3 Xd&@sdZddlZddlZddlmZmZmZmZm Z m Z GdddeZ GdddeZ Gd d d eZ Gd d d eZGd ddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdddeZGdd d eZGd!d"d"eZGd#d$d$eZGd%d&d&eZGd'd(d(eZGd)d*d*eZGd+d,d,eZGd-d.d.eZGd/d0d0eZ Gd1d2d2eZ!Gd3d4d4eZ"Gd5d6d6eZ#Gd7d8d8eZ$Gd9d:d:eZ%Gd;d<dd>eZ'Gd?d@d@eZ(GdAdBdBeZ)GdCdDdDeZ*GdEdFdFeZ+GdGdHdHeZ,GdIdJdJeZ-GdKdLdLeZ.GdMdNdNeZ/GdOdPdPeZ0GdQdRdReZ1GdSdTdTeZ2GdUdVdVeZ3GdWdXdXeZ4dYdZZ5d[d\Z6dS)]zThis script provides interfaces for BrainSuite command line tools. Please see brainsuite.org for more information. Author: Jason Wong N) TraitedSpecCommandLineInputSpec CommandLineFiletraits isdefinedc@s eZdZeddddZedddddZed d dddZejd dd d dZ ej dddddZ ejdddddZ ejdddddZ ejdddddZejdddddZeddddZedd ddZed!d"ddZed#d$ddZejddd%d&dZejd'd(d)Zejd*d+d)Zd,S)- BseInputSpecTz-i %szinput MRI volume) mandatoryargstrdesczXoutput brain-masked MRI volume. If unspecified, output file name will be auto generated.z-o %sF)r r hash_filesgenfilezPsave smooth brain mask. If unspecified, output file name will be auto generated.z --mask %szdiffusion constantz-d %f) usedefaultr r zdiffusion iterationsz-n %dg{Gz?zedge detection constantz-s %fz!radius of erosion/dilation filterz-r %fzdilate final maskz-pztrim brainstemz--trimzdiffusion filter outputz--adf %s)r r r zedge map outputz --edge %szsave detailed brain maskz --hires %sz cortex filez --cortex %sz verbosity level (0=silent)z-v %fzaretain original orientation(default behavior will auto-rotate input NII files to LPI orientation)z --norotate)r r z show timingz--timerN)__name__ __module__ __qualname__r inputMRIFileoutputMRIVolumeoutputMaskFilerFloatZdiffusionConstantIntZdiffusionIterationsZedgeDetectionConstantZradiusBoolZdilateFinalMaskZtrimoutputDiffusionFilter outputEdgeMapoutputDetailedBrainMaskoutputCortexFileverbosityLevelZnoRotatetimerr"r"Y/var/www/html/virt/lib/python3.6/site-packages/nipype/interfaces/brainsuite/brainsuite.pyr sD  r c@sHeZdZeddZeddZeddZeddZeddZeddZ dS) BseOutputSpecz$path/name of brain-masked MRI volume)r zpath/name of smooth brain maskz$path/name of diffusion filter outputzpath/name of edge map outputz path/name of detailed brain maskzpath/name of cortex fileN) rrrrrrrrrrr"r"r"r#r$Fs      r$c@s,eZdZdZeZeZdZddZ ddZ dS)Bsea brain surface extractor (BSE) This program performs automated skull and scalp removal on T1-weighted MRI volumes. http://brainsuite.org/processing/surfaceextraction/bse/ Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> bse = brainsuite.Bse() >>> bse.inputs.inputMRIFile = example_data('structural.nii') >>> results = bse.run() #doctest: +SKIP ZbsecCsN|jj}t||r&tjj||Sddd}||krJt|jj||SdS)Nz .bse.nii.gzz .mask.nii.gz)rr)inputsgetrospathabspath getFileNamer)selfnamer&fileToSuffixMapr"r"r# _gen_filenamees  zBse._gen_filenamecCst|S)N) l_outputs)r,r"r"r# _list_outputstszBse._list_outputsN) rrr__doc__r input_specr$ output_spec_cmdr/r1r"r"r"r#r%Os r%c@sheZdZeddddZeddddZed d ddd Zed d ddZeddddZe j dddZ e j dddZ e j dddZ e jdddZe jdddddZe jdddZedd dZe jd!d"dZe jd#d$dZe jd%d&dZe jd'dd(d)d*Ze jd+dd,d-d*Ze jd.d/d0d1ddZe jd2d3d4d5d6ddZe jd7d8dZe jd9d:dZe j d;ddZ d?S)@ BfcInputSpecTzinput skull-stripped MRI volumez-i %s)r r r z mask filez-m %sF)r r r zZoutput bias-corrected MRI volume. If unspecified, output file name will be auto generated.z-o %s)r r r rzsave bias field estimatez --bias %sz!save bias field estimate (masked)z--maskedbias %szhistogram radius (voxels)z-r %d)r r zbias sample spacing (voxels)z-s %dzcontrol point spacing (voxels)z-c %dz$spline stiffness weighting parameterz-w %fellipseblockz|Options for type of histogram: * ``ellipse``: use ellipsoid for ROI histogram * ``block``:use block for ROI histogram z%sz2iterative mode (overrides -r, -s, -c, -w settings)z --iteratezlist of parameters z --schedule %sz>save iterative bias field estimates as .n.field.nii.gzz--biasprefix %sz8save iterative corrected images as .n.bfc.nii.gzz --prefix %sz'apply correction field to entire volumez --extrapolateg?zminimum allowed bias valuez-L %f)rr r g?zmaximum allowed bias valuez-U %flowmediumhighzPreset options for bias_model * low: small bias model [0.95,1.05] * medium: medium bias model [0.90,1.10] * high: high bias model [0.80,1.20] analyzeniftigzippedAnalyze gzippedNiftiz@Options for the format in which intermediate files are generatedzconvergence thresholdz--eps %fz:bias estimate convergence threshold (values > 0.1 disable)z --beps %fzverbosity level (0=silent)z-v %dzdisplay timing informationz--timerN)!rrrrr inputMaskFileroutputBiasFieldoutputMaskedBiasFieldrrZhistogramRadiusZbiasEstimateSpacingZcontrolPointSpacingrZ splineLambdaEnum histogramTyperZ iterativeModecorrectionScheduleFileStrZbiasFieldEstimatesOutputPrefixZcorrectedImagesOutputPrefixZcorrectWholeVolumeZminBiasZmaxBias biasRangeintermediate_file_typeZconvergenceThresholdZ biasEstimateConvergenceThresholdr r!r"r"r"r#r6xsx        r6c@s4eZdZeddZeddZeddZeddZdS) BfcOutputSpeczpath/name of output file)r z#path/name of bias field output filez%path/name of masked bias field outputzpath/name of schedule fileN)rrrrrrArBrEr"r"r"r#rIs   rIcs<eZdZdZeZeZdZddZ fddZ ddZ Z S) Bfca bias field corrector (BFC) This program corrects gain variation in T1-weighted MRI. http://brainsuite.org/processing/surfaceextraction/bfc/ Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> bfc = brainsuite.Bfc() >>> bfc.inputs.inputMRIFile = example_data('structural.nii') >>> bfc.inputs.inputMaskFile = example_data('mask.nii') >>> results = bfc.run() #doctest: +SKIP ZbfccCsL|jj}t||r&tjj||Sddi}||krHt|jj||SdS)Nrz .bfc.nii.gz)r&r'rr(r)r*r+r)r,r-r&r.r"r"r#r/s  zBfc._gen_filenamecsn|dkr|jddd|S|dkr:|jdddd |S|d krZ|jd d d dd|Stt|j|||S)NrDz --ellipsez--block)r7r8rGz--lowz--mediumz--high)r9r:r;rHz --analyzez--niftiz --analyzegzz --niftigz)r<r=r>r?)r superrJ _format_arg)r,r-specvalue) __class__r"r#rLszBfc._format_argcCst|S)N)r0)r,r"r"r#r1szBfc._list_outputs) rrrr2r6r3rIr4r5r/rLr1 __classcell__r"r")rOr#rJs rJc@szeZdZeddddZedddZedd dd Zed d dd Zej d ddZ ej dddZ ej dddZej dddZdS) PvcInputSpecTzMRI filez-i %s)r r r zbrain mask filez-m %s)r r zKoutput label file. If unspecified, output file name will be auto generated.z-o %s)r r rzoutput tissue fraction filez-f %szspatial prior strengthz-l %fzverbosity level (0 = silent)z-v %dz,use a three-class (CSF=0,GM=1,WM=2) labelingz-3ztime processingz--timerN)rrrrrr@outputLabelFileoutputTissueFractionFilerrZ spatialPriorr verbosityrZthreeClassFlagr!r"r"r"r#rQs   rQc@s eZdZeddZeddZdS) PvcOutputSpeczpath/name of label file)r z!path/name of tissue fraction fileN)rrrrrRrSr"r"r"r#rU*s rUc@s,eZdZdZeZeZdZddZ ddZ dS)PvcaR partial volume classifier (PVC) tool. This program performs voxel-wise tissue classification T1-weighted MRI. Image should be skull-stripped and bias-corrected before tissue classification. http://brainsuite.org/processing/surfaceextraction/pvc/ Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> pvc = brainsuite.Pvc() >>> pvc.inputs.inputMRIFile = example_data('structural.nii') >>> pvc.inputs.inputMaskFile = example_data('mask.nii') >>> results = pvc.run() #doctest: +SKIP ZpvccCsN|jj}t||r&tjj||Sddd}||krJt|jj||SdS)Nz.pvc.label.nii.gzz.pvc.frac.nii.gz)rRrS)r&r'rr(r)r*r+r)r,r-r&r.r"r"r#r/Gs  zPvc._gen_filenamecCst|S)N)r0)r,r"r"r#r1UszPvc._list_outputsN) rrrr2rQr3rUr4r5r/r1r"r"r"r#rV/s rVc@seZdZeddddZeddddZeddddZed d d Zed d ddZeddddZ e j dddddZ e j ddd ZeddddZeddddZe j ddd Ze jddd Ze j dd d Ze jd!d"d Ze j d#d$d Ze jd%d&d Ze jd'd(d Zd)S)*CerebroInputSpecTzinput 3D MRI volumez-i %s)r r r zatlas MRI volumez --atlas %szatlas labelingz--atlaslabels %szbrain mask filez-m %s)r r zUoutput cerebrum mask volume. If unspecified, output file name will be auto generated.z-o %s)r r rzcoutput labeled hemisphere/cerebrum volume. If unspecified, output file name will be auto generated.z-l %srz0,1,2z-c %d)rr r z,use centroids of data to initialize positionz --centroidszsave affine transform to file.z--air %szsave warp transform to file.z --warp %szverbosity level (0=silent)z-v %dzlinear convergencez --linconv %fzwarp order (2,3,4,5,6,7,8)z--warplevel %dzwarp convergencez --warpconv %fzdon't remove temporary filesz--keepz,specify directory to use for temporary filesz --tempdir %sz2create a temporary directory within this directoryz--tempdirbase %sN)rrrrrZinputAtlasMRIFileZinputAtlasLabelFileZinputBrainMaskFileoutputCerebrumMaskFileoutputLabelVolumeFilerrZ costFunctionrZ useCentroidsoutputAffineTransformFileoutputWarpTransformFilerTrZlinearConvergenceZ warpLabelZwarpConvergenceZ keepTempFilesrFZ tempDirectoryZtempDirectoryBaser"r"r"r#rWYs>       rWc@s4eZdZeddZeddZeddZeddZdS)CerebroOutputSpeczpath/name of cerebrum mask file)r zpath/name of label mask filez"path/name of affine transform filez path/name of warp transform fileN)rrrrrXrYrZr[r"r"r"r#r\s   r\c@s,eZdZdZeZeZdZddZ ddZ dS)Cerebroa Cerebrum/cerebellum labeling tool This program performs automated labeling of cerebellum and cerebrum in T1 MRI. Input MRI should be skull-stripped or a brain-only mask should be provided. http://brainsuite.org/processing/surfaceextraction/cerebrum/ Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> cerebro = brainsuite.Cerebro() >>> cerebro.inputs.inputMRIFile = example_data('structural.nii') >>> cerebro.inputs.inputAtlasMRIFile = 'atlasMRIVolume.img' >>> cerebro.inputs.inputAtlasLabelFile = 'atlasLabels.img' >>> cerebro.inputs.inputBrainMaskFile = example_data('mask.nii') >>> results = cerebro.run() #doctest: +SKIP ZcerebrocCsR|jj}t||r&tjj||Sddddd}||krNt|jj||SdS)Nz.cerebrum.mask.nii.gzz.hemi.label.nii.gzz.warpz.air)rXrYr[rZ)r&r'rr(r)r*r+r)r,r-r&r.r"r"r#r/s  zCerebro._gen_filenamecCst|S)N)r0)r,r"r"r#r1szCerebro._list_outputsN) rrrr2rWr3r\r4r5r/r1r"r"r"r#r]s r]c@seZdZeddddZeddddZeddd dZejd dd d d Z ej ddddd Z ej dddZ ej ddddd Z ejdddZej dddZdS)CortexInputSpecTzhemisphere / lobe label volumez-h %s)r r r zOoutput structure mask. If unspecified, output file name will be auto generated.z-o %s)r r rz#tissue fraction file (32-bit float)z-f %sgI@z&tissue fraction threshold (percentage)z-p %f)rr r zcompute WM/GM boundaryz-wzcompute GM/CSF boundaryz-g)r r z(include all subcortical areas in WM maskz-azverbosity levelz-v %dztiming functionz--timerN)rrrrinputHemisphereLabelFileoutputCerebrumMaskinputTissueFractionFilerrZtissueFractionThresholdrZcomputeWGBoundaryZcomputeGCBoundaryZincludeAllSubcorticalAreasrrTr!r"r"r"r#r^s.  r^c@seZdZeddZdS)CortexOutputSpeczpath/name of cerebrum mask)r N)rrrrr`r"r"r"r#rbsrbc@s,eZdZdZeZeZdZddZ ddZ dS)CortexaC cortex extractor This program produces a cortical mask using tissue fraction estimates and a co-registered cerebellum/hemisphere mask. http://brainsuite.org/processing/surfaceextraction/cortex/ Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> cortex = brainsuite.Cortex() >>> cortex.inputs.inputHemisphereLabelFile = example_data('mask.nii') >>> cortex.inputs.inputTissueFractionFile = example_data('tissues.nii.gz') >>> results = cortex.run() #doctest: +SKIP ZcortexcCs@|jj}t||r&tjj||S|dkr>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> scrubmask = brainsuite.Scrubmask() >>> scrubmask.inputs.inputMaskFile = example_data('mask.nii') >>> results = scrubmask.run() #doctest: +SKIP Z scrubmaskcCs@|jj}t||r&tjj||S|dkr>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> tca = brainsuite.Tca() >>> tca.inputs.inputMaskFile = example_data('mask.nii') >>> results = tca.run() #doctest: +SKIP ZtcacCs@|jj}t||r&tjj||S|dkr>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> dewisp = brainsuite.Dewisp() >>> dewisp.inputs.inputMaskFile = example_data('mask.nii') >>> results = dewisp.run() #doctest: +SKIP ZdewispcCs@|jj}t||r&tjj||S|dkrz%f greater_than less_thanequal_tozTo avoid throwing a UserWarning, set tessellationThreshold first. Then set this attribute. Usage: tessellate voxels greater_than, less_than, or equal_to z%stessellationThresholdr)r r rrrequirespositionz*zero-pad volume (avoids clipping at edges)z-zzdo not compute vertex normalsz --nonormalszsmooth vertices after coloringz --postsmoothzverbosity (0 = quiet)z-v %dztiming functionz--timerN)rprq)rprq)rrrrinputVolumeFileoutputSurfaceFileZinputShadingVolumerrZsmoothingIterationsrZsmoothingConstantZcurvatureWeightingZscalingPercentilerrprvrCrqZ zeroPadFlagZ noNormalsFlagZpostSmoothFlagrTr!r"r"r"r#rnsL    rnc@seZdZeddZdS) DfsOutputSpeczpath/name of surface file)r N)rrrrr{r"r"r"r#r|sr|cs<eZdZdZeZeZdZfddZ ddZ ddZ Z S) Dfsa Surface Generator Generates mesh surfaces using an isosurface algorithm. http://brainsuite.org/processing/surfaceextraction/inner-cortical-surface/ Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> dfs = brainsuite.Dfs() >>> dfs.inputs.inputVolumeFile = example_data('structural.nii') >>> results = dfs.run() #doctest: +SKIP dfscsd|dkr dS|dkrP|jj}|jdjd|djd|djd|d|Stt|j|||S)Nrvrqz-gt %fz-lt %fz-eq %f)rsrtru)r&rvr joinrKr}rL)r,r-rMrN threshold)rOr"r#rLs  zDfs._format_argcCs@|jj}t||r&tjj||S|dkr>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> pialmesh = brainsuite.Pialmesh() >>> pialmesh.inputs.inputSurfaceFile = 'input_mesh.dfs' >>> pialmesh.inputs.inputTissueFractionFile = 'frac_file.nii.gz' >>> pialmesh.inputs.inputMaskFile = example_data('mask.nii') >>> results = pialmesh.run() #doctest: +SKIP ZpialmeshcCs@|jj}t||r&tjj||S|dkr>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> hemisplit = brainsuite.Hemisplit() >>> hemisplit.inputs.inputSurfaceFile = 'input_surf.dfs' >>> hemisplit.inputs.inputHemisphereLabelFile = 'label.nii' >>> hemisplit.inputs.pialSurfaceFile = 'pial.dfs' >>> results = hemisplit.run() #doctest: +SKIP Z hemisplitcCsR|jj}t||r&tjj||Sddddd}||krNt|jj||SdS)Nz.left.inner.cortex.dfsz.left.pial.cortex.dfsz.right.inner.cortex.dfsz.right.pial.cortex.dfs)rrrr)r&r'rr(r)r*r+r)r,r-r&r.r"r"r#r/s  zHemisplit._gen_filenamecCst|S)N)r0)r,r"r"r#r1szHemisplit._list_outputsN) rrrr2rr3rr4r5r/r1r"r"r"r#rs rc@seZdZeddddZeddddZedddd Zejd d d Z ejd dd Z ejddd Z ej ddd Z ejddd Zejddd Zejddd Zejddd Zejddd Zejddd ZdS) SkullfinderInputSpecTz input filez-i %s)r r r z7A brain mask file, 8-bit image (0=non-brain, 255=brain)z-m %szoutput multi-colored label volume segmenting brain, scalp, inner skull & outer skull If unspecified, output file name will be auto generated.z-o %s)r r rrTz-v %d)r r z Lower threshold for segmentationz-l %dz Upper threshold for segmentationz-u %dz@if specified, generate surface files for brain, skull, and scalpz-s %szbackground label value (0-255)z --bglabel %dzscalp label value (0-255)z--scalplabel %dzskull label value (0-255)z--skulllabel %dzspace label value (0-255)z--spacelabel %dzbrain label value (0-255)z--brainlabel %dz3perform a final opening operation on the scalp maskz--finalOpeningN)rrrrrr@rRrrrTZlowerThresholdZupperThresholdrFZsurfaceFilePrefixZ bgLabelValueZscalpLabelValueZskullLabelValueZspaceLabelValueZbrainLabelValuerZperformFinalOpeningr"r"r"r#rs8     rc@seZdZeddZdS)SkullfinderOutputSpeczpath/name of label file)r N)rrrrrRr"r"r"r#rsrc@s,eZdZdZeZeZdZddZ ddZ dS) Skullfindera Skull and scalp segmentation algorithm. Examples -------- >>> from nipype.interfaces import brainsuite >>> from nipype.testing import example_data >>> skullfinder = brainsuite.Skullfinder() >>> skullfinder.inputs.inputMRIFile = example_data('structural.nii') >>> skullfinder.inputs.inputMaskFile = example_data('mask.nii') >>> results = skullfinder.run() #doctest: +SKIP Z skullfindercCs@|jj}t||r&tjj||S|dkr and the sulcal numbers <#sul> to be used as constraints. example: curveMatchingInstructions = "subbasename.right.dfc 1 2 20"z'-C'zThe cerebrum mask will be used for masking the final labels instead of the default pial surface mask. Every voxel will be labeled within the cerebrum mask regardless of the boundaries of the pial surface.z'-D %d'aCortical volume labels found in file output subbasename.svreg.label.nii.gz find its boundaries by using the pial surface then dilating by 1 voxel. Use this flag in order to control the number of pial surface mask dilation. (ie. -D 0 will assign no voxel dilation)z'-k'zAKeep the intermediate files after the svreg sequence is complete. verbosity0 verbosity1 verbosity2z'-v0'zno messages will be reported)r rrr z'-v1'zFmessages will be reported but not the