interfaces.niftyfit.qt1¶
FitQt1¶
Wraps the executable command fit_qt1.
Interface for executable fit_qt1 from Niftyfit platform.
Use NiftyFit to perform Qt1 fitting.
T1 Fitting Routine (To inversion recovery or spgr data). Fits single component T1 maps in the first instance.
Examples¶
>>> from nipype.interfaces.niftyfit import FitQt1
>>> fit_qt1 = FitQt1()
>>> fit_qt1.inputs.source_file = 'TI4D.nii.gz'
>>> fit_qt1.cmdline
'fit_qt1 -source TI4D.nii.gz -comp TI4D_comp.nii.gz -error TI4D_error.nii.gz -m0map TI4D_m0map.nii.gz -mcmap TI4D_mcmap.nii.gz -res TI4D_res.nii.gz -syn TI4D_syn.nii.gz -t1map TI4D_t1map.nii.gz'
Inputs:
[Mandatory]
source_file: (a pathlike object or string representing an existing
file)
Filename of the 4D Multi-Echo T1 source image.
argument: ``-source %s``, position: 1
[Optional]
t1map_file: (a pathlike object or string representing a file)
Filename of the estimated output T1 map (in ms).
argument: ``-t1map %s``
m0map_file: (a pathlike object or string representing a file)
Filename of the estimated input M0 map.
argument: ``-m0map %s``
mcmap_file: (a pathlike object or string representing a file)
Filename of the estimated output multi-parameter map.
argument: ``-mcmap %s``
comp_file: (a pathlike object or string representing a file)
Filename of the estimated multi-component T1 map.
argument: ``-comp %s``
error_file: (a pathlike object or string representing a file)
Filename of the error map (symmetric matrix, [Diag,OffDiag]).
argument: ``-error %s``
syn_file: (a pathlike object or string representing a file)
Filename of the synthetic ASL data.
argument: ``-syn %s``
res_file: (a pathlike object or string representing a file)
Filename of the model fit residuals
argument: ``-res %s``
mask: (a pathlike object or string representing an existing file)
Filename of image mask.
argument: ``-mask %s``, position: 2
prior: (a pathlike object or string representing an existing file)
Filename of parameter prior.
argument: ``-prior %s``, position: 3
te_value: (a float)
TE Echo Time [0ms!].
argument: ``-TE %f``, position: 4
tr_value: (a float)
TR Repetition Time [10s!].
argument: ``-TR %f``, position: 5
nb_comp: (an integer (int or long))
Number of components to fit [1] (currently IR/SR only)
argument: ``-nc %d``, position: 6
lm_val: (a tuple of the form: (a float, a float))
Set LM parameters (initial value, decrease rate) [100,1.2].
argument: ``-lm %f %f``, position: 7
gn_flag: (a boolean)
Use Gauss-Newton algorithm [Levenberg-Marquardt].
argument: ``-gn``, position: 8
slice_no: (an integer (int or long))
Fit to single slice number.
argument: ``-slice %d``, position: 9
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``, position: 10
maxit: (an integer (int or long))
NLSQR iterations [100].
argument: ``-maxit %d``, position: 11
sr_flag: (a boolean)
Saturation Recovery fitting [default].
argument: ``-SR``, position: 12
ir_flag: (a boolean)
Inversion Recovery fitting [default].
argument: ``-IR``, position: 13
tis: (a list of items which are a float)
Inversion times for T1 data [1s,2s,5s].
argument: ``-TIs %s``, position: 14
tis_list: (a pathlike object or string representing an existing file)
Filename of list of pre-defined TIs.
argument: ``-TIlist %s``
t1_list: (a pathlike object or string representing an existing file)
Filename of list of pre-defined T1s
argument: ``-T1list %s``
t1min: (a float)
Minimum tissue T1 value [400ms].
argument: ``-T1min %f``
t1max: (a float)
Maximum tissue T1 value [4000ms].
argument: ``-T1max %f``
spgr: (a boolean)
Spoiled Gradient Echo fitting
argument: ``-SPGR``
flips: (a list of items which are a float)
Flip angles
argument: ``-flips %s``
flips_list: (a pathlike object or string representing an existing
file)
Filename of list of pre-defined flip angles (deg).
argument: ``-fliplist %s``
b1map: (a pathlike object or string representing an existing file)
Filename of B1 estimate for fitting (or include in prior).
argument: ``-b1map %s``
mcout: (a pathlike object or string representing an existing file)
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: ``-acceptance %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:
t1map_file: (a pathlike object or string representing a file)
Filename of the estimated output T1 map (in ms)
m0map_file: (a pathlike object or string representing a file)
Filename of the m0 map
mcmap_file: (a pathlike object or string representing a file)
Filename of the estimated output multi-parameter map
comp_file: (a pathlike object or string representing a file)
Filename of the estimated multi-component T1 map.
error_file: (a pathlike object or string representing a file)
Filename of the error map (symmetric matrix, [Diag,OffDiag])
syn_file: (a pathlike object or string representing a file)
Filename of the synthetic ASL data
res_file: (a pathlike object or string representing a file)
Filename of the model fit residuals