interfaces.niftyfit.asl

FitAsl

Link to code

Wraps the executable command fit_asl.

Interface for executable fit_asl from Niftyfit platform.

Use NiftyFit to perform ASL fitting.

ASL fitting routines (following EU Cost Action White Paper recommendations) Fits Cerebral Blood Flow maps in the first instance.

Source code

Examples

>>> from nipype.interfaces import niftyfit
>>> node = niftyfit.FitAsl()
>>> node.inputs.source_file = 'asl.nii.gz'
>>> node.cmdline
'fit_asl -source asl.nii.gz -cbf asl_cbf.nii.gz -error asl_error.nii.gz -syn asl_syn.nii.gz'

Inputs:

[Mandatory]
source_file: (a pathlike object or string representing an existing
          file)
        Filename of the 4D ASL (control/label) source image (mandatory).
        argument: ``-source %s``, position: 1

[Optional]
pasl: (a boolean)
        Fit PASL ASL data [default]
        argument: ``-pasl``
pcasl: (a boolean)
        Fit PCASL ASL data
        argument: ``-pcasl``
cbf_file: (a pathlike object or string representing a file)
        Filename of the Cerebral Blood Flow map (in ml/100g/min).
        argument: ``-cbf %s``
error_file: (a pathlike object or string representing a file)
        Filename of the CBF error map.
        argument: ``-error %s``
syn_file: (a pathlike object or string representing a file)
        Filename of the synthetic ASL data.
        argument: ``-syn %s``
t1map: (a pathlike object or string representing an existing file)
        Filename of the estimated input T1 map (in ms).
        argument: ``-t1map %s``
m0map: (a pathlike object or string representing an existing file)
        Filename of the estimated input M0 map.
        argument: ``-m0map %s``
m0mape: (a pathlike object or string representing an existing file)
        Filename of the estimated input M0 map error.
        argument: ``-m0mape %s``
ir_volume: (a pathlike object or string representing an existing
          file)
        Filename of a [1,2,5]s Inversion Recovery volume (T1/M0 fitting
        carried out internally).
        argument: ``-IRvolume %s``
ir_output: (a pathlike object or string representing an existing
          file)
        Output of [1,2,5]s Inversion Recovery fitting.
        argument: ``-IRoutput %s``
mask: (a pathlike object or string representing an existing file)
        Filename of image mask.
        argument: ``-mask %s``, position: 2
t1_art_cmp: (a float)
        T1 of arterial component [1650ms].
        argument: ``-T1a %f``
plasma_coeff: (a float)
        Single plasma/tissue partition coefficient [0.9ml/g].
        argument: ``-L %f``
eff: (a float)
        Labelling efficiency [0.99 (pasl), 0.85 (pcasl)], ensure any
        background suppression pulses are included in -eff
        argument: ``-eff %f``
out: (a float)
        Outlier rejection for multi CL volumes (enter z-score threshold
        (e.g. 2.5)) [off].
        argument: ``-out %f``
pld: (a float)
        Post Labelling Delay [2000ms].
        argument: ``-PLD %f``
ldd: (a float)
        Labelling Duration [1800ms].
        argument: ``-LDD %f``
dpld: (a float)
        Difference in labelling delay per slice [0.0 ms/slice.
        argument: ``-dPLD %f``
t_inv1: (a float)
        Saturation pulse time [800ms].
        argument: ``-Tinv1 %f``
t_inv2: (a float)
        Inversion time [2000ms].
        argument: ``-Tinv2 %f``
dt_inv2: (a float)
        Difference in inversion time per slice [0ms/slice].
        argument: ``-dTinv2 %f``
gm_t1: (a float)
        T1 of GM [1150ms].
        argument: ``-gmT1 %f``
gm_plasma: (a float)
        Plasma/GM water partition [0.95ml/g].
        argument: ``-gmL %f``
gm_ttt: (a float)
        Time to GM [ATT+0ms].
        argument: ``-gmTTT %f``
wm_t1: (a float)
        T1 of WM [800ms].
        argument: ``-wmT1 %f``
wm_plasma: (a float)
        Plasma/WM water partition [0.82ml/g].
        argument: ``-wmL %f``
wm_ttt: (a float)
        Time to WM [ATT+0ms].
        argument: ``-wmTTT %f``
seg: (a pathlike object or string representing an existing file)
        Filename of the 4D segmentation (in ASL space) for L/T1 estimation
        and PV correction {WM,GM,CSF}.
        argument: ``-seg %s``
sig: (a boolean)
        Use sigmoid to estimate L from T1: L(T1|gmL,wmL) [Off].
        argument: ``-sig``
pv0: (an integer (int or long))
        Simple PV correction (CBF=vg*CBFg + vw*CBFw, with CBFw=f*CBFg)
        [0.25].
        argument: ``-pv0 %d``
pv2: (an integer (int or long))
        In plane PV kernel size [3x3].
        argument: ``-pv2 %d``
pv3: (a tuple of the form: (an integer (int or long), an integer (int
          or long), an integer (int or long)))
        3D kernel size [3x3x1].
        argument: ``-pv3 %d %d %d``
mul: (a float)
        Multiply CBF by this value (e.g. if CL are mislabelled use -1.0).
        argument: ``-mul %f``
mulgm: (a boolean)
        Multiply CBF by segmentation [Off].
        argument: ``-sig``
pv_threshold: (a boolean)
        Set PV threshold for switching off LSQR [O.05].
        argument: ``-pvthreshold``
segstyle: (a boolean)
        Set CBF as [gm,wm] not [wm,gm].
        argument: ``-segstyle``
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:

cbf_file: (a pathlike object or string representing an existing file)
        Filename of the Cerebral Blood Flow map (in ml/100g/min).
error_file: (a pathlike object or string representing an existing
          file)
        Filename of the CBF error map.
syn_file: (a pathlike object or string representing an existing file)
        Filename of the synthetic ASL data.