def calculate_metrics(in_file: Path, out_files: dict):
"""
Calculate various DWI metrics based on Diffusion's kurtosis tensor
estimation.
Parameters
----------
in_file : Path
Path to preprocessed DWI series
out_files : dict
A dictionary containing paths to various metrics to be calculated
"""
tensor = out_files.pop("tensor")
tsr = mrt.FitTensor()
tsr.inputs.in_file = in_file
tsr.inputs.out_file = tensor
tsr.inputs.args = "-quiet"
if not tensor.exists():
tsr.run()
comp = mrt.TensorMetrics()
comp.inputs.in_file = tensor
comp_args = ""
for key, val in out_files.items():
comp_args += f"-{key} {val} "
comp.inputs.args = comp_args
return tsr, comp
def fit_tensors(dwi_file: str, mask_file: str, dti_file: str):
dilated_mask = mask_file.replace(".mif", "_dilated.mif")
cmd = f"maskfilter {mask_file} dilate {dilated_mask} -npass 3"
os.system(cmd)
tsr = mrt.FitTensor()
tsr.inputs.in_file = dwi_file
tsr.inputs.in_mask = dilated_mask
tsr.inputs.out_file = dti_file
print(tsr.cmdline)
tsr.run()
comp = mrt.TensorMetrics()
comp.inputs.in_file = dti_file
comp.inputs.out_fa = dti_file.replace("dti", "fa")
comp.inputs.args = "-force"
print(comp.cmdline)
comp.run()
return comp.inputs.out_fa
def fit_tensors(dwi_file: Path, mask_file: Path, dti_file: Path,
fa_file: Path):
dilated_mask = mask_file.parent / f"{mask_file.stem}_dilated.mif"
cmd = f"maskfilter {mask_file} dilate {dilated_mask} -npass 3"
os.system(cmd)
tsr = mrt.FitTensor()
tsr.inputs.in_file = dwi_file
tsr.inputs.in_mask = dilated_mask
tsr.inputs.out_file = dti_file
print(tsr.cmdline)
tsr.run()
comp = mrt.TensorMetrics()
comp.inputs.in_file = dti_file
comp.inputs.out_fa = fa_file
print(comp.cmdline)
comp.run()
dti_file.unlink()
return comp.inputs.out_fa
def create_DWI_workflow(
subject_list,
bids_dir,
work_dir,
out_dir,
bids_templates,
):
# create initial workflow
wf = Workflow(name='DWI', base_dir=work_dir)
# use infosource to iterate workflow across subject list
n_infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="subject_source"
# input: 'subject_id'
# output: 'subject_id'
)
# runs the node with subject_id = each element in subject_list
n_infosource.iterables = ('subject_id', subject_list)
# select matching files from bids_dir
n_selectfiles = Node(interface=SelectFiles(templates=bids_templates,
base_directory=bids_dir),
name='get_subject_data')
wf.connect([(n_infosource, n_selectfiles, [('subject_id', 'subject_id_p')])
])
# DWIDenoise
# https://nipype.readthedocs.io/en/latest/api/generated/nipype.interfaces.mrtrix3.preprocess.html
n_denoise = Node(interface=mrt.DWIDenoise(), name='n_denoise')
wf.connect([(n_selectfiles, n_denoise, [('DWI_all', 'in_file')])])
# datasink
n_datasink = Node(interface=DataSink(base_directory=out_dir),
name='datasink')
wf.connect([(n_selectfiles, n_datasink, [('all_b0_PA',
'all_b0_PA_unchanged')]),
(n_denoise, n_datasink, [('out_file', 'DWI_all_denoised')])])
########## I'VE ADDED IN ##########################################################################
# MRDeGibbs
# https://nipype.readthedocs.io/en/latest/api/generated/nipype.interfaces.mrtrix3.preprocess.html
n_degibbs = Node(
interface=mrt.MRDeGibbs(out_file='DWI_all_denoised_degibbs.mif'),
name='n_degibbs')
wf.connect([(n_denoise, n_degibbs, [('out_file', 'in_file')])])
wf.connect([(n_degibbs, n_datasink, [('out_file',
'DWI_all_denoised_degibbs.mif')])])
# DWI Extract
n_dwiextract = Node(interface=mrt.DWIExtract(bzero=True,
out_file='b0vols.mif'),
name='n_dwiextract')
wf.connect([(n_degibbs, n_dwiextract, [('out_file', 'in_file')])])
wf.connect([(n_dwiextract, n_datasink, [('out_file', 'noddi_b0_degibbs')])
])
# MRcat
n_mrcat = Node(
interface=mrcatfunc.MRCat(
#axis=3,
out_file='b0s.mif'),
name='n_mrcat')
# Connect DTI_B0_PA to mrcat node
wf.connect([(n_selectfiles, n_mrcat, [('DTI_B0_PA', 'in_file1')])])
wf.connect([(n_dwiextract, n_mrcat, [('out_file', 'in_file2')])])
# Output the mrcat file into file 'noddi_and_PA_b0s.mif'
wf.connect([(n_mrcat, n_datasink, [('out_file', 'noddi_and_PA_b0s.mif')])])
# DWIfslpreproc
n_dwifslpreproc = Node(interface=preprocfunc.DWIFslPreProc(
out_file='preprocessedDWIs.mif', use_header=True),
name='n_dwifslpreproc')
# Connect output of degibbs to dwifslpreproc node
wf.connect([(n_degibbs, n_dwifslpreproc, [('out_file', 'in_file')])])
# Connect output of mrcat to se_epi input
wf.connect([(n_mrcat, n_dwifslpreproc, [('out_file', 'se_epi_file')])])
# Put output of dwifslpreproc into 'preprocessedDWIs.mif'
wf.connect([(n_dwifslpreproc, n_datasink, [('out_file',
'preprocessedDWIs.mif')])])
# DWI bias correct
n_dwibiascorrect = Node(
interface=preprocess.DWIBiasCorrect(use_ants=True),
name='n_dwibiascorrect',
)
wf.connect([(n_dwifslpreproc, n_dwibiascorrect, [('out_file', 'in_file')])
])
wf.connect([(n_dwibiascorrect, n_datasink,
[('out_file', 'ANTSpreprocessedDWIs.mif')])])
#DWI2mask
n_dwi2mask = Node(interface=mrt.BrainMask(out_file='mask.mif'),
name='n_dwi2mask')
wf.connect([(n_dwibiascorrect, n_dwi2mask, [('out_file', 'in_file')])])
wf.connect([(n_dwi2mask, n_datasink, [('out_file', 'mask.mif')])])