iteration-wise detailed messagesz'v2'z>> from nipype.interfaces import brainsuite >>> svreg = brainsuite.SVReg() >>> svreg.inputs.subjectFilePrefix = 'home/user/btestsubject/testsubject' >>> svreg.inputs.refineOutputs = True >>> svreg.inputs.skipToVolumeReg = False >>> svreg.inputs. keepIntermediates = True >>> svreg.inputs.verbosity2 = True >>> svreg.inputs.displayTimestamps = True >>> svreg.inputs.useSingleThreading = True >>> results = svreg.run() #doctest: +SKIP zsvreg.shcsP|dks|dks|dkr*|jtjj|S|dkr<|jdStt|j|||S)Nrrrrr)r r(r) expanduserrKrrL)r,r-rMrN)rOr"r#rLs zSVReg._format_arg) rrrr2rr3r5rLrPr"r")rOr#rsrc @seZdZeddddgddZejddddgd dZed ddd d ZedddgdddZ ej ej d d dddgdddZ ej ej dddZ ejdddddddd dZejd!d"dZejd#d$dZejd%d&dZejd'd(dZejd)d*dZejd+d,dZejd-d.dZed/d0dZed1d2dZejd3d4d5d6d7dZejd3d8d9d:d5d;ddZejd?d@dZejdAdBdZejdCdDgdEdFZedGdHgdIdJZ ejdKdLdMgdNdOdPZ!ejdQdRdZ"ejdSdTdZ#edUdVdZ$edWdXdZ%ejdYdZd[d\d]d^dZ&ed_d`dZ'edadbdZ(ejdcdddZ)ejdedfdZ*ejdgdhdZ+ejdidjdZ,edkdldZ-edmdndZ.edodpdZ/ej dqdrdZ0ejdsdtdZ1edudvdZ2ej dwdxdZ3ej4dydzdZ5ejd{d|dZ6ejd}d~dZ7dS) BDPInputSpecz%sTrnoStructuralRegistrationzqSpecify absolute path to file produced by bfc. By default, bfc produces the file in the format: prefix.bfc.nii.gz)r r rxrrr z--no-structural-registrationbfcFileaQAllows BDP to work without any structural input. This can useful when one is only interested in diffusion modelling part of BDP. With this flag only fieldmap-based distortion correction is supported. outPrefix can be used to specify fileprefix of the output filenames. Change dwiMask to define region of interest for diffusion modelling.z--nii %srawSpecifies the absolute path and filename of the input diffusion data in 4D NIfTI-1 format. The flag must be followed by the filename. Only NIfTI-1 files with extension .nii or .nii.gz are supported. Furthermore, either bMatrixFile, or a combination of both bValueFile and diffusionGradientFile must be used to provide the necessary b-matrices/b-values and gradient vectors. )r r rxr z --bmat %s BVecBValPairraSpecifies the absolute path and filename of the file containing b-matrices for diffusion-weighted scans. The flag must be followed by the filename. This file must be a plain text file containing 3x3 matrices for each diffusion encoding direction. It should contain zero matrices corresponding to b=0 images. This file usually has ".bmat" as its extension, and can be used to provide BDP with the more-accurate b-matrices as saved by some proprietary scanners. The b-matrices specified by the file must be in the voxel coordinates of the input diffusion weighted image (NIfTI file). In case b-matrices are not known/calculated, bvec and .bval files can be used instead (see diffusionGradientFile and bValueFile). )r r rrrxr bMatrixFilez--bvec %s --bval %saMust input a list containing first the BVector file, then the BValue file (both must be absolute paths) Example: bdp.inputs.BVecBValPair = ['/directory/subdir/prefix.dwi.bvec', '/directory/subdir/prefix.dwi.bval'] The first item in the list specifies the filename of the file containing b-values for the diffusion scan. The b-value file must be a plain-text file and usually has an extension of .bval The second item in the list specifies the filename of the file containing the diffusion gradient directions (specified in the voxel coordinates of the input diffusion-weighted image)The b-vectors file must be a plain text file and usually has an extension of .bvec )Zminlenmaxlenr rxrrr r aYFor use in parallel processing workflows including Brainsuite Cortical Surface Extraction sequence. Connect datasink out_file to dataSinkDelay to delay execution of BDP until dataSink has finished sinking outputs. In particular, BDP may be run after BFC has finished. For more information see http://brainsuite.org/processing/diffusion/pipeline/)r r xzx-yzy-zzz-z--dir=%saSpecifies the phase-encoding direction of the EPI (diffusion) images. It is same as the dominant direction of distortion in the images. This information is used to constrain the distortion correction along the specified direction. Directions are represented by any one of x, x-, y, y-, z or z-. "x" direction increases towards the right side of the subject, while "x-" increases towards the left side of the subject. Similarly, "y" and "y-" are along the anterior-posterior direction of the subject, and "z" & "z-" are along the inferior-superior direction. When this flag is not used, BDP uses "y" as the default phase-encoding direction. z--echo-spacing=%fzSets the echo spacing to t seconds, which is used for fieldmap-based distortion correction. This flag is required when using fieldmapCorrectionz--bval-ratio-threshold %faSets a threshold which is used to determine b=0 images. When there are no diffusion weighted image with b-value of zero, then BDP tries to use diffusion weighted images with a low b-value in place of b=0 image. The diffusion images with minimum b-value is used as b=0 image only if the ratio of the maximum and minimum b-value is more than the specified threshold. A lower value of threshold will allow diffusion images with higher b-value to be used as b=0 image. The default value of this threshold is set to 45, if this trait is not set. z --tensorsaEstimates diffusion tensors using a weighted log-linear estimation and saves derived diffusion tensor parameters (FA, MD, axial, radial, L2, L3). This