interfaces.niftyfit.dwi¶
DwiTool¶
Wraps the executable command dwi_tool.
Interface for executable dwi_tool from Niftyfit platform.
Use DwiTool.
Diffusion-Weighted MR Prediction. Predicts DWI from previously fitted models and calculates model derived maps.
Examples¶
>>> from nipype.interfaces import niftyfit
>>> dwi_tool = niftyfit.DwiTool(dti_flag=True)
>>> dwi_tool.inputs.source_file = 'dwi.nii.gz'
>>> dwi_tool.inputs.bvec_file = 'bvecs'
>>> dwi_tool.inputs.bval_file = 'bvals'
>>> dwi_tool.inputs.mask_file = 'mask.nii.gz'
>>> dwi_tool.inputs.b0_file = 'b0.nii.gz'
>>> dwi_tool.inputs.rgbmap_file = 'rgb_map.nii.gz'
>>> dwi_tool.cmdline
'dwi_tool -source dwi.nii.gz -bval bvals -bvec bvecs -b0 b0.nii.gz -mask mask.nii.gz -dti -famap dwi_famap.nii.gz -logdti2 dwi_logdti2.nii.gz -mcmap dwi_mcmap.nii.gz -mdmap dwi_mdmap.nii.gz -rgbmap rgb_map.nii.gz -syn dwi_syn.nii.gz -v1map dwi_v1map.nii.gz'
Inputs:
[Mandatory]
source_file: (a file name)
The source image containing the fitted model.
argument: ``-source %s``, position: 1
bval_file: (a file name)
The file containing the bvalues of the source DWI.
argument: ``-bval %s``, position: 2
[Optional]
bvec_file: (a file name)
The file containing the bvectors of the source DWI.
argument: ``-bvec %s``, position: 3
b0_file: (a file name)
The B0 image corresponding to the source DWI
argument: ``-b0 %s``, position: 4
mask_file: (a file name)
The image mask
argument: ``-mask %s``, position: 5
mcmap_file: (a file name)
Filename of multi-compartment model parameter map (-ivim,-ball,-nod)
argument: ``-mcmap %s``
syn_file: (a file name)
Filename of synthetic image. Requires: bvec_file/b0_file.
argument: ``-syn %s``
requires: bvec_file, b0_file
mdmap_file: (a file name)
Filename of MD map/ADC
argument: ``-mdmap %s``
famap_file: (a file name)
Filename of FA map
argument: ``-famap %s``
v1map_file: (a file name)
Filename of PDD map [x,y,z]
argument: ``-v1map %s``
rgbmap_file: (a file name)
Filename of colour FA map.
argument: ``-rgbmap %s``
logdti_file: (a file name)
Filename of output logdti map.
argument: ``-logdti2 %s``
mono_flag: (a boolean)
Input is a single exponential to non-directional data [default with
no b-vectors]
argument: ``-mono``, position: 6
mutually_exclusive: ivim_flag, dti_flag, dti_flag2, ball_flag,
ballv_flag, nod_flag, nodv_flag
ivim_flag: (a boolean)
Inputs is an IVIM model to non-directional data.
argument: ``-ivim``, position: 6
mutually_exclusive: mono_flag, dti_flag, dti_flag2, ball_flag,
ballv_flag, nod_flag, nodv_flag
dti_flag: (a boolean)
Input is a tensor model diag/off-diag.
argument: ``-dti``, position: 6
mutually_exclusive: mono_flag, ivim_flag, dti_flag2, ball_flag,
ballv_flag, nod_flag, nodv_flag
dti_flag2: (a boolean)
Input is a tensor model lower triangular
argument: ``-dti2``, position: 6
mutually_exclusive: mono_flag, ivim_flag, dti_flag, ball_flag,
ballv_flag, nod_flag, nodv_flag
ball_flag: (a boolean)
Input is a ball and stick model.
argument: ``-ball``, position: 6
mutually_exclusive: mono_flag, ivim_flag, dti_flag, dti_flag2,
ballv_flag, nod_flag, nodv_flag
ballv_flag: (a boolean)
Input is a ball and stick model with optimised PDD.
argument: ``-ballv``, position: 6
mutually_exclusive: mono_flag, ivim_flag, dti_flag, dti_flag2,
ball_flag, nod_flag, nodv_flag
nod_flag: (a boolean)
Input is a NODDI model
argument: ``-nod``, position: 6
mutually_exclusive: mono_flag, ivim_flag, dti_flag, dti_flag2,
ball_flag, ballv_flag, nodv_flag
nodv_flag: (a boolean)
Input is a NODDI model with optimised PDD
argument: ``-nodv``, position: 6
mutually_exclusive: mono_flag, ivim_flag, dti_flag, dti_flag2,
ball_flag, ballv_flag, nod_flag
diso_val: (a float)
Isotropic diffusivity for -nod [3e-3]
argument: ``-diso %f``
dpr_val: (a float)
Parallel diffusivity for -nod [1.7e-3].
