create a brain mask from the nodif_brain
"""
bet = pe.Node(interface=fsl.BET(), name='bet')
bet.inputs.mask = True
bet.inputs.frac = 0.34
"""
correct the diffusion weighted images for eddy_currents
"""
eddycorrect = create_eddy_correct_pipeline('eddycorrect')
eddycorrect.inputs.inputnode.ref_num = 0
hardi_mat = pe.Node(interface=dtk.HARDIMat(), name='hardi_mat')
odf_recon = pe.Node(interface=dtk.ODFRecon(), name='odf_recon')
"""
connect all the nodes for this workflow
"""
compute_ODF.connect([(fslroi, bet, [('roi_file', 'in_file')]),
(eddycorrect, odf_recon, [('outputnode.eddy_corrected',
'DWI')]),
(eddycorrect, hardi_mat, [('outputnode.eddy_corrected',
'reference_file')]),
(hardi_mat, odf_recon, [('out_file', 'matrix')])])
"""
Setup for Tracktography
-----------------------
Here we will create a workflow to enable deterministic tracktography
"""
def create_dtk_recon_flow(config):
flow = pe.Workflow(name="reconstruction")
# inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=["diffusion","diffusion_resampled"]),name="inputnode")
outputnode = pe.Node(interface=util.IdentityInterface(fields=["DWI","B0","ODF","gFA","skewness","kurtosis","P0","max","V1"]),name="outputnode")
if config.imaging_model == "DSI":
prefix = "dsi"
dtk_odfrecon = pe.Node(interface=dtk.ODFRecon(out_prefix=prefix),name='dtk_odfrecon')
dtk_odfrecon.inputs.matrix = os.path.join(os.environ['DSI_PATH'],config.recon_matrix_file)
config.dsi_number_of_directions
config.number_of_output_directions
dtk_odfrecon.inputs.n_b0 = config.number_of_b0_volumes
dtk_odfrecon.inputs.n_directions = int(config.dsi_number_of_directions)+1
dtk_odfrecon.inputs.n_output_directions = config.number_of_output_directions
dtk_odfrecon.inputs.dsi = True
flow.connect([
(inputnode,dtk_odfrecon,[('diffusion_resampled','DWI')]),
(dtk_odfrecon,outputnode,[('DWI','DWI'),('B0','B0'),('ODF','ODF'),('max','max')])])
if config.imaging_model == "HARDI":
prefix = "hardi"
dtk_hardimat = pe.Node(interface=dtk.HARDIMat(),name='dtk_hardimat')
#dtk_hardimat.inputs.gradient_table = config.gradient_table
# Flip gradient table
flip_table = pe.Node(interface=flipTable(),name='flip_table')
flip_table.inputs.table = config.gradient_table
flip_table.inputs.flipping_axis = config.flip_table_axis
flip_table.inputs.delimiter = ','
flip_table.inputs.header_lines = 0
flip_table.inputs.orientation = 'v'
flow.connect([
(flip_table,dtk_hardimat,[("table","gradient_table")]),
])
dtk_hardimat.inputs.oblique_correction = config.apply_gradient_orientation_correction
dtk_odfrecon = pe.Node(interface=dtk.ODFRecon(out_prefix=prefix),name='dtk_odfrecon')
dtk_odfrecon.inputs.n_b0 = config.number_of_b0_volumes
dtk_odfrecon.inputs.n_directions = int(config.number_of_directions)+1
dtk_odfrecon.inputs.n_output_directions = config.number_of_output_directions
flow.connect([
(inputnode,dtk_hardimat,[('diffusion_resampled','reference_file')]),
(dtk_hardimat,dtk_odfrecon,[('out_file','matrix')]),
(inputnode,dtk_odfrecon,[('diffusion_resampled','DWI')]),
(dtk_odfrecon,outputnode,[('DWI','DWI'),('B0','B0'),('ODF','ODF'),('max','max')])])
if config.imaging_model == "DTI":
prefix = "dti"
flip_table = pe.Node(interface=flipTable(),name='flip_table')
flip_table.inputs.table = config.gradient_table
flip_table.inputs.flipping_axis = config.flip_table_axis
flip_table.inputs.delimiter = ','
flip_table.inputs.header_lines = 0
flip_table.inputs.orientation = 'v'
dtk_dtirecon = pe.Node(interface=dtk.DTIRecon(out_prefix=prefix),name='dtk_dtirecon')
dtk_dtirecon.inputs.b_value = config.maximum_b_value
dtk_dtirecon.inputs.multiple_b_values = config.multiple_high_b_values
dtk_dtirecon.inputs.n_averages = config.number_of_averages
dtk_dtirecon.inputs.number_of_b0 = config.number_of_b0_volumes
dtk_dtirecon.inputs.oblique_correction = config.apply_gradient_orientation_correction
flow.connect([
(inputnode,dtk_dtirecon,[('diffusion','DWI')]),
(flip_table, dtk_dtirecon,[('table', 'gradient_matrix')]),
(dtk_dtirecon,outputnode,[('DWI','DWI'),('B0','B0'),('V1','V1')])])
else:
if 'gFA' in config.compute_additional_maps:
dtb_gfa = pe.Node(interface=DTB_gfa(moment=2),name='dtb_gfa')
flow.connect([
(dtk_odfrecon,dtb_gfa,[(('ODF',strip_suffix,prefix),'dsi_basepath')]),
(dtb_gfa,outputnode,[('out_file','gFA')])])
if 'skewness' in config.compute_additional_maps:
dtb_skewness = pe.Node(interface=DTB_gfa(moment=3),name='dtb_skewness')
flow.connect([
(dtk_odfrecon,dtb_skewness,[(('ODF',strip_suffix,prefix),'dsi_basepath')]),
(dtb_skewness,outputnode,[('out_file','skewness')])])
if 'kurtosis' in config.compute_additional_maps:
dtb_kurtosis = pe.Node(interface=DTB_gfa(moment=4),name='dtb_kurtosis')
flow.connect([
(dtk_odfrecon,dtb_kurtosis,[(('ODF',strip_suffix,prefix),'dsi_basepath')]),
(dtb_kurtosis,outputnode,[('out_file','kurtosis')])])
if 'P0' in config.compute_additional_maps:
dtb_p0 = pe.Node(interface=DTB_P0(),name='dtb_P0')
flow.connect([
(inputnode,dtb_p0,[('diffusion','dwi_file')]),
(dtk_odfrecon,dtb_p0,[(('ODF',strip_suffix,prefix),'dsi_basepath')]),
(dtb_p0,outputnode,[('out_file','P0')])])
return flow