/
asl.py
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/
asl.py
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
The ASL module of niftyfit, which wraps the fitting methods in NiftyFit.
"""
from ..base import File, TraitedSpec, traits, CommandLineInputSpec
from .base import NiftyFitCommand
from ..niftyreg.base import get_custom_path
class FitAslInputSpec(CommandLineInputSpec):
"""Input Spec for FitAsl."""
desc = "Filename of the 4D ASL (control/label) source image (mandatory)."
source_file = File(
position=1, exists=True, argstr="-source %s", mandatory=True, desc=desc
)
pasl = traits.Bool(desc="Fit PASL ASL data [default]", argstr="-pasl")
pcasl = traits.Bool(desc="Fit PCASL ASL data", argstr="-pcasl")
# *** Output options:
desc = "Filename of the Cerebral Blood Flow map (in ml/100g/min)."
cbf_file = File(
name_source=["source_file"],
name_template="%s_cbf.nii.gz",
argstr="-cbf %s",
desc=desc,
)
error_file = File(
name_source=["source_file"],
name_template="%s_error.nii.gz",
argstr="-error %s",
desc="Filename of the CBF error map.",
)
syn_file = File(
name_source=["source_file"],
name_template="%s_syn.nii.gz",
argstr="-syn %s",
desc="Filename of the synthetic ASL data.",
)
# *** Input options (see also fit_qt1 for generic T1 fitting):
desc = "Filename of the estimated input T1 map (in ms)."
t1map = File(exists=True, argstr="-t1map %s", desc=desc)
desc = "Filename of the estimated input M0 map."
m0map = File(exists=True, argstr="-m0map %s", desc=desc)
desc = "Filename of the estimated input M0 map error."
m0mape = File(exists=True, argstr="-m0mape %s", desc=desc)
desc = "Filename of a [1,2,5]s Inversion Recovery volume (T1/M0 fitting \
carried out internally)."
ir_volume = File(exists=True, argstr="-IRvolume %s", desc=desc)
desc = "Output of [1,2,5]s Inversion Recovery fitting."
ir_output = File(exists=True, argstr="-IRoutput %s", desc=desc)
# *** Experimental options (Choose those suitable for the model!):
mask = File(
position=2, exists=True, desc="Filename of image mask.", argstr="-mask %s"
)
t1_art_cmp = traits.Float(
desc="T1 of arterial component [1650ms].", argstr="-T1a %f"
)
desc = "Single plasma/tissue partition coefficient [0.9ml/g]."
plasma_coeff = traits.Float(desc=desc, argstr="-L %f")
desc = "Labelling efficiency [0.99 (pasl), 0.85 (pcasl)], ensure any \
background suppression pulses are included in -eff"
eff = traits.Float(desc=desc, argstr="-eff %f")
desc = "Outlier rejection for multi CL volumes (enter z-score threshold \
(e.g. 2.5)) [off]."
out = traits.Float(desc=desc, argstr="-out %f")
# *** PCASL options (Choose those suitable for the model!):
pld = traits.Float(desc="Post Labelling Delay [2000ms].", argstr="-PLD %f")
ldd = traits.Float(desc="Labelling Duration [1800ms].", argstr="-LDD %f")
desc = "Difference in labelling delay per slice [0.0 ms/slice."
dpld = traits.Float(desc=desc, argstr="-dPLD %f")
# *** PASL options (Choose those suitable for the model!):
t_inv1 = traits.Float(desc="Saturation pulse time [800ms].", argstr="-Tinv1 %f")
t_inv2 = traits.Float(desc="Inversion time [2000ms].", argstr="-Tinv2 %f")
desc = "Difference in inversion time per slice [0ms/slice]."
dt_inv2 = traits.Float(desc=desc, argstr="-dTinv2 %f")
# *** Other experimental assumptions:
# Not programmed yet
# desc = 'Slope and intercept for Arterial Transit Time.'
# ATT = traits.Float(desc=desc, argstr='-ATT %f')
gm_t1 = traits.Float(desc="T1 of GM [1150ms].", argstr="-gmT1 %f")
gm_plasma = traits.Float(
desc="Plasma/GM water partition [0.95ml/g].", argstr="-gmL %f"
)
gm_ttt = traits.Float(desc="Time to GM [ATT+0ms].", argstr="-gmTTT %f")
wm_t1 = traits.Float(desc="T1 of WM [800ms].", argstr="-wmT1 %f")
wm_plasma = traits.Float(
desc="Plasma/WM water partition [0.82ml/g].", argstr="-wmL %f"
)
wm_ttt = traits.Float(desc="Time to WM [ATT+0ms].", argstr="-wmTTT %f")
# *** Segmentation options:
desc = "Filename of the 4D segmentation (in ASL space) for L/T1 \
estimation and PV correction {WM,GM,CSF}."
seg = File(exists=True, argstr="-seg %s", desc=desc)
desc = "Use sigmoid to estimate L from T1: L(T1|gmL,wmL) [Off]."
sig = traits.Bool(desc=desc, argstr="-sig")
desc = "Simple PV correction (CBF=vg*CBFg + vw*CBFw, with CBFw=f*CBFg) \
[0.25]."
pv0 = traits.Int(desc=desc, argstr="-pv0 %d")
pv2 = traits.Int(desc="In plane PV kernel size [3x3].", argstr="-pv2 %d")
pv3 = traits.Tuple(
traits.Int,
traits.Int,
traits.Int,
desc="3D kernel size [3x3x1].",
argstr="-pv3 %d %d %d",
)
desc = "Multiply CBF by this value (e.g. if CL are mislabelled use -1.0)."
mul = traits.Float(desc=desc, argstr="-mul %f")
mulgm = traits.Bool(desc="Multiply CBF by segmentation [Off].", argstr="-sig")
desc = "Set PV threshold for switching off LSQR [O.05]."
pv_threshold = traits.Bool(desc=desc, argstr="-pvthreshold")
segstyle = traits.Bool(desc="Set CBF as [gm,wm] not [wm,gm].", argstr="-segstyle")
class FitAslOutputSpec(TraitedSpec):
"""Output Spec for FitAsl."""
desc = "Filename of the Cerebral Blood Flow map (in ml/100g/min)."
cbf_file = File(exists=True, desc=desc)
desc = "Filename of the CBF error map."
error_file = File(exists=True, desc=desc)
desc = "Filename of the synthetic ASL data."
syn_file = File(exists=True, desc=desc)
class FitAsl(NiftyFitCommand):
"""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 <https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyFit-Release>`_
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'
"""
_cmd = get_custom_path("fit_asl", env_dir="NIFTYFITDIR")
input_spec = FitAslInputSpec
output_spec = FitAslOutputSpec
_suffix = "_fit_asl"