## A) Fixel-based analysis
#DWI2response
n_dwi2response = Node(interface=mrt.ResponseSD(algorithm='dhollander',
wm_file='wm_res.txt',
gm_file='gm_res.txt',
csf_file='csf_res.txt'),
name='n_dwi2response')
wf.connect([(n_dwibiascorrect, n_dwi2response, [('out_file', 'in_file')])])
wf.connect([(n_dwi2response, n_datasink, [('wm_file', 'wm_res.txt')])])
wf.connect([(n_dwi2response, n_datasink, [('gm_file', 'gm_res.txt')])])
wf.connect([(n_dwi2response, n_datasink, [('csf_file', 'csf_res.txt')])])
#DWI2fod
n_dwi2fod = Node(interface=mrt.ConstrainedSphericalDeconvolution(
algorithm='msmt_csd',
wm_odf='wmfod.mif',
gm_odf='gmfod.mif',
csf_odf='csffod.mif'),
name='n_dwi2fod')
# connect outputs of dwi2fod into dwi2response
wf.connect([(n_dwibiascorrect, n_dwi2fod, [('out_file', 'in_file')])])
wf.connect([(n_dwi2response, n_dwi2fod, [('wm_file', 'wm_txt')])])
wf.connect([(n_dwi2response, n_dwi2fod, [('gm_file', 'gm_txt')])])
wf.connect([(n_dwi2response, n_dwi2fod, [('csf_file', 'csf_txt')])])
# output wmfod file from dwi2fod
wf.connect([(n_dwi2fod, n_datasink, [('wm_odf', 'wmfod.mif')])])
wf.connect([(n_dwi2fod, n_datasink, [('gm_odf', 'gmfod.mif')])])
wf.connect([(n_dwi2fod, n_datasink, [('csf_odf', 'csffod.mif')])])
#mrconvert to extract Z component of wmfod
n_mrconvert_fod = Node(interface=utils.MRConvert(out_file='Zwmfod.mif',
coord=[3, 0]),
name='n_mrconvert_fod')
wf.connect([(n_dwi2fod, n_mrconvert_fod, [('wm_odf', 'in_file')])])
wf.connect([(n_mrconvert_fod, n_datasink, [('out_file', 'Zwmfod.mif')])])
# Concatenate all wm, gm, csf fod files to see their distribution throughout Brain
n_mrcat_fod = Node(interface=mrcatfunc.MRCat(out_file='vf.mif'),
name='n_mrcat_fod')
# Connect Zwmfod, gmfod and csffod as inputs
wf.connect([(n_mrconvert_fod, n_mrcat_fod, [('out_file', 'in_file1')])])
wf.connect([(n_dwi2fod, n_mrcat_fod, [('gm_odf', 'in_file2')])])
wf.connect([(n_dwi2fod, n_mrcat_fod, [('csf_odf', 'in_file3')])])
# Output the mrcat file into file into 'vf.mif'
wf.connect([(n_mrcat_fod, n_datasink, [('out_file', 'vf.mif')])])
#fod2fixel wmfod.mif wmfixels -fmls_peak_value 0 -fmls_integral 0.10 -afd afd.mif -peak peak.mif -disp disp.mif
# OUTPUTS: -afd afd.mif -peak peak.mif -disp disp.mif
n_fod2fixel = Node(
interface=fod2fixelfunc.fod2fixel(
out_file='wmfixels',
#afd_file = 'afd.mif',
peak_file='peak.mif',
disp_file='disp.mif'),
name='n_fod2fixel')
# let the peak value parameter be trialed as multiple values
n_fod2fixel.iterables = ('fmls_peak_value', [0, 0.10, 0.50])
n_fod2fixel.iterables = ('fmls_integral', [0, 0.10, 0.50])
# obtain wm fibre image as input
wf.connect([(n_dwi2fod, n_fod2fixel, [('wm_odf', 'in_file')])])
# ouputs of fod2fixel
wf.connect([(n_fod2fixel, n_datasink, [('out_file', 'wmfixels')])])
wf.connect([(n_fod2fixel, n_datasink, [('afd_file', 'afd.mif')])])
wf.connect([(n_fod2fixel, n_datasink, [('peak_file', 'peak.mif')])])
wf.connect([(n_fod2fixel, n_datasink, [('disp_file', 'disp.mif')])])
## Fixel2peaks
n_fixel2peaks = Node(interface=fixel2peaksfunc.fixel2peaks(
out_file='peaks_wmdirections.mif'),
name='n_fixel2peaks')
n_fixel2peaks.iterables = ('number', [1, 2, 3])
# obtain directions file in output folder of fod2fixel, as input
wf.connect([(n_fod2fixel, n_fixel2peaks, [('out_file', 'in_file')])])
# ouputs of fixel2peaks
wf.connect([(n_fixel2peaks, n_datasink, [('out_file',
'peaks_wmdirections.mif')])])
#mrmath to find normalised value of peak WM directions
n_mrmath = Node(interface=mrt.MRMath(
axis=3, operation='norm', out_file='norm_peaks_wmdirections.mif'),
name='n_mrmath')
wf.connect([(n_fixel2peaks, n_mrmath, [('out_file', 'in_file')])])
wf.connect([(n_mrmath, n_datasink, [('out_file',
'norm_peaks_wmdirections.mif')])])
# mrcalc to divide peak WM direction by normalised value
n_mrcalc = Node(interface=mrcalcfunc.MRCalc(operation='divide',
out_file='wm_peak_dir.mif'),
name='n_mrcalc')
wf.connect([(n_fixel2peaks, n_mrcalc, [('out_file', 'in_file1')])])
wf.connect([(n_mrmath, n_mrcalc, [('out_file', 'in_file2')])])
wf.connect([(n_mrcalc, n_datasink, [('out_file', 'WM_peak_dir.mif')])])
#mrconvert to extract Z component of peak directions
n_mrconvert2 = Node(interface=utils.MRConvert(
out_file='Zpeak_WM_Directions.mif', coord=[3, 2]),
name='n_mrconvert2')
wf.connect([(n_mrcalc, n_mrconvert2, [('out_file', 'in_file')])])
wf.connect([(n_mrconvert2, n_datasink, [('out_file',
'Zpeak_WM_Directions.mif')])])
# mrcalc to find absolute value
n_mrcalc2 = Node(interface=mrcalcfunc.MRCalc(
operation='abs', out_file='absZpeak_WM_Directions.mif'),
name='n_mrcalc2')
wf.connect([(n_mrconvert2, n_mrcalc2, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc2, n_datasink, [('out_file',
'absZpeak_WM_Directions.mif')])])
# mrcalc to get angle by doing inverse cosine
n_mrcalc3 = Node(interface=mrcalcfunc.MRCalc(
operation='acos', out_file='acosZpeak_WM_Directions.mif'),
name='n_mrcalc3')
wf.connect([(n_mrcalc2, n_mrcalc3, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc3, n_datasink, [('out_file',
