3 d0I@sddljZddlmZmZmZmZmZm Z m Z m Z ddlm Z m Z Gddde ZGdd d eZGd d d e ZGd d d e ZGdddeZGddde ZGddde ZGdddeZGddde ZGddde ZGdddeZGddde ZGddde ZGd d!d!eZGd"d#d#e ZGd$d%d%eZGd&d'd'eZGd(d)d)eZGd*d+d+eZ Gd,d-d-eZ!Gd.d/d/eZ"dS)0N)CommandLineInputSpec CommandLinetraits TraitedSpecFile isdefined UndefinedInputMultiObject)MRTrix3BaseInputSpec MRTrix3Basec@speZdZeddddddZeddddd Zejejejejfd d d Z ed dddddZ edddddddZ dS)DWIDenoiseInputSpecTz%srzinput DWI image)existsargstrposition mandatorydescz-mask %sr z mask image)rrrrz-extent %d,%d,%dz>set the window size of the denoising filter. (default = 5,5,5))rrz -noise %sz%s_noisein_filezthe output noise map)r name_template name_sourcekeep_extensionrz %s_denoisedzthe output denoised DWI image)rrrrrrN) __name__ __module__ __qualname__rrmaskrTupleIntZextentnoiseout_filer"r"F/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/mrtrix3/preprocess.pyrs(rc@s$eZdZedddZedddZdS)DWIDenoiseOutputSpeczthe output noise mapT)rrzthe output denoised DWI imageN)rrrrr r!r"r"r"r#r$/s r$c@seZdZdZdZeZeZdS) DWIDenoisea Denoise DWI data and estimate the noise level based on the optimal threshold for PCA. DWI data denoising and noise map estimation by exploiting data redundancy in the PCA domain using the prior knowledge that the eigenspectrum of random covariance matrices is described by the universal Marchenko Pastur distribution. Important note: image denoising must be performed as the first step of the image processing pipeline. The routine will fail if interpolation or smoothing has been applied to the data prior to denoising. Note that this function does not correct for non-Gaussian noise biases. For more information, see Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> denoise = mrt.DWIDenoise() >>> denoise.inputs.in_file = 'dwi.mif' >>> denoise.inputs.mask = 'mask.mif' >>> denoise.inputs.noise = 'noise.mif' >>> denoise.cmdline # doctest: +ELLIPSIS 'dwidenoise -mask mask.mif -noise noise.mif dwi.mif dwi_denoised.mif' >>> denoise.run() # doctest: +SKIP Z dwidenoiseN) rrr__doc___cmdr input_specr$ output_specr"r"r"r#r%4sr%c @seZdZeddddddZejddgddddd d d Zejd dd ddZ ejdddddZ ejdddddZ edddddddZ dS)MRDeGibbsInputSpecTz%srzinput DWI image)rrrrrrr ,z-axes %sz]indicate the plane in which the data was acquired (axial = 0,1; coronal = 0,2; sagittal = 1,2) default_value usedefaultsepZminlenmaxlenrrz -nshifts %dz1discretization of subpixel spacing (default = 20))r,r-rrz-minW %dzMleft border of window used for total variation (TV) computation (default = 1)z-maxW %dzNright border of window used for total variation (TV) computation (default = 3)z%s_unrrzthe output unringed DWI image)rrrrrrNrr) rrrrrrZListIntZaxesrZnshiftsZminWZmaxWr!r"r"r"r#r*Ys@r*c@seZdZedddZdS)MRDeGibbsOutputSpeczthe output unringed DWI imageT)rrN)rrrrr!r"r"r"r#r2sr2c@seZdZdZdZeZeZdS) MRDeGibbsa Remove Gibbs ringing artifacts. This application attempts to remove Gibbs ringing artefacts from MRI images using the method of local subvoxel-shifts proposed by Kellner et al. This command is designed to run on data directly after it has been reconstructed by the scanner, before any interpolation of any kind has taken place. You should not run this command after any form of motion correction (e.g. not after dwipreproc). Similarly, if you intend running dwidenoise, you should run this command afterwards, since it has the potential to alter the noise structure, which would impact on dwidenoise's performance. Note that this method is designed to work on images acquired with full k-space coverage. Running this method on partial Fourier ('half-scan') data may lead to suboptimal and/or biased results, as noted in the original reference below. There is currently no means of dealing with this; users should exercise caution when using this method on partial Fourier data, and inspect its output for any obvious artefacts. For more