interfaces.mrtrix3.reconst¶
EstimateFOD¶
Wraps command dwi2fod
Convert diffusion-weighted images to tensor images
Note that this program makes use of implied symmetries in the diffusion profile. First, the fact the signal attenuation profile is real implies that it has conjugate symmetry, i.e. Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the diffusion profile should be antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l components should be zero. Therefore, this program only computes the even elements.
Note that the spherical harmonics equations used here differ slightly from those conventionally used, in that the (-1)^m factor has been omitted. This should be taken into account in all subsequent calculations. The spherical harmonic coefficients are stored as follows. First, since the signal attenuation profile is real, it has conjugate symmetry, i.e. Y(l,-m) = Y(l,m)* (where * denotes the complex conjugate). Second, the diffusion profile should be antipodally symmetric (i.e. S(x) = S(-x)), implying that all odd l components should be zero. Therefore, only the even elements are computed.
Note that the spherical harmonics equations used here differ slightly from those conventionally used, in that the (-1)^m factor has been omitted. This should be taken into account in all subsequent calculations. Each volume in the output image corresponds to a different spherical harmonic component. Each volume will correspond to the following:
volume 0: l = 0, m = 0 volume 1: l = 2, m = -2 (imaginary part of m=2 SH) volume 2: l = 2, m = -1 (imaginary part of m=1 SH) volume 3: l = 2, m = 0 volume 4: l = 2, m = 1 (real part of m=1 SH) volume 5: l = 2, m = 2 (real part of m=2 SH) etc...
Example¶
>>> import nipype.interfaces.mrtrix3 as mrt
>>> fod = mrt.EstimateFOD()
>>> fod.inputs.in_file = 'dwi.mif'
>>> fod.inputs.response = 'response.txt'
>>> fod.inputs.in_mask = 'mask.nii.gz'
>>> fod.inputs.grad_fsl = ('bvecs', 'bvals')
>>> fod.cmdline
'dwi2fod -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif response.txt fods.mif'
>>> fod.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input diffusion weighted images
flag: %s, position: -3
out_file: (a file name, nipype default value: fods.mif)
the output spherical harmonics coefficients image
flag: %s, position: -1
response: (an existing file name)
a text file containing the diffusion-weighted signal response
function coefficients for a single fibre population
flag: %s, position: -2
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
bval_scale: ('yes' or 'no')
specifies whether the b - values should be scaled by the square of
the corresponding DW gradient norm, as often required for multishell
or DSI DW acquisition schemes. The default action can also be set in
the MRtrix config file, under the BValueScaling entry. Valid choices
are yes / no, true / false, 0 / 1 (default: true).
flag: -bvalue_scaling %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
grad_file: (an existing file name)
dw gradient scheme (MRTrix format
flag: -grad %s
grad_fsl: (a tuple of the form: (an existing file name, an existing
file name))
(bvecs, bvals) dw gradient scheme (FSL format
flag: -fslgrad %s %s
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_bval: (an existing file name)
bvals file in FSL format
in_bvec: (an existing file name)
bvecs file in FSL format
flag: -fslgrad %s %s
in_dirs: (an existing file name)
specify the directions over which to apply the non-negativity
constraint (by default, the built-in 300 direction set is used).
These should be supplied as a text file containing the [ az el ]
pairs for the directions.
flag: -directions %s
in_mask: (an existing file name)
provide initial mask image
flag: -mask %s
max_sh: (an integer (int or long))
maximum harmonic degree of response function
flag: -lmax %d
n_iter: (an integer (int or long))
the maximum number of iterations to perform for each voxel
flag: -niter %d
neg_lambda: (a float)
the regularisation parameter lambda that controls the strength of
the non-negativity constraint
flag: -neg_lambda %f
nthreads: (an integer (int or long))
number of threads. if zero, the number of available cpus will be
used
flag: -nthreads %d
sh_filter: (an existing file name)
the linear frequency filtering parameters used for the initial
linear spherical deconvolution step (default = [ 1 1 1 0 0 ]). These
should be supplied as a text file containing the filtering
coefficients for each even harmonic order.
flag: -filter %s
shell: (a list of items which are a float)
specify one or more dw gradient shells
flag: -shell %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
thres: (a float)
the threshold below which the amplitude of the FOD is assumed to be
zero, expressed as an absolute amplitude
flag: -threshold %f
Outputs:
out_file: (an existing file name)
the output response file
FitTensor¶
Wraps command dwi2tensor
Convert diffusion-weighted images to tensor images
Example¶
>>> import nipype.interfaces.mrtrix3 as mrt
>>> tsr = mrt.FitTensor()
>>> tsr.inputs.in_file = 'dwi.mif'
>>> tsr.inputs.in_mask = 'mask.nii.gz'
>>> tsr.inputs.grad_fsl = ('bvecs', 'bvals')
>>> tsr.cmdline
'dwi2tensor -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif dti.mif'
>>> tsr.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input diffusion weighted images
flag: %s, position: -2
out_file: (a file name, nipype default value: dti.mif)
the output diffusion tensor image
flag: %s, position: -1
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
bval_scale: ('yes' or 'no')
specifies whether the b - values should be scaled by the square of
the corresponding DW gradient norm, as often required for multishell
or DSI DW acquisition schemes. The default action can also be set in
the MRtrix config file, under the BValueScaling entry. Valid choices
are yes / no, true / false, 0 / 1 (default: true).
flag: -bvalue_scaling %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
grad_file: (an existing file name)
dw gradient scheme (MRTrix format
flag: -grad %s
grad_fsl: (a tuple of the form: (an existing file name, an existing
file name))
(bvecs, bvals) dw gradient scheme (FSL format
flag: -fslgrad %s %s
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_bval: (an existing file name)
bvals file in FSL format
in_bvec: (an existing file name)
bvecs file in FSL format
flag: -fslgrad %s %s
in_mask: (an existing file name)
only perform computation within the specified binary brain mask
image
flag: -mask %s
method: ('nonlinear' or 'loglinear' or 'sech' or 'rician')
select method used to perform the fitting
flag: -method %s
nthreads: (an integer (int or long))
number of threads. if zero, the number of available cpus will be
used
flag: -nthreads %d
reg_term: (a float)
specify the strength of the regularisation term on the magnitude of
the tensor elements (default = 5000). This only applies to the non-
linear methods
flag: -regularisation %f
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (an existing file name)
the output DTI file