'acosZpeak_WM_Directions.mif')])])
# mrcalc to convert angle to degrees
n_mrcalc4 = Node(interface=mrcalcfunc.MRCalc(
operation='multiply', operand=180, out_file='Fixel1_Z_angle.mif'),
name='n_mrcalc4')
wf.connect([(n_mrcalc3, n_mrcalc4, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc4, n_datasink, [('out_file', 'Fixel1_Z_angle.mif')])])
n_mrcalc5 = Node(interface=mrcalcfunc.MRCalc(
operation='divide',
operand=3.14159265,
out_file='Fixel1_Z_cos_deg.mif'),
name='n_mrcalc5')
wf.connect([(n_mrcalc4, n_mrcalc5, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc5, n_datasink, [('out_file', 'Fixel1_Z_cos_deg.mif')])
])
## B) Tensor-based analysis
#dwi2tensor
n_dwi2tensor = Node(interface=mrt.FitTensor(out_file='dti.mif'),
name='n_dwi2tensor')
wf.connect([(n_dwibiascorrect, n_dwi2tensor, [('out_file', 'in_file')])])
wf.connect([(n_dwi2mask, n_dwi2tensor, [('out_file', 'in_mask')])])
wf.connect([(n_dwi2tensor, n_datasink, [('out_file', 'dt.mif')])])
#tensor2metric
n_tensor2metric = Node(interface=tensor2metricfunc.tensor2metric(
modulate='none', num=1, vector_file='eigenvector.mif'),
name='n_tensor2metric')
wf.connect([(n_dwi2tensor, n_tensor2metric, [('out_file', 'input_file')])])
wf.connect([(n_tensor2metric, n_datasink, [('vector_file',
'eigenvector.mif')])])
#mrconvert to get Z eigenvector
n_mrconvert3 = Node(interface=utils.MRConvert(coord=[3, 2],
out_file='eigenvectorZ.mif'),
name='n_mrconvert3')
wf.connect([(n_tensor2metric, n_mrconvert3, [('vector_file', 'in_file')])])
wf.connect([(n_mrconvert3, n_datasink, [('out_file', 'eigenvectorZ.mif')])
])
#ALL SUBSEQUENT STEPS GET ANGLE IN DEGREES
# mrcalc to find absolute value
n_mrcalc6 = Node(interface=mrcalcfunc.MRCalc(
operation='abs', out_file='abs_eigenvectorZ.mif'),
name='n_mrcalc6')
wf.connect([(n_mrconvert3, n_mrcalc6, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc6, n_datasink, [('out_file', 'abs_eigenvectorZ.mif')])
])
# mrcalc to get angle by doing inverse cosine
n_mrcalc7 = Node(interface=mrcalcfunc.MRCalc(
operation='acos', out_file='acos_eigenvectorZ.mif'),
name='n_mrcalc7')
wf.connect([(n_mrcalc6, n_mrcalc7, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc7, n_datasink, [('out_file', 'acos_eigenvectorZ.mif')
])])
# mrcalc to convert angle to degrees
n_mrcalc8 = Node(
interface=mrcalcfunc.MRCalc(operation='multiply',
operand=180,
out_file='degrees_eigenvectorZ.mif'),
name='n_mrcalc8')
wf.connect([(n_mrcalc7, n_mrcalc8, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc8, n_datasink, [('out_file',
'degrees_eigenvectorZ.mif')])])
n_mrcalc9 = Node(interface=mrcalcfunc.MRCalc(operation='divide',
operand=3.14159265,
out_file='dti_z_cos_deg.mif'),
name='n_mrcalc9')
wf.connect([(n_mrcalc8, n_mrcalc9, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc9, n_datasink, [('out_file', 'dti_z_cos_deg.mif')])])
# Difference image between fixel based and tensor based outputs
n_mrcalc10 = Node(interface=mrcalcfunc.MRCalc(
operation='subtract', out_file='diff_imag_tensor_minus_fixel.mif'),
name='n_mrcalc10')
wf.connect([(n_mrcalc9, n_mrcalc10, [('out_file', 'in_file1')])])
wf.connect([(n_mrcalc5, n_mrcalc10, [('out_file', 'in_file2')])])
wf.connect([(n_mrcalc10, n_datasink,
[('out_file', 'diff_imag_tensor_minus_fixel.mif')])])
#################################################################################3
return wf
def dholl_preproc_wf(shells=[0, 1000, 2000],
lmax=[0, 8, 8],
sshell=False,
noreorient=False,
template_dir=None,
template_label=None,
wdir=None,
nthreads=1,
name='dholl_preproc_wf'):
"""
Set up Dhollander response preproc workflow
No assumption of registration to T1w space is made
"""
if template_dir is None or template_label is None:
print("Missing template info")
raise IOError
# Grab template data
templateGrabber = io.getTemplate(template_dir=template_dir,
template_label=template_label,
wdir=wdir)
# Convert nii to mif
dwiConvert = pe.Node(mrt.MRConvert(), name='dwiConvert')
dwiConvert.base_dir = wdir
dwiConvert.inputs.nthreads = nthreads
dwiConvert.interface.num_threads = nthreads
# dwi2response - included but not used
dwi2response = pe.Node(mrt.ResponseSD(), name='dwi2response')
dwi2response.base_dir = wdir
dwi2response.inputs.algorithm = 'dhollander'
dwi2response.inputs.wm_file = 'space-dwi_model-CSD_WMResp.txt'
dwi2response.inputs.gm_file = 'space-dwi_model-CSD_GMResp.txt'
dwi2response.inputs.csf_file = 'space-dwi_model-CSD_CSFResp.txt'
dwi2response.inputs.max_sh = lmax
dwi2response.inputs.shell = shells
dwi2response.inputs.nthreads = nthreads
dwi2response.interface.num_threads = nthreads
# Convert mask (nii) to mif
maskConvert = pe.Node(mrt.MRConvert(), name='maskConvert')
maskConvert.base_dir = wdir
maskConvert.inputs.nthreads = nthreads
maskConvert.interface.num_threads = nthreads
# dwi2fod
dwi2fod = pe.Node(mrt.EstimateFOD(), name='dwi2fod')
dwi2fod.base_dir = wdir
dwi2fod.inputs.algorithm = 'msmt_csd'
dwi2fod.inputs.shell = shells
dwi2fod.inputs.wm_odf = 'space-dwi_model-CSD_WMFOD.mif'
if sshell is False:
dwi2fod.inputs.gm_odf = 'space-dwi_model-CSD_GMFOD.mif'
