1515
1616import os .path as op
1717
18- from ..base import traits , TraitedSpec , File
18+ from ..base import traits , TraitedSpec , File , Undefined
1919from .base import MRTrix3BaseInputSpec , MRTrix3Base
2020
2121
@@ -74,108 +74,55 @@ def _list_outputs(self):
7474
7575
7676class EstimateFODInputSpec (MRTrix3BaseInputSpec ):
77- in_file = File (exists = True , argstr = '%s' , mandatory = True , position = - 3 ,
78- desc = 'input diffusion weighted images' )
79- response = File (
80- exists = True , argstr = '%s' , mandatory = True , position = - 2 ,
81- desc = ('a text file containing the diffusion-weighted signal response '
82- 'function coefficients for a single fibre population' ))
83- out_file = File (
84- 'fods.mif' , argstr = '%s' , mandatory = True , position = - 1 ,
85- usedefault = True , desc = ('the output spherical harmonics coefficients'
86- ' image' ))
77+ algorithm = traits .Enum ('csd' ,'msmt_csd' , argstr = '%s' , position = - 8 ,
78+ mandatory = True , desc = 'FOD algorithm' )
79+ dwi_file = File (exists = True , argstr = '%s' , position = - 7 ,
80+ mandatory = True , desc = 'input DWI image' )
81+ wm_txt = File (argstr = '%s' , position = - 6 ,
82+ mandatory = True , desc = 'WM response text file' )
83+ wm_odf = File ('wm.mif' , argstr = '%s' , position = - 5 , usedefault = True ,
84+ mandatory = True , desc = 'output WM ODF' )
85+ gm_txt = File (argstr = '%s' , position = - 4 , desc = 'GM response text file' )
86+ gm_odf = File ('gm.mif' , argstr = '%s' , position = - 3 , desc = 'output GM ODF' )
87+ csf_txt = File (argstr = '%s' , position = - 2 , desc = 'CSF response text file' )
88+ csf_odf = File ('csf.mif' , argstr = '%s' , position = - 1 , desc = 'output CSF ODF' )
89+ mask_file = File (exists = True , argstr = '-mask %s' , desc = 'mask image' )
8790
8891 # DW Shell selection options
8992 shell = traits .List (traits .Float , sep = ',' , argstr = '-shell %s' ,
9093 desc = 'specify one or more dw gradient shells' )
91-
92- # Spherical deconvolution options
9394 max_sh = traits .Int (8 , argstr = '-lmax %d' ,
9495 desc = 'maximum harmonic degree of response function' )
95- in_mask = File (exists = True , argstr = '-mask %s' ,
96- desc = 'provide initial mask image' )
9796 in_dirs = File (
9897 exists = True , argstr = '-directions %s' ,
9998 desc = ('specify the directions over which to apply the non-negativity '
10099 'constraint (by default, the built-in 300 direction set is '
101100 'used). These should be supplied as a text file containing the '
102101 '[ az el ] pairs for the directions.' ))
103- sh_filter = File (
104- exists = True , argstr = '-filter %s' ,
105- desc = ('the linear frequency filtering parameters used for the initial '
106- 'linear spherical deconvolution step (default = [ 1 1 1 0 0 ]). '
107- 'These should be supplied as a text file containing the '
108- 'filtering coefficients for each even harmonic order.' ))
109-
110- neg_lambda = traits .Float (
111- 1.0 , argstr = '-neg_lambda %f' ,
112- desc = ('the regularisation parameter lambda that controls the strength'
113- ' of the non-negativity constraint' ))
114- thres = traits .Float (
115- 0.0 , argstr = '-threshold %f' ,
116- desc = ('the threshold below which the amplitude of the FOD is assumed '
117- 'to be zero, expressed as an absolute amplitude' ))
118-
119- n_iter = traits .Int (
120- 50 , argstr = '-niter %d' , desc = ('the maximum number of iterations '
121- 'to perform for each voxel' ))
122102
123103
124104class EstimateFODOutputSpec (TraitedSpec ):
125- out_file = File (exists = True , desc = 'the output response file' )
105+ wm_odf = File (argstr = '%s' , desc = 'output WM ODF' )
106+ gm_odf = File (argstr = '%s' , desc = 'output GM ODF' )
107+ csf_odf = File (argstr = '%s' , desc = 'output CSF ODF' )
126108
127109
128110class EstimateFOD (MRTrix3Base ):
129111
130112 """
131- Convert diffusion-weighted images to tensor images
132-
133- Note that this program makes use of implied symmetries in the diffusion
134- profile. First, the fact the signal attenuation profile is real implies
135- that it has conjugate symmetry, i.e. Y(l,-m) = Y(l,m)* (where * denotes
136- the complex conjugate). Second, the diffusion profile should be
137- antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l
138- components should be zero. Therefore, this program only computes the even
139- elements.
