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
"""
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 = fsl_wf.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
def test_create_bedpostx_pipeline():
fsl_course_dir = os.environ["FSL_COURSE_DATA"]
mask_file = os.path.join(fsl_course_dir, "fsl_course_data/fdt/subj1.bedpostX/nodif_brain_mask.nii.gz")
bvecs_file = os.path.join(fsl_course_dir, "fsl_course_data/fdt/subj1/bvecs")
bvals_file = os.path.join(fsl_course_dir, "fsl_course_data/fdt/subj1/bvals")
dwi_file = os.path.join(fsl_course_dir, "fsl_course_data/fdt/subj1/data.nii.gz")
nipype_bedpostx = fsl_wf.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 = test_dir
def pickFirst(l):
return l[0]
def pickSecond(l):
return l[1]
pipeline.connect([(nipype_bedpostx, test_f1, [(("outputnode.mean_fsamples", pickFirst), "inputnode.volume1")]),
(nipype_bedpostx, test_f2, [(("outputnode.mean_fsamples", pickSecond), "inputnode.volume1")]),
(nipype_bedpostx, test_th1, [(("outputnode.mean_thsamples", pickFirst), "inputnode.volume1")]),
(nipype_bedpostx, test_th2, [(("outputnode.mean_thsamples", pickSecond), "inputnode.volume1")]),
(nipype_bedpostx, test_ph1, [(("outputnode.mean_phsamples", pickFirst), "inputnode.volume1")]),
(nipype_bedpostx, test_ph2, [(("outputnode.mean_phsamples", pickSecond), "inputnode.volume1")]),
(original_bedpostx, test_f1, [(("mean_fsamples", pickFirst), "inputnode.volume2")]),
(original_bedpostx, test_f2, [(("mean_fsamples", pickSecond), "inputnode.volume2")]),
(original_bedpostx, test_th1, [(("mean_thsamples", pickFirst), "inputnode.volume2")]),
(original_bedpostx, test_th2, [(("mean_thsamples", pickSecond), "inputnode.volume2")]),
(original_bedpostx, test_ph1, [(("mean_phsamples", pickFirst), "inputnode.volume2")]),
(original_bedpostx, test_ph2, [(("mean_phsamples", pickSecond), "inputnode.volume2")])
])
pipeline.run(inseries=True)