dwi2fod.inputs.csf_odf = 'space-dwi_model-CSD_CSFFOD.mif'
dwi2fod.inputs.nthreads = nthreads
dwi2fod.interface.num_threads = nthreads
# mtnormalise
mtnormalise = pe.Node(mrt.MTNormalise(), name='mtnormalise')
mtnormalise.base_dir = wdir
mtnormalise.inputs.out_wm = 'space-dwi_model-CSD_WMFODNorm.mif'
if sshell is False:
mtnormalise.inputs.out_gm = 'space-dwi_model-CSD_GMFODNorm.mif'
mtnormalise.inputs.out_csf = 'space-dwi_model-CSD_CSFFODNorm.mif'
mtnormalise.inputs.nthreads = nthreads
mtnormalise.interface.num_threads = nthreads
# Registration
MRRegister = pe.Node(mrt.MRRegister(), name='MRRegister')
MRRegister.base_dir = wdir
# MRRegister.inputs.ref_file = template
MRRegister.inputs.nl_warp = [
'from-dwi_to-Template_xfm.mif', 'from-Template_to-dwi_xfm.mif'
]
if noreorient is not False:
MRRegister.inputs.noreorientation = noreorient
MRRegister.inputs.nthreads = nthreads
MRRegister.interface.num_threads = nthreads
# Transforms
WarpSelect1 = pe.Node(niu.Select(), name='WarpSelect1')
WarpSelect1.base_dir = wdir
WarpSelect1.inputs.index = [0]
WarpSelect1.interface.num_threads = nthreads
WarpSelect2 = pe.Node(niu.Select(), name='WarpSelect2')
WarpSelect2.base_dir = wdir
WarpSelect2.inputs.index = [1]
WarpSelect2.interface.num_threads = nthreads
# Warp data
MaskTransform = pe.Node(mrt.MRTransform(), name='MaskTransform')
MaskTransform.base_dir = wdir
MaskTransform.inputs.out_file = 'space-Template_brainmask.mif'
MaskTransform.inputs.nthreads = nthreads
MaskTransform.interface.num_threads = nthreads
FODTransform = pe.Node(mrt.MRTransform(), name='FODTransform')
FODTransform.base_dir = wdir
FODTransform.inputs.out_file = 'space-Template_model-CSD_WMFODNorm.mif'
FODTransform.inputs.nthreads = nthreads
FODTransform.interface.num_threads = nthreads
# Tensor processing
DWINormalise = pe.Node(mrt.DWINormalise(), name='DWINormalise')
DWINormalise.base_dir = wdir
DWINormalise.inputs.out_file = 'space-dwi_dwiNorm.mif'
DWINormalise.inputs.nthreads = nthreads
DWINormalise.interface.num_threads = nthreads
DWITransform = pe.Node(mrt.MRTransform(), name='DWITransform')
DWITransform.base_dir = wdir
DWITransform.inputs.out_file = 'space-Template_dwiNorm.mif'
DWITransform.inputs.nthreads = nthreads
DWITransform.interface.num_threads = nthreads
FitTensor = pe.Node(mrt.FitTensor(), name='FitTensor')
FitTensor.base_dir = wdir
FitTensor.inputs.out_file = 'space-Template_desc-WLS_model-DTI_tensor.mif'
FitTensor.inputs.nthreads = nthreads
FitTensor.interface.num_threads = nthreads
TensorMetrics = pe.Node(mrt.TensorMetrics(), name='TensorMetrics')
TensorMetrics.base_dir = wdir
TensorMetrics.inputs.out_fa = 'space-Template_model-DTI_FA.mif'
TensorMetrics.inputs.out_adc = 'space-Template_model-DTI_MD.mif'
TensorMetrics.inputs.out_ad = 'space-Template_model-DTI_AD.mif'
TensorMetrics.inputs.out_rd = 'space-Template_model-DTI_RD.mif'
TensorMetrics.inputs.nthreads = nthreads
TensorMetrics.interface.num_threads = nthreads
# Build workflow
workflow = pe.Workflow(name=name)
# Single shell
if sshell is True:
workflow.connect([
# Compute FOD
(dwiConvert, dwi2response, [('out_file', 'in_file')]),
(dwiConvert, dwi2fod, [('out_file', 'in_file')]),
(dwi2response, dwi2fod, [('wm_file', 'wm_txt'),
('csf_file', 'csf_txt')]),
(dwi2fod, mtnormalise, [('wm_odf', 'in_wm'),
('csf_odf', 'in_csf')]),
(maskConvert, dwi2response, [('out_file', 'in_mask')]),
(maskConvert, dwi2fod, [('out_file', 'mask_file')]),
(maskConvert, mtnormalise, [('out_file', 'mask')]),
(maskConvert, MRRegister, [('out_file', 'mask1')]),
(templateGrabber, MRRegister, [('wm_fod', 'ref_file'),
('mask', 'mask2')]),
(mtnormalise, MRRegister, [('out_wm', 'in_file')]),
(MRRegister, WarpSelect1, [('nl_warp', 'inlist')]),
(MRRegister, WarpSelect2, [('nl_warp', 'inlist')]),
(maskConvert, MaskTransform, [('out_file', 'in_file')]),
(WarpSelect1, MaskTransform, [('out', 'warp')]),
(mtnormalise, FODTransform, [('out_wm', 'in_file')]),
(WarpSelect1, FODTransform, [('out', 'warp')]),
# Compute tensors
(dwiConvert, DWINormalise, [('out_file', 'in_file')]),
(maskConvert, DWINormalise, [('out_file', 'in_mask')]),
(DWINormalise, DWITransform, [('out_file', 'in_file')]),
(WarpSelect1, DWITransform, [('out', 'warp')]),
(DWITransform, FitTensor, [('out_file', 'in_file')]),
(MaskTransform, FitTensor, [('out_file', 'in_mask')]),
(FitTensor, TensorMetrics, [('out_file', 'in_file')]),
(MaskTransform, TensorMetrics, [('out_file', 'in_mask')])
])
# For multi-shell
else:
workflow.connect([
# Compute FOD
(dwiConvert, dwi2response, [('out_file', 'in_file')]),
(dwiConvert, dwi2fod, [('out_file', 'in_file')]),
(dwi2response, dwi2fod, [('wm_file', 'wm_txt'),
('gm_file', 'gm_txt'),
('csf_file', 'csf_txt')]),
(dwi2fod, mtnormalise, [('wm_odf', 'in_wm'), ('gm_odf', 'in_gm'),
('csf_odf', 'in_csf')]),
(maskConvert, dwi2response, [('out_file', 'in_mask')]),
(maskConvert, dwi2fod, [('out_file', 'mask_file')]),
(maskConvert, mtnormalise, [('out_file', 'mask')]),
(maskConvert, MRRegister, [('out_file', 'mask1')]),
(templateGrabber, MRRegister, [('wm_fod', 'ref_file'),
