3 d}@sddlZddlmZddlmZmZmZmZmZm Z ddl m Z ddl m Z ddlmZmZmZmZddlmZmZGdd d eZGd d d eZGd d d e ZGdddeZGdddeZGddde ZGddde ZdS)N)Path)InputMultiPath TraitedSpectraits isdefinedFileStr)Cell) SPMCommand)SPMCommandInputSpec ImageFileSPMscans_for_fnamesscans_for_fname)split_filenamefname_presuffixc @seZdZeedddddddZdZeedddedddZd Zedd ed ddd Z eded dddZ ededdddZ ededdddZ ededdddZ ejd dddddZdZedddedZdZejdddedZdZejddeddZd Zejd d!eddZd"Zejdd#ded$Zd%Zejdd&eddZd'Zejd d(eddZ d)Z!ejd*d+e!ddZ"d,Z#ej$ejd ejd-dddd.d/d0Z%d1Z&ejd d2e&ddZ'd3Z(ejd d4e(ddZ)ejd d5dd6d$Z*ej+dd7dd8d$Z,ej+dd9dd:d$Z-ej+dd;ddd$Z/eeddd?d@dddZ0ej+ddAddBd$Z1ej+ddCddDd$Z2ej+ddEddFd$Z3dGZ4ej+ddHddId$Z5ej+ddJddId$Z6ej+ddKddId$Z7dLZ8ej+ddMddNd$Z9ej+ddOddNd$Z:ej+ddPddNd$Z;dQZej+ddVdeTemplate (forward): [1 0]; Template->Image (inverse): [0 1]; inverse + forward: [1 1]z output.warps)rr rrrN)J__name__ __module__ __qualname__rr in_filesZ _help_tpmrZ_help_shoots_tpm shooting_tpmZshooting_tpm_template_1Zshooting_tpm_template_2Zshooting_tpm_template_3Zshooting_tpm_template_4rIntZn_jobsZ_help_affine_regrZaffine_regularizationZ_help_bias_accFloatZ"power_spm_inhomogeneity_correctionZ _help_appZaffine_preprocessingZ_help_initial_segZinitial_segmentationZ _help_lasZlocal_adaptive_segZ _help_gcutstrZ skull_stripZ _help_wmhcZwm_hyper_intensity_correctionZ _help_voxZ voxel_sizeZ_help_resamplingTupleZinternal_resampling_processZ _errors_help ignore_errorsZ _help_surfZ surface_and_thickness_estimationZsurface_measuresBoolZneuromorphometricsZlpba40ZcobraZhammersZ own_atlasZgm_output_nativeZgm_output_modulatedZgm_output_dartelZ_wm_descZwm_output_nativeZwm_output_modulatedZwm_output_dartelZ _csf_descZcsf_output_nativeZcsf_output_modulatedZcsf_output_dartelZ_help_label_descZ label_nativeZ label_warpedZ label_dartelZoutput_labelnativesave_bias_correctedZ _las_descZ las_nativeZ las_warpedZ las_dartelZ_help_jacobianZjacobianwarpedZ _help_warpZwarpsr.r.D/tmp/pip-build-7vycvbft/nipype/nipype/interfaces/cat12/preprocess.pyrsx        rc@s(eZdZejeddddZedddZedddZejeddddZ edd dZ edd dZ edd dZ edd dZ edd dZedddZedddZedddZedddZedddZejeddddZedddZedddZedddZedddZejeddddZedddZdS)CAT12SegmentOutputSpecT)rz2Files with the measures extracted for OI ands ROIs)rz$Files with thickness values of ROIs.)rrz#Files with thickness values of ROI.zDifferent segmented images.zGrey matter modulated image.zGrey matter dartel image.zGrey matter native space.zWhite matter modulated image.zWhite matter dartel image.zWhite matter in native space.zCSF modulated image.zCSF dartel image.zCSF in native space.zBias corrected imagez Surface fileszCentral right hemisphere fileszSphere right hemisphere fileszCentral left hemisphere fileszSphere left hemisphere filesz Report files.z Report file.N)r#r$r%rListr label_files label_rois label_roi mri_imagesZgm_modulated_imageZgm_dartel_imageZgm_native_imageZwm_modulated_imageZwm_dartel_imageZwm_native_imageZcsf_modulated_imageZcsf_dartel_imageZcsf_native_imagebias_corrected_image surface_filesZrh_central_surfaceZrh_sphere_surfaceZlh_central_surfaceZlh_sphere_surface report_filesreportr.r.r.r/r0s,                r0cs8eZdZdZeZeZddZfddZ ddZ Z S) CAT12Segmentap CAT12: Segmentation This toolbox is an extension to the default segmentation in SPM12, but uses a completely different segmentation approach. The segmentation approach is based on an Adaptive Maximum A Posterior (MAP) technique without the need for a priori information about tissue probabilities. That is, the Tissue Probability Maps (TPM) are not used constantly