi_file", "in_file")]),
(fslroi, anat2b0, [("roi_file", "reference")]),
(datasource, anat2b0, [("mri", "in_file")]),
(datasource, mni2anat, [("mri", "reference")]),
(mni2anat, xfmconcat, [("out_matrix_file", "in_file")]),
def dti_preprocessing(args):
'''
Write it later
'''
dataLoc = '/Volumes/CCNC_3T/KMA'
subject_list = args.subjects
# Make exclusion mask in order to exclude tracks
# going towards posterior paths from the thalamus
for subject in subject_list:
dtiDir = os.path.join(dataLoc, subject, 'DTI')
# Check dicom
dicoms = glob.glob(dtiDir+'/*dcm') + \
glob.glob(dtiDir+'/*IMA') + \
glob.glob(dtiDir+'/dicom/*dcm') + \
glob.glob(dtiDir+'/dicom/*IMA')
if dicoms:
dicomDir = os.path.join(dtiDir, 'dicom')
if not os.path.isdir(dicomDir):
os.mkdir(dicomDir)
try:
for dicomFile in dicoms:
shutil.move(dicomFile, dicomDir)
except:
pass
dicoms = glob.glob(dicomDir+'/*dcm') + glob.glob(dicomDir+'/*IMA')
nifti = glob.glob(dtiDir+'/*nii.gz')
# if no nifti
if not nifti:
converter = Dcm2nii(
source_names = dicoms,
gzip_output = True,
output_dir = dtiDir)
converter.run()
# raw data
data = glob.glob(dtiDir+'/2*DTI*.nii.gz')[0]
bvec = glob.glob(dtiDir+'/2*.bvec')[0]
bval = glob.glob(dtiDir+'/2*.bval')[0]
# preprocessed data
eddy_out = os.path.join(dtiDir, 'data_eddy.nii.gz')
newBvec = os.path.join(dtiDir, 'bvecs_new')
nodif = os.path.join(dtiDir, 'nodif.nii.gz')
nodif_brain = os.path.join(dtiDir, 'nodif_brain.nii.gz')
nodif_brain_mask = os.path.join(dtiDir, 'nodif_brain_mask.nii.gz')
fa_map = os.path.join(dtiDir, 'dti_FA.nii.gz')
bedpostxdir = os.path.join(subject, 'DTI.bedpostX')
if not os.path.isfile(eddy_out):
eddy = fsl.EddyCorrect(
in_file = data,
out_file = eddy_out)
eddy.run()
if not os.path.isfile(newBvec):
bvecCorrectCommand = 'bash /Volumes/CCNC_3T/KMA/kmaproject/fdt_rotate_bvecs.sh \
{origBvec} {newBvec} {ecclog}'.format(
origBvec = bvec,
newBvec = newBvec,
ecclog = eddy_out.split('nii.gz')[0]+'ecclog'
)
print bvecCorrectCommand
os.popen(bvecCorrectCommand).read()
if not os.path.isfile(nodif):
extractROI = fsl.ExtractROI(
in_file = eddy_out,
t_min = 0,
t_size = 1,
roi_file = nodif,)
extractROI.run()
if not os.path.isfile(nodif_brain_mask):
bet = fsl.BET(
in_file = eddy_out,
frac = .35,
mask = True,
out_file = nodif_brain
)
bet.run()
if not os.path.isfile(fa_map):
dtifit = fsl.DTIFit(
bvals = bval,
bvecs = newBvec,
dwi = eddy_out,
mask = nodif_brain_mask,
base_name = dtiDir+'/DTI')
dtifit.run()
params = dict(n_fibres = 2,
fudge = 1,
burn_in = 1000,
n_jumps = 1250,
sample_every = 25)
bpwf = create_bedpostx_pipeline('nipype_bedpostx', params)
bpwf.inputs.inputnode.dwi = eddy_out
bpwf.inputs.inputnode.mask = nodif_brain_mask
bpwf.inputs.inputnode.bvecs = newBvec
bpwf.inputs.inputnode.bvals = bval
bpwf.run()