('mask', 'mask2')]),
(mtnormalise, MRRegister, [('out_wm', 'in_file')]),
(MRRegister, WarpSelect1, [('nl_warp', 'inlist')]),
(MRRegister, WarpSelect2, [('nl_warp', 'inlist')]),
(maskConvert, MaskTransform, [('out_file', 'in_file')]),
(WarpSelect1, MaskTransform, [('out', 'warp')]),
(mtnormalise, FODTransform, [('out_wm', 'in_file')]),
(WarpSelect1, FODTransform, [('out', 'warp')]),
# Compute tensors
(dwiConvert, DWINormalise, [('out_file', 'in_file')]),
(maskConvert, DWINormalise, [('out_file', 'in_mask')]),
(DWINormalise, DWITransform, [('out_file', 'in_file')]),
(WarpSelect1, DWITransform, [('out', 'warp')]),
(DWITransform, FitTensor, [('out_file', 'in_file')]),
(MaskTransform, FitTensor, [('out_file', 'in_mask')]),
(FitTensor, TensorMetrics, [('out_file', 'in_file')]),
(MaskTransform, TensorMetrics, [('out_file', 'in_mask')])
])
return workflow
def create_DWI_workflow(
subject_list,
bids_dir,
work_dir,
out_dir,
bids_templates,
):
# create initial workflow
wf = Workflow(name='DWI', base_dir=work_dir)
# use infosource to iterate workflow across subject list
n_infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="subject_source"
# input: 'subject_id'
# output: 'subject_id'
)
# runs the node with subject_id = each element in subject_list
n_infosource.iterables = ('subject_id', subject_list)
# select matching files from bids_dir
n_selectfiles = Node(interface=SelectFiles(templates=bids_templates,
base_directory=bids_dir),
name='get_subject_data')
wf.connect([(n_infosource, n_selectfiles, [('subject_id', 'subject_id_p')])
])
########## IMPLEMENTING MRTRIX COMMANDS FOR IMAGE ANALYSIS #######################################
## 1) Preprocessing of Data
# https://nipype.readthedocs.io/en/latest/api/generated/nipype.interfaces.mrtrix3.preprocess.html
# DWIDenoise to remove Gaussian noise
n_denoise = Node(interface=mrt.DWIDenoise(), name='n_denoise')
wf.connect([(n_selectfiles, n_denoise, [('DWI_all', 'in_file')])])
# datasink
n_datasink = Node(interface=DataSink(base_directory=out_dir),
name='datasink')
# output denoised data into 'DWI_all_denoised'
wf.connect([(n_selectfiles, n_datasink, [('all_b0_PA',
'all_b0_PA_unchanged')]),
(n_denoise, n_datasink, [('out_file', 'DWI_all_denoised')])])
# MRDeGibbs to remove Gibbs ringing artifact
n_degibbs = Node(
interface=mrt.MRDeGibbs(out_file='DWI_all_denoised_degibbs.mif'),
name='n_degibbs')
# input denoised data into degibbs function
wf.connect([(n_denoise, n_degibbs, [('out_file', 'in_file')])])
# output degibbs data into 'DWI_all_denoised_degibbs.mif'
wf.connect([(n_degibbs, n_datasink, [('out_file',
'DWI_all_denoised_degibbs.mif')])])
# DWI Extract to extract b0 volumes from multi-b image data
n_dwiextract = Node(interface=mrt.DWIExtract(bzero=True,
out_file='b0vols.mif'),
name='n_dwiextract')
# input degibbs data into dwiextract function
wf.connect([(n_degibbs, n_dwiextract, [('out_file', 'in_file')])])
# output extracted b0 volume from degibbs data (contains multiple b values)
wf.connect([(n_dwiextract, n_datasink, [('out_file', 'noddi_b0_degibbs')])
])
# MRcat to combine b0 volumes from input image and reverse phase encoded data
n_mrcat = Node(
interface=mrcatfunc.MRCat(
#axis=3,
out_file='b0s.mif'),
name='n_mrcat')
# input DTI images (all b0 volumes; reverse phase encoded) for concatenating
wf.connect([(n_selectfiles, n_mrcat, [('DTI_B0_PA', 'in_file1')])])
# input b0 volumes from NODDI data for concatenating
wf.connect([(n_dwiextract, n_mrcat, [('out_file', 'in_file2')])])
# output the mrcat file into 'noddi_and_PA_b0s.mif'
wf.connect([(n_mrcat, n_datasink, [('out_file', 'noddi_and_PA_b0s.mif')])])
# DWIfslpreproc for image pre-processing using FSL's eddy tool
n_dwifslpreproc = Node(interface=preprocfunc.DWIFslPreProc(
out_file='preprocessedDWIs.mif', use_header=True),
name='n_dwifslpreproc')
# output of degibbs as input for preprocessing
wf.connect([(n_degibbs, n_dwifslpreproc, [('out_file', 'in_file')])])
# output of mrcat (extracted b0 volumes) as se_epi input
wf.connect([(n_mrcat, n_dwifslpreproc, [('out_file', 'se_epi_file')])])
# output of dwifslpreproc into 'preprocessedDWIs.mif'
wf.connect([(n_dwifslpreproc, n_datasink, [('out_file',
'preprocessedDWIs.mif')])])
# DWI bias correct for B1 field inhomogeneity correction
n_dwibiascorrect = Node(
interface=preprocess.DWIBiasCorrect(use_ants=True),
name='n_dwibiascorrect',
)
# input preprocessed data
wf.connect([(n_dwifslpreproc, n_dwibiascorrect, [('out_file', 'in_file')])
])
# output biascorrect data into 'ANTSpreprocessedDWIs.mif'
wf.connect([(n_dwibiascorrect, n_datasink,
[('out_file', 'ANTSpreprocessedDWIs.mif')])])
# DWI2mask to compute whole brain mask from bias corrected data
n_dwi2mask = Node(interface=mrt.BrainMask(out_file='mask.mif'),
name='n_dwi2mask')
wf.connect([(n_dwibiascorrect, n_dwi2mask, [('out_file', 'in_file')])])
wf.connect([(n_dwi2mask, n_datasink, [('out_file', 'mask.mif')])])
##################################################################################