in the sense of the classical Unified Segmentation approach (Ashburner et. al. 2005), but just for spatial normalization. The following AMAP estimation is adaptive in the sense that local variations of the parameters (i.e., means and variance) are modeled as slowly varying spatial functions (Rajapakse et al. 1997). This not only accounts for intensity inhomogeneities but also for other local variations of intensity. Additionally, the segmentation approach uses a Partial Volume Estimation (PVE) with a simplified mixed model of at most two tissue types (Tohka et al. 2004). We start with an initial segmentation into three pure classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) based on the above described AMAP estimation. The initial segmentation is followed by a PVE of two additional mixed classes: GM-WM and GM-CSF. This results in an estimation of the amount (or fraction) of each pure tissue type present in every voxel (as single voxels - given by Another important extension to the SPM12 segmentation is the integration of the Dartel or Geodesic Shooting registration into the toolbox by an already existing Dartel/Shooting template in MNI space. This template was derived from 555 healthy control subjects of the IXI-database (http://www.brain-development.org) and provides the several Dartel or Shooting iterations. Thus, for the majority of studies the creation of sample-specific templates is not necessary anymore and is mainly recommended for children data.'}; http://www.neuro.uni-jena.de/cat12/CAT12-Manual.pdf#page=15 Examples -------- >>> path_mr = 'structural.nii' >>> cat = CAT12Segment(in_files=path_mr) >>> cat.run() # doctest: +SKIP cKs2tj}|r d|kr d|_d|_tj|f|dS)Nz12.toolsz cat.estwrite)r version_jobtype_jobname__init__)selfinputs_local_versionr.r.r/r?s  zCAT12Segment.__init__csH|dkr$t|trt|St|Sn|dkr4t|Stt|j|||S)z+Convert input to appropriate format for spmr&rr')rr') isinstancelistr rCell2Strsuperr: _format_arg)r@optspecval) __class__r.r/rGs  zCAT12Segment._format_argc Cs*|jj}|jjd}t|\}}}ddt|jdD|d<xtdddgD]\}}xd8D]\}} |d|} tt |j| r`t |j| r`|d|d} t j j d| d|d} |d krt || d|| <q`t || dd|| <q`WqRW|jjrt |t j j ddd|d<ddt|jdD|d<xXd9D]P} xHd:D]@} | d| d!} t |t j j d"| d#| d#d$d%d&|| <qBWq8Wd'dt|jd(D|d)<|d)<t |t j j d*d+d,d%d&|d*<d-dt|jd.D|d/<t |t j j d0d1d,d%d&|d2<t |t j j d0d3d,d%d&|d4<|S);NrcSsg|]}|jrt|qSr.)is_filestr).0mrir.r.r/ sz.CAT12Segment._list_outputs..zmri/*r5ZgmZwmZcsf modulatedmwr"rr!Z_output__Z_imagerOpr)prefixZ_rigid)rWsuffixZwmir6cSsg|]}|jrt|qSr.)rLrM)rNsurfr.r.r/rP1szsurf/*r7rhlhcentralsphereZ_surfacerY.z.giiF)rWrXZuse_extcSsg|]}|jrt|qSr.)rLrM)rNr9r.r.r/rP?szreport/*r8r9Zcat_z.xmlcSsg|]}|jrt|qSr.)rLrM)rNlabelr.r.r/rPGszlabel/*r2r_ZcatROIs_r3ZcatROI_r4rQrRr"rSr!rT)r`rarb)rZr[)r\r])_outputsgetrAr&rrglob enumeratergetattrospathjoinrr-)r@outputsfpthbaseextZtidxZtissueimagerWZouttypeZoutfieldZ hemisphererXr.r.r/ _list_outputssH      $zCAT12Segment._list_outputs) r#r$r%__doc__r input_specr0 output_specr?rGrq __classcell__r.r.)rKr/r:s  r:c @seZdZeedddddddZejddd d d dd d Zej dddddZ ej dddddZ ej dddddZ ejdddddZejddddd Zejddddd Zejd d!d"d#dd$d Zd%S)&CAT12SANLMDenoisingInputSpecT)rrzImages for filtering.F)rrrrfloat32uint16uint8samespm_typezDData type of the output images. 