Cortical thickness

Compute the cortical thickness along Laplace field lines from a classification volume, using upwinding (much faster than advection, but slightly less precise)

Description

The only mandatory input is classif, the output is thickness_image.

Parameters

classif: Any Type ( input )

classification image of the cortex (100 inside, 0 in CSF, 200 in white matter)

verbosity: Integer ( optional, input )

Verbosity level

laplace_precision: Number ( optional, input )

target maximum relative error in first-order finite differences

laplace_typical_cortical_thickness: Number ( optional, input )

typical thickness of the cortex (mm), used for accelerating convergence

equidistant_depth: String ( optional, input )

thickness_image: Any Type ( output )

result of the arithmetic

use_capsul_completion: Boolean ( input )

edit_pipeline: Boolean ( input )

capsul_gui: Boolean ( input )

edit_study_config: Boolean ( input )

Technical information

Toolbox : highres-cortex

User level : 0

Identifier : thickness_upw

File name : brainvisa/toolboxes/highres_cortex/processes/thickness_upw.py

Supported file formats :

classif :

Directory, BMP image, Directory, ECAT i image, ECAT v image, File, GIF image, GIS image, JPEG image, MINC image, MNG image, NIFTI-1 image, PBM image, PGM image, PNG image, PPM image, SPM image, TIFF image, TIFF(.tif) image, VIDA image, XBM image, XPM image, gz compressed MINC image, gz compressed NIFTI-1 image

thickness_image :

Directory, BMP image, Directory, ECAT i image, ECAT v image, File, GIF image, GIS image, JPEG image, MINC image, MNG image, NIFTI-1 image, PBM image, PGM image, PNG image, PPM image, SPM image, TIFF image, TIFF(.tif) image, VIDA image, XBM image, XPM image, gz compressed MINC image, gz compressed NIFTI-1 image