# 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 `_ 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"