information, see Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> unring = mrt.MRDeGibbs() >>> unring.inputs.in_file = 'dwi.mif' >>> unring.cmdline 'mrdegibbs -axes 0,1 -maxW 3 -minW 1 -nshifts 20 dwi.mif dwi_unr.mif' >>> unring.run() # doctest: +SKIP Z mrdegibbsN) rrrr&r'r*r(r2r)r"r"r"r#r3s"r3c @sxeZdZeddddddZedddZejd dd d d gd Zejdddd dgd Z edddZ eddddddddZ dS)DWIBiasCorrectInputSpecTz%srzinput DWI image)rrrrrz-mask %sz*input mask image for bias field estimation)rrZantsz/use ANTS N4 to estimate the inhomogeneity fieldruse_fsl)rrrrxorZfslz0use FSL FAST to estimate the inhomogeneity fielduse_antsz-bias %sz bias fieldz %s_biascorrrr z#the output bias corrected DWI image)rrrrrrZgenfileNrr) rrrrrin_maskrBoolr7r5biasr!r"r"r"r#r4s0    r4c@s$eZdZedddZedddZdS)DWIBiasCorrectOutputSpeczthe output bias fieldT)rrz#the output bias corrected DWI imageN)rrrrr:r!r"r"r"r#r;s r;cs,eZdZdZdZeZeZfddZ Z S)DWIBiasCorrecta) Perform B1 field inhomogeneity correction for a DWI volume series. For more information, see Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> bias_correct = mrt.DWIBiasCorrect() >>> bias_correct.inputs.in_file = 'dwi.mif' >>> bias_correct.inputs.use_ants = True >>> bias_correct.cmdline 'dwibiascorrect ants dwi.mif dwi_biascorr.mif' >>> bias_correct.run() # doctest: +SKIP ZdwibiascorrectcsH|dkr8|j}|dk r8|ddks,|jdr8d|jStj|||S)Nr7r5r3z3.0_RC-)r7r5)version startswithrsuper _format_arg)selfnameZ trait_specvaluever) __class__r"r#rBs  zDWIBiasCorrect._format_arg) rrrr&r'r4r(r;r)rB __classcell__r"r")rGr#r<s r<c @seZdZeddddddZeddddddd Zejd d d d dddddZej ddddZ ej dddZ eddddZ ejdddZej dddZej dd dZejd!d"dZejd#d$dZed%ddd&gd'd(Zejed)dd*d+ed,dd-d+d.d/gd0d1Zd2S)3DWIPreprocInputSpecTz%srzinput DWI image)rrrrrz preproc.mifr zoutput file after preprocessing)rrrr-rnonepairallheaderz-rpe_%sra3Specify acquisition phase-encoding design. "none" for no reversed phase-encoding image, "all" for all DWIs have opposing phase-encoding acquisition, "pair" for using a pair of b0 volumes for inhomogeneity field estimation only, and "header" for phase-encoding information can be found in the image header(s))rrrrz -pe_dir %szSpecify the phase encoding direction of the input series, can be a signed axis number (e.g. -0, 1, +2), an axis designator (e.g. RL, PA, IS), or NIfTI axis codes (e.g. i-, j, k))rrrz-readout_time %fz/Total readout time of input series (in seconds))rrz -se_epi %szProvide an additional image series consisting of spin-echo EPI images, which is to be used exclusively by topup for estimating the inhomogeneity field (i.e. it will not form part of the output image series))rrrz -align_seepizaAchieve alignment between the SE-EPI images used for inhomogeneity field estimation, and the DWIsz-eddy_options "%s"zDManually provide additional command-line options to the eddy commandz-topup_options "%s"zEManually provide additional command-line options to the topup commandz-export_grad_mrtrixz*export new gradient files in mrtrix formatz-export_grad_fslz#export gradient files in FSL formatzgrad.bexport_grad_mrtrixzname of new gradient file)rr-requiresrz grad.bvecsZbvecs)r-rz grad.bvalsZbvalsz%s, %sexport_grad_fslz*Output (bvecs, bvals) gradients FSL format)rrOrN)rrrrrr!rEnumZ rpe_optionsStrZpe_dirZFloatZro_timeZin_epir9Z align_seepiZ eddy_optionsZ topup_optionsrNrPout_grad_mrtrixr out_grad_fslr"r"r"r#rIsl    rIc@sHeZdZedddZedddddZedddd dZed ddd dZd S) DWIPreprocOutputSpecz%sz output preprocessed image series)rrzgrad.bTz,preprocessed gradient file in mrtrix3 format)rr-rz grad.bvecszexported fsl gradient bvec filez grad.bvalszexported fsl gradient bval fileN)rrrrr!rS out_fsl_bvec out_fsl_bvalr"r"r"r#rU<s  rUc@s$eZdZdZdZeZeZddZ dS) DWIPreproca~ Perform diffusion image pre-processing using FSL's eddy tool; including inhomogeneity distortion correction using FSL's topup tool if possible For more information, see Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> preproc = mrt.DWIPreproc() >>> preproc.inputs.in_file = 'dwi.mif' >>> preproc.inputs.rpe_options = 'none' >>> preproc.inputs.out_file = "preproc.mif" >>> preproc.inputs.eddy_options = '--slm=linear --repol' # linear second level model and replace outliers >>> preproc.inputs.export_grad_mrtrix = True # export final gradient table in MRtrix format >>> preproc.inputs.ro_time = 0.165240 # 'TotalReadoutTime' in BIDS JSON metadata files >>> preproc.inputs.pe_dir = 'j' # 'PhaseEncodingDirection' in BIDS JSON metadata files >>> preproc.cmdline 'dwifslpreproc dwi.mif preproc.mif -rpe_none -eddy_options "--slm=linear --repol" -export_grad_mrtrix grad.b -pe_dir j -readout_time 0.165240' >>> preproc.run() # doctest: +SKIP Z dwifslpreproccCsp|jj}tj|jj|d<|jjr8tj|jj|d<|jjrltj|jj d|d<tj|jj d|d<|S)Nr!rSrrVr rW) r)getopabspathinputsr!rNrSrPrT)rCoutputsr"r"r# _list_outputsns zDWIPreproc._list_outputsN) rrrr&r'rIr(rUr)r^r"r"r"r#rXRs rXc @seZdZejddddddddd Zeddddd d Zedd ddZeddd!dddZ edd"ddZ edd#ddZ eddddZ e ejddddZdS)$ResponseSDInputSpecZmsmt_5ttZ dhollanderZtournierZtaxz%sr Tz,response estimation algorithm (multi-tissue))rrrrzinput DWI image)rrrrrzinput 5tt image)rrrzwm.txtr1zoutput WM response text file)rrr-rrzoutput GM response text filezoutput CSF response text filez-mask %szprovide initial mask image)rrrz-lmax %sr+ztmaximum harmonic degree of response function - single value for single-shell response, list for multi-shell response)rr.rNrr)rrrrrQ algorithmrrZmtt_filewm_filegm_filecsf_filer8r rZmax_shr"r"r"r#r_zs4r_c@s0eZdZedddZedddZedddZdS)ResponseSDOutputSpecz%szoutput WM response text file)rrzoutput GM response text filezoutput CSF response text fileN)rrrrrfrgrhr"r"r"r#ris  ric@s$eZdZdZdZeZeZddZ dS) ResponseSDa Estimate response function(s) for spherical deconvolution using the specified algorithm. Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> resp = mrt.ResponseSD() >>> resp.inputs.in_file = 'dwi.mif' >>> resp.inputs.algorithm = 'tournier' >>> resp.inputs.grad_fsl = ('bvecs', 'bvals') >>> resp.cmdline # doctest: +ELLIPSIS 'dwi2response tournier -fslgrad bvecs bvals dwi.mif wm.txt' >>> resp.run() # doctest: +SKIP # We can also pass in multiple harmonic degrees in the case of multi-shell >>> resp.inputs.max_sh = [6,8,10] >>> resp.cmdline 'dwi2response tournier -fslgrad bvecs bvals -lmax 6,8,10 dwi.mif wm.txt' Z dwi2responsecCs^|jj}tj|jj|d<|jjtkr>> import nipype.interfaces.mrtrix3 as mrt >>> prep = mrt.ACTPrepareFSL() >>> prep.inputs.in_file = 'T1.nii.gz' >>> prep.cmdline # doctest: +ELLIPSIS 'act_anat_prepare_fsl T1.nii.gz act_5tt.mif' >>> prep.run() # doctest: +SKIP Zact_anat_prepare_fslcCs"|jj}tj|jj|d<|S)Nr!)r)rYrZr[r\r!)rCr]r"r"r#r^s zACTPrepareFSL._list_outputsN) rrrr&r'rkr(rlr)r^r"r"r"r#rms rmc@sTeZdZeddddddZeddddddZedddd d Zed ddddd dZdS)ReplaceFSwithFIRSTInputSpecTz%srazinput anatomical image)rrrrrr1zinput T1 imagerzconnectome configuration file)rrrrzaparc+first.mifr zoutput file after processing)rrrr-rNrcrdrr)rrrrrZin_t1wZ in_configr!r"r"r"r#rns"rnc@seZdZedddZdS)ReplaceFSwithFIRSTOutputSpecTzthe output response file)rrN)rrrrr!r"r"r"r#rosroc@s$eZdZdZdZeZeZddZ dS)ReplaceFSwithFIRSTaY Replace deep gray matter structures segmented with FSL FIRST in a FreeSurfer parcellation. Example ------- >>> import nipype.interfaces.mrtrix3 as mrt >>> prep = mrt.ReplaceFSwithFIRST() >>> prep.inputs.in_file = 'aparc+aseg.nii' >>> prep.inputs.in_t1w = 'T1.nii.gz' >>> prep.inputs.in_config = 'mrtrix3_labelconfig.txt' >>> prep.cmdline # doctest: +ELLIPSIS 'fs_parc_replace_sgm_first aparc+aseg.nii T1.nii.gz mrtrix3_labelconfig.txt aparc+first.mif' >>> prep.run() # doctest: +SKIP Zfs_parc_replace_sgm_firstcCs"|jj}tj|jj|d<|S)Nr!)r)rYrZr[r\r!)rCr]r"r"r#r^-s z ReplaceFSwithFIRST._list_outputsN) rrrr&r'rnr(ror)r^r"r"r"r#rps rp)#os.pathpathrZbaserrrrrrr r r r rr$r%r*r2r3r4r;r<rIrUrXr_rirjrkrlrmrnrorpr"r"r"r#s. ( %,) F($$