## 2) Fixel-based analysis
# DWI2response for etimation of response function for spherical deconvolution
n_dwi2response = Node(interface=mrt.ResponseSD(algorithm='dhollander',
wm_file='wm_res.txt',
gm_file='gm_res.txt',
csf_file='csf_res.txt'),
name='n_dwi2response')
# input bias corrected data for response function estimation
wf.connect([(n_dwibiascorrect, n_dwi2response, [('out_file', 'in_file')])])
# output WM, GM, CSF response text files
wf.connect([(n_dwi2response, n_datasink, [('wm_file', 'wm_res.txt')])])
wf.connect([(n_dwi2response, n_datasink, [('gm_file', 'gm_res.txt')])])
wf.connect([(n_dwi2response, n_datasink, [('csf_file', 'csf_res.txt')])])
# DWI2fod for fibre orientation distribution estimation (FOD)
n_dwi2fod = Node(interface=mrt.ConstrainedSphericalDeconvolution(
algorithm='msmt_csd',
wm_odf='wmfod.mif',
gm_odf='gmfod.mif',
csf_odf='csffod.mif'),
name='n_dwi2fod')
# utilise dwi2fod response files as input
wf.connect([(n_dwibiascorrect, n_dwi2fod, [('out_file', 'in_file')])])
wf.connect([(n_dwi2response, n_dwi2fod, [('wm_file', 'wm_txt')])])
wf.connect([(n_dwi2response, n_dwi2fod, [('gm_file', 'gm_txt')])])
wf.connect([(n_dwi2response, n_dwi2fod, [('csf_file', 'csf_txt')])])
# output WM, GM and CSF FODs for saving
wf.connect([(n_dwi2fod, n_datasink, [('wm_odf', 'wmfod.mif')])])
wf.connect([(n_dwi2fod, n_datasink, [('gm_odf', 'gmfod.mif')])])
wf.connect([(n_dwi2fod, n_datasink, [('csf_odf', 'csffod.mif')])])
# Mrconvert to select the first volume of the WM file (is best image out of 45 slices of wmfod file)
n_mrconvert_fod = Node(interface=utils.MRConvert(out_file='Zwmfod.mif',
coord=[3, 0]),
name='n_mrconvert_fod')
# utilise WM FOD as input
wf.connect([(n_dwi2fod, n_mrconvert_fod, [('wm_odf', 'in_file')])])
# output z component of WM FOD
wf.connect([(n_mrconvert_fod, n_datasink, [('out_file', 'Zwmfod.mif')])])
# MRcat to concatenate all WM, GM, CSF FOD files to see their distributions throughout Brain
n_mrcat_fod = Node(interface=mrcatfunc.MRCat(out_file='vf.mif'),
name='n_mrcat_fod')
# connect Zwmfod, gmfod and csffod as inputs
wf.connect([(n_mrconvert_fod, n_mrcat_fod, [('out_file', 'in_file1')])])
wf.connect([(n_dwi2fod, n_mrcat_fod, [('gm_odf', 'in_file2')])])
wf.connect([(n_dwi2fod, n_mrcat_fod, [('csf_odf', 'in_file3')])])
# output the mrcat file into file 'vf.mif'
wf.connect([(n_mrcat_fod, n_datasink, [('out_file', 'vf.mif')])])
# fod2fixel
# Perform segmentation of continuous FODs to produce discrete fixels
# OUTPUTS: -afd afd.mif -peak peak.mif -disp disp.mif
n_fod2fixel = Node(
interface=fod2fixelfunc.fod2fixel(
out_file='wmfixels',
#afd_file = 'afd.mif',
peak_file='peak.mif',
disp_file='disp.mif'),
name='n_fod2fixel')
# let the peak value parameter be trialed as multiple values
n_fod2fixel.iterables = ('fmls_peak_value', [0, 0.10, 0.50])
n_fod2fixel.iterables = ('fmls_integral', [0, 0.10, 0.50])
# obtain WM fibre image as input
wf.connect([(n_dwi2fod, n_fod2fixel, [('wm_odf', 'in_file')])])
# ouputs of fod2fixel saved
wf.connect([(n_fod2fixel, n_datasink, [('out_file', 'wmfixels')])])
wf.connect([(n_fod2fixel, n_datasink, [('afd_file', 'afd.mif')])])
wf.connect([(n_fod2fixel, n_datasink, [('peak_file', 'peak.mif')])])
wf.connect([(n_fod2fixel, n_datasink, [('disp_file', 'disp.mif')])])
# fixel2peaks to convert data in the fixel directory format into 4D image of 3-vectors
n_fixel2peaks = Node(interface=fixel2peaksfunc.fixel2peaks(
out_file='peaks_wmdirections.mif'),
name='n_fixel2peaks')
# look at multiple values for maximum number of fixels in each voxel
n_fixel2peaks.iterables = ('number', [1, 2, 3])
# obtain directions file in output folder of fod2fixel, as input
wf.connect([(n_fod2fixel, n_fixel2peaks, [('out_file', 'in_file')])])
# ouput of fixel2peaks saved in peaks_wmdirections.mif'
wf.connect([(n_fixel2peaks, n_datasink, [('out_file',
'peaks_wmdirections.mif')])])
# MRmath to find normalised value of peak WM directions
n_mrmath = Node(interface=mrt.MRMath(
axis=3, operation='norm', out_file='norm_peaks_wmdirections.mif'),
name='n_mrmath')
# input peak fixel data
wf.connect([(n_fixel2peaks, n_mrmath, [('out_file', 'in_file')])])
# output saved into 'norm_peaks_wmdirections.mif'
wf.connect([(n_mrmath, n_datasink, [('out_file',
'norm_peaks_wmdirections.mif')])])
# MRcalc to divide peak WM direction by normalised value
n_mrcalc = Node(interface=mrcalcfunc.MRCalc(operation='divide',
out_file='wm_peak_dir.mif'),
name='n_mrcalc')
# fixel2peaks image as input 1
wf.connect([(n_fixel2peaks, n_mrcalc, [('out_file', 'in_file1')])])
# normalised fixel2peak image as input 2
wf.connect([(n_mrmath, n_mrcalc, [('out_file', 'in_file2')])])
# save output image as 'WM_peak_dir.mif'
wf.connect([(n_mrcalc, n_datasink, [('out_file', 'WM_peak_dir.mif')])])
# MRconvert to extract Z component of peak directions
n_mrconvert2 = Node(interface=utils.MRConvert(
out_file='Zpeak_WM_Directions.mif', coord=[3, 2]),
name='n_mrconvert2')
# input normalised peak direction file
wf.connect([(n_mrcalc, n_mrconvert2, [('out_file', 'in_file')])])