argument: ``-dpr %f``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
mcmap_file: (a file name)
Filename of multi-compartment model parameter map (-ivim,-ball,-nod)
syn_file: (a file name)
Filename of synthetic image
mdmap_file: (a file name)
Filename of MD map/ADC
famap_file: (a file name)
Filename of FA map
v1map_file: (a file name)
Filename of PDD map [x,y,z]
rgbmap_file: (a file name)
Filename of colour FA map
logdti_file: (a file name)
Filename of output logdti map
FitDwi¶
Wraps the executable command fit_dwi.
Interface for executable fit_dwi from Niftyfit platform.
Use NiftyFit to perform diffusion model fitting.
Diffusion-weighted MR Fitting. Fits DWI parameter maps to multi-shell, multi-directional data.
Examples¶
>>> from nipype.interfaces import niftyfit
>>> fit_dwi = niftyfit.FitDwi(dti_flag=True)
>>> fit_dwi.inputs.source_file = 'dwi.nii.gz'
>>> fit_dwi.inputs.bvec_file = 'bvecs'
>>> fit_dwi.inputs.bval_file = 'bvals'
>>> fit_dwi.inputs.rgbmap_file = 'rgb.nii.gz'
>>> fit_dwi.cmdline
'fit_dwi -source dwi.nii.gz -bval bvals -bvec bvecs -dti -error dwi_error.nii.gz -famap dwi_famap.nii.gz -mcout dwi_mcout.txt -mdmap dwi_mdmap.nii.gz -nodiff dwi_no_diff.nii.gz -res dwi_resmap.nii.gz -rgbmap rgb.nii.gz -syn dwi_syn.nii.gz -tenmap2 dwi_tenmap2.nii.gz -v1map dwi_v1map.nii.gz'
Inputs:
[Mandatory]
source_file: (a file name)
The source image containing the dwi data.
argument: ``-source %s``, position: 1
bval_file: (a file name)
The file containing the bvalues of the source DWI.
argument: ``-bval %s``, position: 2
bvec_file: (a file name)
The file containing the bvectors of the source DWI.
argument: ``-bvec %s``, position: 3
[Optional]
te_file: (a file name)
Filename of TEs (ms).
argument: ``-TE %s``
mutually_exclusive: te_file
te_value: (a file name)
Value of TEs (ms).
argument: ``-TE %s``
mutually_exclusive: te_file
mask_file: (a file name)
The image mask
argument: ``-mask %s``
prior_file: (a file name)
Filename of parameter priors for -ball and -nod.
argument: ``-prior %s``
rot_sform_flag: (an integer (int or long))
Rotate the output tensors according to the q/s form of the image
(resulting tensors will be in mm coordinates, default: 0).
argument: ``-rotsform %d``
error_file: (a file name)
Filename of parameter error maps.
argument: ``-error %s``
res_file: (a file name)
Filename of model residual map.
argument: ``-res %s``
syn_file: (a file name)
Filename of synthetic image.
argument: ``-syn %s``
nodiff_file: (a file name)
Filename of average no diffusion image.
argument: ``-nodiff %s``
mcmap_file: (a file name)
Filename of multi-compartment model parameter map (-ivim,-ball,-nod)
argument: ``-mcmap %s``
requires: nodv_flag
mdmap_file: (a file name)
Filename of MD map/ADC
argument: ``-mdmap %s``
famap_file: (a file name)
Filename of FA map
argument: ``-famap %s``
v1map_file: (a file name)
Filename of PDD map [x,y,z]
argument: ``-v1map %s``
rgbmap_file: (a file name)
Filename of colour-coded FA map
argument: ``-rgbmap %s``
requires: dti_flag
ten_type: ('lower-tri' or 'diag-off-diag', nipype default value:
lower-tri)
Use lower triangular (tenmap2) or diagonal, off-diagonal tensor
format
tenmap_file: (a file name)
Filename of tensor map [diag,offdiag].
argument: ``-tenmap %s``
requires: dti_flag
tenmap2_file: (a file name)
Filename of tensor map [lower tri]
argument: ``-tenmap2 %s``
requires: dti_flag
mono_flag: (a boolean)
Fit single exponential to non-directional data [default with no
b-vectors]
argument: ``-mono``, position: 4
mutually_exclusive: ivim_flag, dti_flag, ball_flag, ballv_flag,
nod_flag, nodv_flag
ivim_flag: (a boolean)
Fit IVIM model to non-directional data.