140-
141- Note that the spherical harmonics equations used here differ slightly from
142- those conventionally used, in that the (-1)^m factor has been omitted.
143- This should be taken into account in all subsequent calculations.
144- The spherical harmonic coefficients are stored as follows. First, since
145- the signal attenuation profile is real, it has conjugate symmetry, i.e.
146- Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the
147- diffusion profile should be antipodally symmetric (i.e. S(x) = S(-x)),
148- implying that all odd l components should be zero. Therefore, only the
149- even elements are computed.
150-
151- Note that the spherical harmonics equations used here differ slightly from
152- those conventionally used, in that the (-1)^m factor has been omitted.
153- This should be taken into account in all subsequent calculations.
154- Each volume in the output image corresponds to a different spherical
155- harmonic component. Each volume will correspond to the following:
156-
157- volume 0: l = 0, m = 0
158- volume 1: l = 2, m = -2 (imaginary part of m=2 SH)
159- volume 2: l = 2, m = -1 (imaginary part of m=1 SH)
160- volume 3: l = 2, m = 0
161- volume 4: l = 2, m = 1 (real part of m=1 SH)
162- volume 5: l = 2, m = 2 (real part of m=2 SH)
163- etc...
164-
165-
113+ Estimate fibre orientation distributions from diffusion data using spherical deconvolution
166114
167115 Example
168116 -------
169117
170118 >>> import nipype.interfaces.mrtrix3 as mrt
171119 >>> fod = mrt.EstimateFOD()
172- >>> fod.inputs.in_file = 'dwi.mif '
173- >>> fod.inputs.response = 'response.txt '
174- >>> fod.inputs.in_mask = 'mask.nii.gz '
120+ >>> fod.inputs.algorithm = 'csd '
121+ >>> fod.inputs.dwi_file = 'dwi.mif '
122+ >>> fod.inputs.wm_txt = 'wm.txt '
175123 >>> fod.inputs.grad_fsl = ('bvecs', 'bvals')
176124 >>> fod.cmdline # doctest: +ELLIPSIS
177- 'dwi2fod -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif response.txt\
178-
125+ 'dwi2fod -fslgrad bvecs bvals csd dwi.mif wm.txt wm.mif'
179126 >>> fod.run() # doctest: +SKIP
180127 """
181128
@@ -185,5 +132,12 @@ class EstimateFOD(MRTrix3Base):
185132
186133 def _list_outputs (self ):
187134 outputs = self .output_spec ().get ()
188- outputs ['out_file' ] = op .abspath (self .inputs .out_file )
135+ outputs ['wm_odf' ] = op .abspath (self .inputs .wm_odf )
136+ if self .inputs .gm_odf != Undefined :
137+ outputs ['gm_odf' ] = op .abspath (self .inputs .gm_odf )
138+ if self .inputs .csf_odf != Undefined :
139+ outputs ['csf_odf' ] = op .abspath (self .inputs .csf_odf )
189140 return outputs
141+
142+
143+
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