# save ouptut as 'Zpeak_WM_Directions.mif'
wf.connect([(n_mrconvert2, n_datasink, [('out_file',
'Zpeak_WM_Directions.mif')])])
# MRcalc to find absolute value of peak fibre directions
n_mrcalc2 = Node(interface=mrcalcfunc.MRCalc(
operation='abs', out_file='absZpeak_WM_Directions.mif'),
name='n_mrcalc2')
# input z peaks image
wf.connect([(n_mrconvert2, n_mrcalc2, [('out_file', 'in_file1')])])
# save output as 'absZpeak_WM_Directions.mif'
wf.connect([(n_mrcalc2, n_datasink, [('out_file',
'absZpeak_WM_Directions.mif')])])
# MRcalc to get angle by doing inverse cosine
n_mrcalc3 = Node(interface=mrcalcfunc.MRCalc(
operation='acos', out_file='acosZpeak_WM_Directions.mif'),
name='n_mrcalc3')
# input normalised z component of peaks image
wf.connect([(n_mrcalc2, n_mrcalc3, [('out_file', 'in_file1')])])
# save ouput as 'acosZpeak_WM_Directions.mif'
wf.connect([(n_mrcalc3, n_datasink, [('out_file',
'acosZpeak_WM_Directions.mif')])])
# MRcalc to convert angle of peak fibre (w.r.t z axis), to degrees
n_mrcalc4 = Node(interface=mrcalcfunc.MRCalc(
operation='multiply', operand=180, out_file='Fixel1_Z_angle.mif'),
name='n_mrcalc4')
# input inverse cosine image of peak fibre
wf.connect([(n_mrcalc3, n_mrcalc4, [('out_file', 'in_file1')])])
# output image as 'Fixel1_Z_angle.mif'
wf.connect([(n_mrcalc4, n_datasink, [('out_file', 'Fixel1_Z_angle.mif')])])
# MRcalc to divide by pi to finish converting from radians to degrees
n_mrcalc5 = Node(interface=mrcalcfunc.MRCalc(
operation='divide',
operand=3.14159265,
out_file='Fixel1_Z_cos_deg.mif'),
name='n_mrcalc5')
# input image multiplied by 180
wf.connect([(n_mrcalc4, n_mrcalc5, [('out_file', 'in_file1')])])
# save output as 'Fixel1_Z_cos_deg.mif'
wf.connect([(n_mrcalc5, n_datasink, [('out_file', 'Fixel1_Z_cos_deg.mif')])
])
##################################################################################
## 3) Tensor-based analysis
# dwi2tensor to compute tensor from biascorrected DWI image
n_dwi2tensor = Node(interface=mrt.FitTensor(out_file='dti.mif'),
name='n_dwi2tensor')
# input bias corrected image
wf.connect([(n_dwibiascorrect, n_dwi2tensor, [('out_file', 'in_file')])])
# utilise mask to only compute tensors for regions of Brain
wf.connect([(n_dwi2mask, n_dwi2tensor, [('out_file', 'in_mask')])])
# output data into 'dt.mif'
wf.connect([(n_dwi2tensor, n_datasink, [('out_file', 'dt.mif')])])
# tensor2metric to convert tensors to generate maps of tensor-derived parameters
n_tensor2metric = Node(interface=tensor2metricfunc.tensor2metric(
modulate='none', num=1, vector_file='eigenvector.mif'),
name='n_tensor2metric')
# input tensor image
wf.connect([(n_dwi2tensor, n_tensor2metric, [('out_file', 'input_file')])])
# save output eigenvectors of the diffusion tensor
wf.connect([(n_tensor2metric, n_datasink, [('vector_file',
'eigenvector.mif')])])
# MRconvert to get eigenvector w.r.t z direction (main field)
n_mrconvert3 = Node(interface=utils.MRConvert(coord=[3, 2],
out_file='eigenvectorZ.mif'),
name='n_mrconvert3')
# input eigenvector file from tensor2metric
wf.connect([(n_tensor2metric, n_mrconvert3, [('vector_file', 'in_file')])])
# save output as 'eigenvectorZ.mif'
wf.connect([(n_mrconvert3, n_datasink, [('out_file', 'eigenvectorZ.mif')])
])
# ALL SUBSEQUENT STEPS GET ANGLE IN DEGREES
# MRcalc to find absolute value of z eigenvector file
n_mrcalc6 = Node(interface=mrcalcfunc.MRCalc(
operation='abs', out_file='abs_eigenvectorZ.mif'),
name='n_mrcalc6')
# z eigenvector image as input
wf.connect([(n_mrconvert3, n_mrcalc6, [('out_file', 'in_file1')])])
# save output as 'abs_eigenvectorZ.mif'
wf.connect([(n_mrcalc6, n_datasink, [('out_file', 'abs_eigenvectorZ.mif')])
])
# MRcalc to get angle by doing inverse cosine
n_mrcalc7 = Node(interface=mrcalcfunc.MRCalc(
operation='acos', out_file='acos_eigenvectorZ.mif'),
name='n_mrcalc7')
# input absolute value of z eigenvector image
wf.connect([(n_mrcalc6, n_mrcalc7, [('out_file', 'in_file1')])])
# save output as 'acos_eigenvectorZ.mif'
wf.connect([(n_mrcalc7, n_datasink, [('out_file', 'acos_eigenvectorZ.mif')
])])
# MRcalc to convert angle to degrees
n_mrcalc8 = Node(
interface=mrcalcfunc.MRCalc(operation='multiply',
operand=180,
out_file='degrees_eigenvectorZ.mif'),
name='n_mrcalc8')
# input inverse cosine image of z eigenvector
wf.connect([(n_mrcalc7, n_mrcalc8, [('out_file', 'in_file1')])])
# save output as 'degrees_eigenvectorZ.mif'
wf.connect([(n_mrcalc8, n_datasink, [('out_file',
'degrees_eigenvectorZ.mif')])])
# MRcalc to divide by pi to finish converting from radians to degrees
n_mrcalc9 = Node(interface=mrcalcfunc.MRCalc(operation='divide',
operand=3.14159265,
out_file='dti_z_cos_deg.mif'),
name='n_mrcalc9')
# input z eigenvector image multiplied by 180
wf.connect([(n_mrcalc8, n_mrcalc9, [('out_file', 'in_file1')])])
# save output as 'dti_z_cos_deg.mif'
wf.connect([(n_mrcalc9, n_datasink, [('out_file', 'dti_z_cos_deg.mif')])])
# MRcalc to give difference image between fixel based and tensor based outputs
n_mrcalc10 = Node(interface=mrcalcfunc.MRCalc(
operation='subtract', out_file='diff_imag_tensor_minus_fixel.mif'),