argument: ``-ivim``, position: 4
mutually_exclusive: mono_flag, dti_flag, ball_flag, ballv_flag,
nod_flag, nodv_flag
dti_flag: (a boolean)
Fit the tensor model [default with b-vectors].
argument: ``-dti``, position: 4
mutually_exclusive: mono_flag, ivim_flag, ball_flag, ballv_flag,
nod_flag, nodv_flag
ball_flag: (a boolean)
Fit the ball and stick model.
argument: ``-ball``, position: 4
mutually_exclusive: mono_flag, ivim_flag, dti_flag, ballv_flag,
nod_flag, nodv_flag
ballv_flag: (a boolean)
Fit the ball and stick model with optimised PDD.
argument: ``-ballv``, position: 4
mutually_exclusive: mono_flag, ivim_flag, dti_flag, ball_flag,
nod_flag, nodv_flag
nod_flag: (a boolean)
Fit the NODDI model
argument: ``-nod``, position: 4
mutually_exclusive: mono_flag, ivim_flag, dti_flag, ball_flag,
ballv_flag, nodv_flag
nodv_flag: (a boolean)
Fit the NODDI model with optimised PDD
argument: ``-nodv``, position: 4
mutually_exclusive: mono_flag, ivim_flag, dti_flag, ball_flag,
ballv_flag, nod_flag
maxit_val: (an integer (int or long))
Maximum number of non-linear LSQR iterations [100x2 passes])
argument: ``-maxit %d``
requires: gn_flag
lm_vals: (a tuple of the form: (a float, a float))
LM parameters (initial value, decrease rate) [100,1.2].
argument: ``-lm %f %f``
requires: gn_flag
gn_flag: (a boolean)
Use Gauss-Newton algorithm [Levenberg-Marquardt].
argument: ``-gn``
mutually_exclusive: wls_flag
vb_flag: (a boolean)
Use Variational Bayes fitting with known prior (currently identity
covariance...).
argument: ``-vb``
cov_file: (a file name)
Filename of ithe nc*nc covariance matrix [I]
argument: ``-cov %s``
wls_flag: (a boolean)
Use Variational Bayes fitting with known prior (currently identity
covariance...).
argument: ``-wls``
mutually_exclusive: gn_flag
swls_val: (a float)
Use location-weighted least squares for DTI fitting [3x3 Gaussian]
argument: ``-swls %f``
slice_no: (an integer (int or long))
Fit to single slice number.
argument: ``-slice %d``
voxel: (a tuple of the form: (an integer (int or long), an integer
(int or long), an integer (int or long)))
Fit to single voxel only.
argument: ``-voxel %d %d %d``
diso_val: (a float)
Isotropic diffusivity for -nod [3e-3]
argument: ``-diso %f``
dpr_val: (a float)
Parallel diffusivity for -nod [1.7e-3].
argument: ``-dpr %f``
wm_t2_val: (a float)
White matter T2 value [80ms].
argument: ``-wmT2 %f``
csf_t2_val: (a float)
CSF T2 value [400ms].
argument: ``-csfT2 %f``
perf_thr: (a float)
Threshold for perfusion/diffsuion effects [100].
argument: ``-perfthreshold %f``
mcout: (a file name)
Filename of mc samples (ascii text file)
argument: ``-mcout %s``
mcsamples: (an integer (int or long))
Number of samples to keep [100].
argument: ``-mcsamples %d``
mcmaxit: (an integer (int or long))
Number of iterations to run [10,000].
argument: ``-mcmaxit %d``
acceptance: (a float)
Fraction of iterations to accept [0.23].
argument: ``-accpetance %f``
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
error_file: (a file name)
Filename of parameter error maps
res_file: (a file name)
Filename of model residual map
syn_file: (a file name)
Filename of synthetic image
nodiff_file: (a file name)
Filename of average no diffusion image.
mdmap_file: (a file name)
Filename of MD map/ADC
famap_file: (a file name)
Filename of FA map
v1map_file: (a file name)
Filename of PDD map [x,y,z]
rgbmap_file: (a file name)
Filename of colour FA map
tenmap_file: (a file name)
Filename of tensor map
tenmap2_file: (a file name)
Filename of tensor map [lower tri]
mcmap_file: (a file name)
Filename of multi-compartment model parameter map
(-ivim,-ball,-nod).
mcout: (a file name)
Filename of mc samples (ascii text file)