is the default behavior if no diffusion modeling flags are specified. The estimated diffusion tensors can be visualized by loading the saved ``*.eig.nii.gz`` file in BrainSuite. BDP reports diffusivity (MD, axial, radial, L2 and L3) in a unit which is reciprocal inverse of the unit of input b-value. z--FRACTzEstimates ODFs using the Funk-Radon and Cosine Transformation (FRACT). The outputs are saved in a separate directory with name "FRACT" and the ODFs can be visualized by loading the saved ".odf" file in BrainSuite. z--FRTa#Estimates ODFs using Funk-Radon Transformation (FRT). The coefficient maps for ODFs are saved in a separate directory with name "FRT" and the ODFs can be visualized by loading the saved ".odf" file in BrainSuite. The derived generalized-FA (GFA) maps are also saved in the output directory. z --3dshore --diffusion_time_ms %fz;Estimates ODFs using 3Dshore. Pass in diffusion time, in msz--odf-lambda zSets the regularization parameter, lambda, of the Laplace-Beltrami operator while estimating ODFs. The default value is set to 0.006 . This can be used to set the appropriate regularization for the input diffusion data. z --t1-mask %saSpecifies the filename of the brain-mask file for input T1-weighted image. This mask can be same as the brain mask generated during BrainSuite extraction sequence. For best results, the mask should not include any extra-meningial tissues from T1-weighted image. The mask must be in the same coordinates as input T1-weighted image (i.e. should overlay correctly with input .bfc.nii.gz file in BrainSuite). This mask is used for co-registration and defining brain boundary for statistics computation. The mask can be generated and/or edited in BrainSuite. In case outputDiffusionCoordinates is also used, this mask is first transformed to diffusion coordinate and the transformed mask is used for defining brain boundary in diffusion coordinates. When t1Mask is not set, BDP will try to use fileprefix>.mask.nii.gz as brain-mask. If .mask.nii.gz is not found, then BDP will use the input .bfc.nii.gz itself as mask (i.e. all non-zero voxels in .bfc.nii.gz is assumed to constitute brain mask). z --dwi-mask %saMSpecifies the filename of the brain-mask file for diffusion data. This mask is used only for co-registration purposes and can affect overall quality of co-registration (see t1Mask for definition of brain mask for statistics computation). The mask must be a 3D volume and should be in the same coordinates as input Diffusion file/data (i.e. should overlay correctly with input diffusion data in BrainSuite). For best results, the mask should include only brain voxels (CSF voxels around brain is also acceptable). When this flag is not used, BDP will generate a pseudo mask using first b=0 image volume and would save it as fileprefix>.dwi.RSA.mask.nii.gz. In case co-registration is not accurate with automatically generated pseudo mask, BDP should be re-run with a refined diffusion mask. The mask can be generated and/or edited in BrainSuite. ZMIZ INVERSIONBDPz--rigid-reg-measure %sa Defines the similarity measure to be used for rigid registration. Possible measures are "MI", "INVERSION" and "BDP". MI measure uses normalized mutual information based cost function. INVERSION measure uses simpler cost function based on sum of squared difference by exploiting the approximate inverse-contrast relationship in T1- and T2-weighted images. BDP measure combines MI and INVERSION. It starts with INVERSION measure and refines the result with MI measure. BDP is the default measure when this trait is not set. z INVERSION-EPIz INVERSION-T1zINVERSION-BOTHz--dcorr-reg-method %saDefines the method for registration-based distortion correction. Possible methods are "MI", "INVERSION-EPI", "INVERSION-T1", INVERSION-BOTH", and "BDP". MI method uses normalized mutual information based cost-function while estimating the distortion field. INVERSION-based method uses simpler cost function based on sum of squared difference by exploiting the known approximate contrast relationship in T1- and T2-weighted images. T2-weighted EPI is inverted when INVERSION-EPI is used; T1-image is inverted when INVERSION-T1 is used; and both are inverted when INVERSION-BOTH is used. BDP method add the MI-based refinement after the correction using INVERSION-BOTH method. BDP is the default method when this trait is not set. z--dcorr-regularization-wt %faSets the (scalar) weighting parameter for regularization penalty in registration-based distortion correction. Set this trait to a single, non-negative number which specifies the weight. A large regularization weight encourages smoother distortion field at the cost of low measure of image similarity after distortion correction. On the other hand, a smaller regularization weight can result into higher measure of image similarity but with unrealistic and unsmooth distortion field. A weight of 0.5 would reduce the penalty to half of the default regularization penalty (By default, this weight is set to 1.0). Similarly, a weight of 2.0 would increase the penalty to twice of the default penalty. z--no-distortion-correctionzSkips distortion correction completely and performs only a rigid registration of diffusion and T1-weighted image. This can be useful when the input diffusion images do not have any distortion or they have been