smoothest: Estimate global image smoothness.

In spisakt/pTFCE: Probabilitic Threshold-free Cluster Enhancement of Neuroimages

Estimate global image smoothness.

Description

Ported form the C++ implementation of FSL.

Usage

smoothest(img, mask, dof = NA, verbose = T)

Arguments

img	Z-score image or a 4D residual image, ("nifti" class from "oro.nifti" package)
mask	image mask ("nifti" class from "oro.nifti" package)
dof	degrees of freedom, obligatory if img is a 4D residual image
verbose	boolean: print progress bar and diagnostic messages if true (default)

Details

For mathematical background, see: https://www.fmrib.ox.ac.uk/datasets/techrep/tr00df1/tr00df1/index.html

The function takes two images (both "nifti" object of the oro.nifti package): (i) either a Z-score image or a 4D residual image together with the degrees of freedom, and (ii) a mask image as obligatory inputs. Mask can be either binary or continous, in the latter case it will be thresholded at 0.5.

For a Gaussian random field the smoothness is defined as

W =|\Lambda|^{-1/2D}

where D is the dimensionality of the field and \Lambda the covariance matrix of it's first partial derivatives.

Using Z-score image is less optimal because:

• Smoothness estimates need spatial derivatives, which are very noisy quantities and, for a single 3D map, can be computed just once on each direction.

• The z-map may contain effects, and these affect smoothness.

Value

An object of class "smoothness" is a list containing at least the following components:

• volume volume of the mask used for estimating smoothness, in voxels

• sigmasq sigma squared values in the x, y and z directions

• FWHM full width at half maximum values of smoothness in the x, y and z direction (voxels)

• dLh determininant of Lambda to the half (voxels^-3)

• resels resel size (voxels per resel)

spisakt/pTFCE documentation built on Aug. 22, 2023, 7:42 p.m.