Wraps command csdeconv
Perform non-negativity constrained spherical deconvolution.
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. Each volume in the output image corresponds to a different spherical harmonic component, according to the following convention:
>>> import nipype.interfaces.mrtrix as mrt
>>> csdeconv = mrt.ConstrainedSphericalDeconvolution()
>>> csdeconv.inputs.in_file = 'dwi.mif'
>>> csdeconv.inputs.encoding_file = 'encoding.txt'
>>> csdeconv.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
diffusion-weighted image
response_file: (an existing file name)
the diffusion-weighted signal response function for a single fibre population (see
EstimateResponse)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
directions_file: (an existing file name)
a text file containing the [ el az ] pairs for the directions: Specify the directions
over which to apply the non-negativity constraint (by default, the built-in 300
direction set is used)
encoding_file: (an existing file name)
Gradient encoding, supplied as a 4xN text file with each line is in the format [ X Y Z b
], where [ X Y Z ] describe the direction of the applied gradient, and b gives the
b-value in units (1000 s/mm^2). See FSL2MRTrix
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
filter_file: (an existing file name)
a text file containing the filtering coefficients for each even harmonic order.the
linear frequency filtering parameters used for the initial linear spherical
deconvolution step (default = [ 1 1 1 0 0 ]).
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
iterations: (an integer)
the maximum number of iterations to perform for each voxel (default = 50)
lambda_value: (a float)
the regularisation parameter lambda that controls the strength of the constraint
(default = 1.0).
mask_image: (an existing file name)
only perform computation within the specified binary brain mask image
maximum_harmonic_order: (an integer)
set the maximum harmonic order for the output series. By default, the program will use
the highest possible lmax given the number of diffusion-weighted images.
normalise: (a boolean)
normalise the DW signal to the b=0 image
out_filename: (a file name)
Output filename
threshold_value: (a float)
the threshold below which the amplitude of the FOD is assumed to be zero, expressed as a
fraction of the mean value of the initial FOD (default = 0.1)
Outputs:
spherical_harmonics_image: (an existing file name)
Spherical harmonics image
Wraps command dwi2SH
Convert base diffusion-weighted images to their spherical harmonic representation.
This program outputs the spherical harmonic decomposition for the set measured signal attenuations. The signal attenuations are calculated by identifying the b-zero images from the diffusion encoding supplied (i.e. those with zero as the b-value), and dividing the remaining signals by the mean b-zero signal intensity. The spherical harmonic decomposition is then calculated by least-squares linear fitting. 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.
Each volume in the output image corresponds to a different spherical harmonic component, according to the following convention:
>>> import nipype.interfaces.mrtrix as mrt
>>> dwi2SH = mrt.DWI2SphericalHarmonicsImage()
>>> dwi2SH.inputs.in_file = 'diffusion.nii'
>>> dwi2SH.inputs.encoding_file = 'encoding.txt'
>>> dwi2SH.run()
Inputs:
[Mandatory]
encoding_file: (an existing file name)
Gradient encoding, supplied as a 4xN text file with each line is in the format [ X Y Z b
], where [ X Y Z ] describe the direction of the applied gradient, and b gives the
b-value in units (1000 s/mm^2). See FSL2MRTrix
in_file: (an existing file name)
Diffusion-weighted images
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
maximum_harmonic_order: (a float)
set the maximum harmonic order for the output series. By default, the program will use
the highest possible lmax given the number of diffusion-weighted images.
normalise: (a boolean)
normalise the DW signal to the b=0 image
out_filename: (a file name)
Output filename
Outputs:
spherical_harmonics_image: (an existing file name)
Spherical harmonics image
Wraps command estimate_response
Estimates the fibre response function for use in spherical deconvolution.
>>> import nipype.interfaces.mrtrix as mrt
>>> estresp = mrt.EstimateResponseForSH()
>>> estresp.inputs.in_file = 'dwi.mif'
>>> estresp.inputs.mask_image = 'dwi_WMProb.mif'
>>> estresp.inputs.encoding_file = 'encoding.txt'
>>> estresp.run()
Inputs:
[Mandatory]
encoding_file: (an existing file name)
Gradient encoding, supplied as a 4xN text file with each line is in the format [ X Y Z b
], where [ X Y Z ] describe the direction of the applied gradient, and b gives the
b-value in units (1000 s/mm^2). See FSL2MRTrix
in_file: (an existing file name)
Diffusion-weighted images
mask_image: (an existing file name)
only perform computation within the specified binary brain mask image
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
debug: (a boolean)
Display debugging messages.
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
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
maximum_harmonic_order: (an integer)
set the maximum harmonic order for the output series. By default, the program will use
the highest possible lmax given the number of diffusion-weighted images.
normalise: (a boolean)
normalise the DW signal to the b=0 image
out_filename: (a file name)
Output filename
quiet: (a boolean)
Do not display information messages or progress status.
Outputs:
response: (an existing file name)
Spherical harmonics image