corrected for distortion. z--no-nonuniformity-correctionzSkips intensity non-uniformity correction in b=0 image for registration-based distortion correction. The intensity non-uniformity correction does not affect any diffusion modeling. z--no-intensity-correctionfieldmapCorrectionMethodzDisables intensity correction when performing distortion correction. Intensity correction can change the noise distribution in the corrected image, but it does not affect estimated diffusion parameters like FA, etc. )r rrr z--fieldmap-correction %s echoSpacingaUse an acquired fieldmap for distortion correction. The fieldmap must have units of radians/second. Specify the filename of the fieldmap file. The field of view (FOV) of the fieldmap scan must cover the FOV of the diffusion scan. BDP will try to check the overlap of the FOV of the two scans and will issue a warning/error if the diffusion scan"s FOV is not fully covered by the fieldmap"s FOV. BDP uses all of the information saved in the NIfTI header to compute the FOV. If you get this error and think that it is incorrect, then it can be suppressed using the flag ignore-fieldmap-FOV. Neither the image matrix size nor the imaging grid resolution of the fieldmap needs to be the same as that of the diffusion scan, but the fieldmap must be pre-registred to the diffusion scan. BDP does NOT align the fieldmap to the diffusion scan, nor does it check the alignment of the fieldmap and diffusion scans. Only NIfTI files with extension of .nii or .nii.gz are supported. Fieldmap-based distortion correction also requires the echoSpacing. Also fieldmapCorrectionMethod allows you to define method for distortion correction. least squares is the default method. )r rwr Z pixelshiftZleastsqskipIntensityCorrz--fieldmap-correction-method %saDefines the distortion correction method while using fieldmap. Possible methods are "pixelshift" and "leastsq". leastsq is the default method when this flag is not used. Pixel-shift (pixelshift) method uses image interpolation to un-distort the distorted diffusion images. Least squares (leastsq) method uses a physical model of distortion which is more accurate (and more computationally expensive) than pixel-shift method.)rrr r z--ignore-fieldmap-fovzSupresses the error generated by an insufficient field of view of the input fieldmap and continues with the processing. It is useful only when used with fieldmap-based distortion correction. See fieldmap-correction for a detailed explanation. z--fieldmap-smooth3=%fzApplies 3D Gaussian smoothing with a standard deviation of S millimeters (mm) to the input fieldmap before applying distortion correction. This trait is only useful with fieldmapCorrection. Skip this trait for no smoothing. z--transform-diffusion-volume %saThis flag allows to define custom volumes in diffusion coordinate which would be transformed into T1 coordinate in a rigid fashion. The flag must be followed by the name of either a NIfTI file or of a folder that contains one or more NIfTI files. All of the files must be in diffusion coordinate, i.e. the files should overlay correctly with the diffusion scan in BrainSuite. Only NIfTI files with an extension of .nii or .nii.gz are supported. The transformed files are written to the output directory with suffix ".T1_coord" in the filename and will not be corrected for distortion, if any. The trait transformInterpolation can be used to define the type of interpolation that would be used (default is set to linear). If you are attempting to transform a label file or mask file, use "nearest" interpolation method with transformInterpolation. See also transformT1Volume and transformInterpolationz--transform-t1-volume %saSame as transformDiffusionVolume except that files specified must be in T1 coordinate, i.e. the files should overlay correctly with the input .bfc.nii.gz files in BrainSuite. BDP transforms these data/images from T1 coordinate to diffusion coordinate. The transformed files are written to the output directory with suffix ".D_coord" in the filename. See also transformDiffusionVolume and transformInterpolation. ZlinearZnearestZcubicZsplinez--transform-interpolation %szDefines the type of interpolation method which would be used while transforming volumes defined by transformT1Volume and transformDiffusionVolume. Possible methods are "linear", "nearest", "cubic" and "spline". By default, "linear" interpolation is used. z--transform-t1-surface %saSimilar to transformT1Volume, except that this flag allows transforming surfaces (instead of volumes) in T1 coordinate into diffusion coordinate in a rigid fashion. The flag must be followed by the name of either a .dfs file or of a folder that contains one or more dfs files. All of the files must be in T1 coordinate, i.e. the files should overlay correctly with the T1-weighted scan in BrainSuite. The transformed files are written to the output directory with suffix D_coord" in the filename. z --transform-diffusion-surface %sa7Same as transformT1Volume, except that the .dfs files specified must be in diffusion coordinate, i.e. the surface files should overlay correctly with the diffusion scan in BrainSuite. The transformed files are written to the output directory with suffix ".T1_coord" in the filename. See also transformT1Volume. z--transform-data-onlyaSkip all of the processing (co-registration, distortion