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Merge pull request #1910 from mmodat/adding_niftyfit
Inital commit to add niftyfit interfaces
2 parents f39e222 + a74e966 commit 109cf78

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‎nipype/interfaces/niftyfit/__init__.py

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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""
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The niftyfit module provides classes for interfacing with the `NiftyFit`_
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command line tools.
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Top-level namespace for niftyfit.
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"""
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from .asl import FitAsl
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from .dwi import FitDwi, DwiTool
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from .qt1 import FitQt1

‎nipype/interfaces/niftyfit/asl.py

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

‎nipype/interfaces/niftyfit/base.py

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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""
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The niftyfit module provide an interface with the niftyfit software
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developed in TIG, UCL.
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Software available at:
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https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyFit-Release
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Version used for this version of the interfaces (git):
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commit c6232e4c4223c3d19f7a32906409da5af36299a2
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Date: Fri Jan 6 13:34:02 2017 +0000
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Examples
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--------
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See the docstrings of the individual classes for examples.
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"""
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import os
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import warnings
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from ..base import CommandLine
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from ...utils.filemanip import split_filename
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warn = warnings.warn
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warnings.filterwarnings('always', category=UserWarning)
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class NiftyFitCommand(CommandLine):
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"""
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Base support interface for NiftyFit commands.
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"""
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_suffix = '_nf'
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def __init__(self, **inputs):
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""" Init method calling super. No version to be checked."""
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super(NiftyFitCommand, self).__init__(**inputs)
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def _gen_fname(self, basename, out_dir=None, suffix=None, ext=None):
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if basename == '':
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msg = 'Unable to generate filename for command %s. ' % self.cmd
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msg += 'basename is not set!'
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raise ValueError(msg)
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_, final_bn, final_ext = split_filename(basename)
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if out_dir is None:
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out_dir = os.getcwd()
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if ext is not None:
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final_ext = ext
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if suffix is not None:
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final_bn = ''.join((final_bn, suffix))
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return os.path.abspath(os.path.join(out_dir, final_bn + final_ext))

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