name='n_mrcalc10')
# input tensor based image of whole Brain
wf.connect([(n_mrcalc9, n_mrcalc10, [('out_file', 'in_file1')])])
# input fixel based image of Brain
wf.connect([(n_mrcalc5, n_mrcalc10, [('out_file', 'in_file2')])])
# output difference image as 'diff_imag_tensor_minus_fixel.mif'
wf.connect([(n_mrcalc10, n_datasink,
[('out_file', 'diff_imag_tensor_minus_fixel.mif')])])
#####################################################################################
## 4) Tensor based analysis on WM fibres only (NOT WHOLE BRAIN TENSORS)
# MRthreshold to create WM mask from WM FOD (created earlier)
n_mrthreshold = Node(interface=mrthresholdfunc.MRThreshold(
out_file='thresholded_wmfod.mif'),
name='n_mrthreshold')
# input WM FOD
wf.connect([(n_dwi2fod, n_mrthreshold, [('wm_odf', 'in_file')])])
# output thresholded WM FOD
wf.connect([(n_mrthreshold, n_datasink, [('out_file',
'thresholded_wmfod.mif')])])
# MRconvert to extract 1st volume of thresholded WM FOD
n_mrconvert4 = Node(interface=utils.MRConvert(coord=[3, 0],
out_file='WMmask.mif'),
name='n_mrconvert4')
# input thresholded wmfod
wf.connect([(n_mrthreshold, n_mrconvert4, [('out_file', 'in_file')])])
# save output as 'WMmask.mif'
wf.connect([(n_mrconvert4, n_datasink, [('out_file', 'WMmask.mif')])])
# MRcalc to multiple WM mask with dti image to get tensors only of WM regions
n_mrcalc11 = Node(interface=mrcalcfunc.MRCalc(operation='multiply',
out_file='WM_dt.mif'),
name='n_mrcalc11')
# WM mask as input 1
wf.connect([(n_mrconvert4, n_mrcalc11, [('out_file', 'in_file1')])])
# dti image as input 2
wf.connect([(n_dwi2tensor, n_mrcalc11, [('out_file', 'in_file2')])])
# save output as 'WM_dt.mif'
wf.connect([(n_mrcalc11, n_datasink, [('out_file', 'WM_dt.mif')])])
# tensor2metric to convert tensors to generate maps of tensor-derived parameters
n_tensor2metric2 = Node(interface=tensor2metricfunc.tensor2metric(
modulate='none', num=1, vector_file='WMeigenvector.mif'),
name='n_tensor2metric2')
# input tensor image
wf.connect([(n_mrcalc11, n_tensor2metric2, [('out_file', 'input_file')])])
# save output eigenvectors of the diffusion tensor
wf.connect([(n_tensor2metric2, n_datasink, [('vector_file',
'WMeigenvector.mif')])])
# MRconvert to get eigenvector w.r.t z direction (main field)
n_mrconvert5 = Node(interface=utils.MRConvert(
coord=[3, 2], out_file='WMeigenvectorZ.mif'),
name='n_mrconvert5')
# input eigenvector file from tensor2metric
wf.connect([(n_tensor2metric2, n_mrconvert5, [('vector_file', 'in_file')])
])
# save output as 'eigenvectorZ.mif'
wf.connect([(n_mrconvert5, n_datasink, [('out_file', 'WMeigenvectorZ.mif')
])])
# ALL SUBSEQUENT STEPS GET ANGLE IN DEGREES
# MRcalc to find absolute value of z eigenvector file
n_mrcalc12 = Node(interface=mrcalcfunc.MRCalc(
operation='abs', out_file='WM_abs_eigenvectorZ.mif'),
name='n_mrcalc12')
# z eigenvector image as input
wf.connect([(n_mrconvert5, n_mrcalc12, [('out_file', 'in_file1')])])
# save output as 'WM_abs_eigenvectorZ.mif'
wf.connect([(n_mrcalc12, n_datasink, [('out_file',
'WM_abs_eigenvectorZ.mif')])])
# MRcalc to get angle by doing inverse cosine
n_mrcalc13 = Node(interface=mrcalcfunc.MRCalc(
operation='acos', out_file='acos_WMeigenvectorZ.mif'),
name='n_mrcalc13')
# input absolute value of z eigenvector image
wf.connect([(n_mrcalc12, n_mrcalc13, [('out_file', 'in_file1')])])
# save output as 'acos_WMeigenvectorZ.mif'
wf.connect([(n_mrcalc13, n_datasink, [('out_file',
'acos_WMeigenvectorZ.mif')])])
# MRcalc to convert angle to degrees
n_mrcalc14 = Node(interface=mrcalcfunc.MRCalc(
operation='multiply',
operand=180,
out_file='degrees_WMeigenvectorZ.mif'),
name='n_mrcalc14')
# input inverse cosine image of WM z eigenvector
wf.connect([(n_mrcalc13, n_mrcalc14, [('out_file', 'in_file1')])])
# save output as 'degrees_WMeigenvectorZ.mif'
wf.connect([(n_mrcalc14, n_datasink, [('out_file',
'degrees_WMeigenvectorZ.mif')])])
# MRcalc to divide by pi to finish converting from radians to degrees
n_mrcalc15 = Node(
interface=mrcalcfunc.MRCalc(operation='divide',
operand=3.14159265,
out_file='WMdti_z_cos_deg.mif'),
name='n_mrcalc15')
# input WM z eigenvector image multiplied by 180
wf.connect([(n_mrcalc14, n_mrcalc15, [('out_file', 'in_file1')])])
# save output as 'WMdti_z_cos_deg.mif'
wf.connect([(n_mrcalc15, n_datasink, [('out_file', 'WMdti_z_cos_deg.mif')])
])
# MRcalc to give difference image between fixel based and WM tensor based outputs
n_mrcalc16 = Node(interface=mrcalcfunc.MRCalc(
operation='subtract', out_file='diffImage_WMtensor_minus_fixel.mif'),
name='n_mrcalc16')
# input fixel image of Brain
wf.connect([(n_mrcalc15, n_mrcalc16, [('out_file', 'in_file1')])])
# input tensor image of WM fibres of Brain
wf.connect([(n_mrcalc5, n_mrcalc16, [('out_file', 'in_file2')])])
# output difference image as 'diff_imag_WMtensor_minus_fixel.mif'
wf.connect([(n_mrcalc16, n_datasink,
[('out_file', 'diffImage_WMtensor_minus_fixel.mif')])])
######################################################################################
return wf