correction and tensor/ODF estimation) and directly start transformation of defined custom volumes, mask and labels (using transformT1Volume, transformDiffusionVolume, transformT1Surface, transformDiffusionSurface, customDiffusionLabel, customT1Label). This flag is useful when BDP was previously run on a subject (or ) and some more data (volumes, mask or labels) need to be transformed across the T1-diffusion coordinate spaces. This assumes that all the necessary files were generated earlier and all of the other flags MUST be used in the same way as they were in the initial BDP run that processed the data. z--generate-statsaGenerate ROI-wise statistics of estimated diffusion tensor parameters. Units of the reported statistics are same as that of the estimated tensor parameters (see estimateTensors). Mean, variance, and voxel counts of white matter(WM), grey matter(GM), and both WM and GM combined are written for each estimated parameter in a separate comma-seperated value csv) file. BDP uses the ROI labels generated by Surface-Volume Registration (SVReg) in the BrainSuite extraction sequence. Specifically, it looks for labels saved in either fileprefix>.svreg.corr.label.nii.gz or .svreg.label.nii.gz. In case both files are present, only the first file is used. Also see customDiffusionLabel and customT1Label for specifying your own ROIs. It is also possible to forgo computing the SVReg ROI-wise statistics and only compute stats with custom labels if SVReg label is missing. BDP also transfers (and saves) the label/mask files to appropriate coordinates before computing statistics. Also see outputDiffusionCoordinates for outputs in diffusion coordinate and forcePartialROIStats for an important note about field of view of diffusion and T1-weighted scans. z--generate-only-statsaSkip all of the processing (co-registration, distortion correction and tensor/ODF estimation) and directly start computation of statistics. This flag is useful when BDP was previously run on a subject (or fileprefix>) and statistics need to be (re-)computed later. This assumes that all the necessary files were generated earlier. All of the other flags MUST be used in the same way as they were in the initial BDP run that processed the data. z--force-partial-roi-statsa=The field of view (FOV) of the diffusion and T1-weighted scans may differ significantly in some situations. This may result in partial acquisitions of some ROIs in the diffusion scan. By default, BDP does not compute statistics for partially acquired ROIs and shows warnings. This flag forces computation of statistics for all ROIs, including those which are partially acquired. When this flag is used, number of missing voxels are also reported for each ROI in statistics files. Number of missing voxels are reported in the same coordinate system as the statistics file. z--custom-diffusion-label %saBDP supports custom ROIs in addition to those generated by BrainSuite SVReg) for ROI-wise statistics calculation. The flag must be followed by the name of either a file (custom ROI file) or of a folder that contains one or more ROI files. All of the files must be in diffusion coordinate, i.e. the label files should overlay correctly with the diffusion scan in BrainSuite. These input label files are also transferred (and saved) to T1 coordinate for statistics in T1 coordinate. BDP uses nearest-neighborhood interpolation for this transformation. Only NIfTI files, with an extension of .nii or .nii.gz are supported. In order to avoid confusion with other ROI IDs in the statistic files, a 5-digit ROI ID is generated for each custom label found and the mapping of ID to label file is saved in the file fileprefix>.BDP_ROI_MAP.xml. Custom label files can also be generated by using the label painter tool in BrainSuite. See also customLabelXMLz--custom-t1-label %saSame as customDiffusionLabelexcept that the label files specified must be in T1 coordinate, i.e. the label files should overlay correctly with the T1-weighted scan in BrainSuite. If the trait outputDiffusionCoordinates is also used then these input label files are also transferred (and saved) to diffusion coordinate for statistics in diffusion coordinate. BDP uses nearest-neighborhood interpolation for this transformation. See also customLabelXML. z--custom-label-xml %saBrainSuite saves a descriptions of the SVReg labels (ROI name, ID, color, and description) in an .xml file brainsuite_labeldescription.xml). BDP uses the ROI ID"s from this xml file to report statistics. This flag allows for the use of a custom label description xml file. The flag must be followed by an xml filename. This can be useful when you want to limit the ROIs for which you compute statistics. You can also use custom xml files to name your own ROIs (assign ID"s) for custom labels. BrainSuite can save a label description in .xml format after using the label painter tool to create a ROI label. The xml file MUST be in the same format as BrainSuite"s label description file (see brainsuite_labeldescription.xml for an example). When this flag is used, NO 5-digit ROI ID is generated for custom label files and NO Statistics will be calculated for ROIs not identified in the custom xml file. See also customDiffusionLabel and customT1Label.z--output-subdir %saBy default, BDP writes out all the output (and intermediate) files in the