Normalize to MNI space (using SPM8)
Normalize to MNI space (using SPM8)Normaliser: Estimation et Ecrire Calcule la chaîne qui correspond le mieux enregistre une image source (ou d'une série d'images source) pour correspondre à un modèle, l'enregistrer dans le fichier imagename'_sn. Mat '. Cette option permet également le contenu des fichiers du mat imagename'_sn.; appliquer à une série d'images.
Normalise: Estimate and Write
Computes the warp that best registers a source image (or series of source images) to match a template, saving it to the file imagename'_sn.mat'.
This option also allows the contents of the imagename'_sn.mat' files to be applied to a series of images.
source: Volume 4D ( entrée )
imageToWrite: Volume 4D ( entrée )
warpedInMni: Volume 4D ( sortie )
template: Volume 4D ( entrée )
checkReg: Postscript file ( sortie )
wtsrc: String ( input )
weight: String ( input )
smosrc: String ( input )Source Image Smoothing : Smoothing to apply to a copy of the source image. The template and source images should have approximately the same smoothness. Remember that the templates supplied with SPM have been smoothed by 8mm, and that smoothnesses combine by Pythagoras' rule.
smoref: String ( input )Template Image Smoothing : Smoothing to apply to a copy of the template image. The template and source images should have approximately the same smoothness. Remember that the templates supplied with SPM have been smoothed by 8mm, and that smoothnesses combine by Pythagoras' rule.
regtype: String ( input )Affine Regularisation : Affine registration into a standard space can be made more robust by regularisation (penalising excessive stretching or shrinking). The best solutions can be obtained by knowing the approximate amount of stretching that is needed (e.g. ICBM templates are slightly bigger than typical brains, so greater zooms are likely to be needed). If registering to an image in ICBM/MNI space, then choose the first option. * ICBM space template If registering to a template that is close in size, then select the second option. * Average sized template If you do not want to regularise, then choose the third. * No regularisation
cutoff: String ( input )Nonlinear Frequency Cutoff : Cutoff of DCT bases. Only DCT bases of periods longer than the cutoff are used to describe the warps. The number used will depend on the cutoff and the field of view of the template image(s).
nits: String ( input )Nonlinear Iterations : Number of iterations of nonlinear warping performed.
reg: String ( input )Nonlinear Regularisation : The amount of regularisation for the nonlinear part of the spatial normalisation. Pick a value around one. However, if your normalised images appear distorted, then it may be an idea to increase the amount of regularisation (by an order of magnitude) - or even just use an affine normalisation. The regularisation influences the smoothness of the deformation fields.
preserve: String ( input )Preserve : Preserve Concentrations: Spatially normalised images are not "modulated". The warped images preserve the intensities of the original images. Preserve Total: Spatially normalised images are "modulated" in order to preserve the total amount of signal in the images. Areas that are expanded during warping are correspondingly reduced in intensity.
bb: String ( input )Bounding box : The bounding box (in mm) of the volume which is to be written (relative to the anterior commissure).
vox: String ( input )Voxel sizes : The voxel sizes (x, y, z, in mm) of the written normalised images.
interp: String ( input )Interpolation : The method by which the images are sampled when being written in a different space. (Note that Inf or NaN values are treated as zero, rather than as missing data) Nearest Neighbour: - Fastest, but not normally recommended. Bilinear Interpolation: - OK for PET, realigned fMRI, or segmentations B-spline Interpolation: - Better quality (but slower) interpolation, especially with higher degree splines. Can produce values outside the original range (e.g. small negative values from an originally all positive image).
wrap: String ( input )Wrapping : These are typically: No wrapping: for PET or images that have already been spatially transformed. Wrap in Y: for (un-resliced) MRI where phase encoding is in the Y direction (voxel space).
prefix: String ( input )Filename Prefix : Specify the string to be prepended to the filenames of the normalised image file(s).
generatePsFileWithMatlab: Booléen ( input )
Toolbox : Outils
Niveau d'utilisateur : 0
Identifiant :
normalize_SPMNom de fichier :
brainvisa/toolboxes/tools/processes/spm/normalize_SPM.pySupported file formats :
source :GIS image, VIDA image, NIFTI-1 image, MINC image, gz compressed MINC image, DICOM image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, TIFF(.tif) image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v imageimageToWrite :GIS image, VIDA image, NIFTI-1 image, MINC image, gz compressed MINC image, DICOM image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, TIFF(.tif) image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v imagewarpedInMni :GIS image, VIDA image, NIFTI-1 image, MINC image, gz compressed MINC image, DICOM image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, TIFF(.tif) image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v imagetemplate :GIS image, VIDA image, NIFTI-1 image, MINC image, gz compressed MINC image, DICOM image, TIFF image, XBM image, PBM image, PGM image, BMP image, XPM image, PPM image, gz compressed NIFTI-1 image, TIFF(.tif) image, ECAT i image, PNG image, JPEG image, MNG image, GIF image, SPM image, ECAT v imagecheckReg :PS file