'same' matches the input image type.)rrrintlimdz$intensity limitation (default = 100))rrrrrWZsanlm_zfFilename prefix. Specify the string to be prepended to the filenames of the filtered image file(s).rXrTzhFilename suffix. Specify the string to be appended to the filenames of the filtered image file(s).g?addnoisea Strength of additional noise in noise-free regions. Add minimal amount of noise in regions without any noise to avoid image segmentation problems. This parameter defines the strength of additional noise as percentage of the average signal intensity.)rrrrriciana'Rician noise MRIs can have Gaussian or Rician distributed noise with uniform or nonuniform variance across the image. If SNR is high enough (>3) noise can be well approximated by Gaussian noise in the foreground. However, for SENSE reconstruction or DTI data a Rician distribution is expected. Please note that the Rician noise estimation is sensitive for large signals in the neighbourhood and can lead to artefacts, e.g. cortex can be affected by very high values in the scalp or in blood vessels.ZreplaceNANandINFzJReplace NAN by 0, -INF by the minimum and INF by the maximum of the image.z-Infrrznlmfilter.optimized.NCstraStrength of Noise Corrections Strength of the (sub-resolution) spatial adaptive non local means (SANLM) noise correction. Please note that the filter strength is automatically estimated. Change this parameter only for specific conditions. The "light" option applies half of the filter strength of the adaptive "medium" cases, whereas the "strong" option uses the full filter strength, force sub-resolution filtering and applies an additional iteration. Sub-resolution filtering is only used in case of high image resolution below 0.8 mm or in case of the "strong" option. light = 2, medium = -Inf, strong = 4N)r#r$r%rr r&rEnumr{r(r|rfilename_prefixfilename_suffixr)r~r,rZreplace_nan_and_infZnoisecorr_strengthr.r.r.r/rvTsfrvc@seZdZeddZdS)CAT12SANLMDenoisingOutputSpeczout file)rN)r#r$r%rout_filer.r.r.r/rsrcs8eZdZdZeZeZddZfddZ ddZ Z S)CAT12SANLMDenoisinga Spatially adaptive non-local means (SANLM) denoising filter This function applies an spatial adaptive (sub-resolution) non-local means denoising filter to the data. This filter will remove noise while preserving edges. The filter strength is automatically estimated based on the standard deviation of the noise. This filter is internally used in the segmentation procedure anyway. Thus, it is not necessary (and not recommended) to apply the filter before segmentation. ______________________________________________________________________ Christian Gaser, Robert Dahnke Structural Brain Mapping Group (http://www.neuro.uni-jena.de) Departments of Neurology and Psychiatry Jena University Hospital ______________________________________________________________________ Examples -------- >>> from nipype.interfaces import cat12 >>> c = cat12.CAT12SANLMDenoising() >>> c.inputs.in_files = 'anatomical.nii' >>> c.run() # doctest: +SKIP cKs2tj}|r d|kr d|_d|_tj|f|dS)Nz12.r;zcat.tools.sanlm)r r<r=r>r?)r@rArBr.r.r/r?s  zCAT12SANLMDenoising.__init__csT|dkr"t|trt|St|S|dkr@ddddd}||}tt|j|||S)z+Convert input to appropriate format for spmr&r{rri)rzryrxrw)rCrDr rrFrrG)r@rHrIrJZtype_map)rKr.r/rGs zCAT12SANLMDenoising._format_argcCs8|jj}t|jjdtj|jj|jjd|d<|S)Nr)newpathrWrXr) rcrdrrAr&rhgetcwdrr)r@rkr.r.r/rqs  z!CAT12SANLMDenoising._list_outputs) r#r$r%rrrvrsrrtr?rGrqrur.r.)rKr/rs  rc@seZdZddZdS)rEcCs d|jS)z-Convert input to appropriate format for cat12z{'%s'})Z to_string)r@r.r.r/__str__szCell2Str.__str__N)r#r$r%rr.r.r.r/rEsrE)rhZpathlibrZnipype.interfaces.baserrrrrrZnipype.interfaces.cat12.baser Znipype.interfaces.spmr Znipype.interfaces.spm.baser r r rZnipype.utils.filemaniprrrr0r:rvrrrEr.r.r.r/s     4yU;