same directory (or folder) as the BFC file. This flag allows to specify a sub-directory name in which output (and intermediate) files would be written. BDP will create the sub-directory in the same directory as BFC file. should be the name of the sub-directory without any path. This can be useful to organize all outputs generated by BDP in a separate sub-directory. z--output-diffusion-coordinateaEnables estimation of diffusion tensors and/or ODFs (and statistics if applicable) in the native diffusion coordinate in addition to the default T1-coordinate. All native diffusion coordinate files are saved in a seperate folder named "diffusion_coord_outputs". In case statistics computation is required, it will also transform/save all label/mask files required to diffusion coordinate (see generateStats for details). z--flag-conf-file %saUses the defined file to specify BDP flags which can be useful for batch processing. A flag configuration file is a plain text file which can contain any number of BDP"s optional flags (and their parameters) separated by whitespace. Everything coming after # until end-of-line is treated as comment and is ignored. If a flag is defined in configuration file and is also specified in the command used to run BDP, then the later get preference and overrides the definition in configuration file. z--output-fileprefix %saSpecifies output fileprefix when noStructuralRegistration is used. The fileprefix can not start with a dash (-) and should be a simple string reflecting the absolute path to desired location, along with outPrefix. When this flag is not specified (and noStructuralRegistration is used) then the output files have same file-base as the input diffusion file. This trait is ignored when noStructuralRegistration is not used. z --threads=%dzSets the number of parallel process threads which can be used for computations to N, where N must be an integer. Default value of N is z --low-memoryzActivates low-memory mode. This will run the registration-based distortion correction at a lower resolution, which could result in a less-accurate correction. This should only be used when no other alternative is available. z--ignore-memoryaDeactivates the inbuilt memory checks and forces BDP to run registration-based distortion correction at its default resolution even on machines with a low amount of memory. This may result in an out-of-memory error when BDP cannot allocate sufficient memory. Nryry)8rrrrrrrrZinputDiffusionDatarrrFrrrCZphaseEncodingDirectionrrZbValRatioThresholdZestimateTensorsZestimateODF_FRACTZestimateODF_FRTZestimateODF_3DShoreZ odfLambtaZt1MaskZdwiMaskZrigidRegMeasureZdcorrRegMeasureZ dcorrWeightZskipDistortionCorrZskipNonuniformityCorrrZfieldmapCorrectionrZignoreFieldmapFOVZfieldmapSmoothZtransformDiffusionVolumeZtransformT1VolumeZtransformInterpolationZtransformT1SurfaceZtransformDiffusionSurfaceZtransformDataOnlyZ generateStatsZ onlyStatsZforcePartialROIStatsZcustomDiffusionLabelZ customT1LabelZcustomLabelXMLZ outputSubdirZoutputDiffusionCoordinatesZflagConfigFileZ outPrefixrthreadsZ lowMemoryZ ignoreMemoryr"r"r"r#rs`           rcs(eZdZdZeZdZfddZZS)rav BrainSuite Diffusion Pipeline (BDP) enables fusion of diffusion and structural MRI information for advanced image and connectivity analysis. It provides various methods for distortion correction, co-registration, diffusion modeling (DTI and ODF) and basic ROI-wise statistic. BDP is a flexible and diverse tool which supports wide variety of diffusion datasets. For more information, please see: http://brainsuite.org/processing/diffusion/ Examples -------- >>> from nipype.interfaces import brainsuite >>> bdp = brainsuite.BDP() >>> bdp.inputs.bfcFile = '/directory/subdir/prefix.bfc.nii.gz' >>> bdp.inputs.inputDiffusionData = '/directory/subdir/prefix.dwi.nii.gz' >>> bdp.inputs.BVecBValPair = ['/directory/subdir/prefix.dwi.bvec', '/directory/subdir/prefix.dwi.bval'] >>> results = bdp.run() #doctest: +SKIP zbdp.shcsD|dkr|j|d|dfS|dkr0|jdStt|j|||S)Nrrrrr)r rKrrL)r,r-rMrN)rOr"r#rLs  zBDP._format_arg) rrrr2rr3r5rLrPr"r")rOr#rsrc@seZdZejddddZdS)ThicknessPVCInputSpecz%sTz5Absolute path and filename prefix of the subject data)r r r N)rrrrrFrr"r"r"r#rsrc@seZdZdZeZdZdS) ThicknessPVCa: ThicknessPVC computes cortical thickness using partial tissue fractions. This thickness measure is then transferred to the atlas surface to facilitate population studies. It also stores the computed thickness into separate hemisphere files and subject thickness mapped to the atlas hemisphere surfaces. ThicknessPVC is not run through the main SVReg sequence, and should be used after executing the BrainSuite and SVReg sequence. For more informaction, please see: http://brainsuite.org/processing/svreg/svreg_modules/ Examples -------- >>> from nipype.interfaces import brainsuite >>> thicknessPVC = brainsuite.ThicknessPVC() >>> thicknessPVC.inputs.subjectFilePrefix = 'home/user/btestsubject/testsubject' >>> results = thicknessPVC.run() #doctest: +SKIP zthicknessPVC.shN)rrrr2rr3r5r"r"r"r#rsrcCs:tjj|}tjd}|j|d}tjjdj||fS)Nz[^.]+rr)r(r)basenameregexcompilefindallr*r)Z inputNamesuffixZ fullInputZdotRegexZinputNoExtensionr"r"r#r+ s  r+cCs8|jj}x&|D]}|j|}|dk r|||<qW|S)N)r4r'r/)r,outputskeyr-r"r"r#r0s     r0)7r2r(rerbaserrrrrrr r$r%r6rIrJrQrUrVrWr\r]r^rbrcrdrerfrgrirjrkrlrmrnr|r}rrrrrrrrrrrrrrrr+r0r"r"r"r#sf 1 )];*+/ ''# )432&!,)"s)$