xfibres: Bayesian Estimation of Diffusion Parameters Obtained using...

In fslr: Wrapper Functions for 'FSL' ('FMRIB' Software Library) from Functional MRI of the Brain ('FMRIB')

Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques with Crossing Fibers

Description

Calls xfibres from FSL to fit, also known as bedpostx

Usage

xfibres(
  infile,
  bvecs,
  bvals,
  mask = NULL,
  nfibres = 1,
  bet.opts = "",
  verbose = TRUE,
  njumps = NULL,
  burnin = NULL,
  burnin_noard = NULL,
  sampleevery = NULL,
  updateproposalevery = NULL,
  seed = NULL,
  noard = FALSE,
  allard = FALSE,
  nospat = FALSE,
  nonlinear = FALSE,
  cnonlinear = FALSE,
  rician = FALSE,
  f0 = FALSE,
  ardf0 = FALSE,
  opts = ""
)

Arguments

infile	Input filename
bvecs	b-vectors: matrix of 3 columns or filename of ASCII text file
bvals	b-values: vector of same length as number of rows of b-vectors or filename of ASCII text file
mask	Mask filename
nfibres	Maximum number of fibres to fit in each voxel (default 1)
bet.opts	Options for fslbet if mask is not supplied
verbose	print diagnostic messages
njumps	num of jumps to be made by MCMC (default is 5000)
burnin	Total num of jumps at start of MCMC to be discarded (default is 0)
burnin_noard	num of burnin jumps before the ard is imposed (default is 0)
sampleevery	num of jumps for each sample (MCMC) (default is 1)
updateproposalevery	num of jumps for each update to the proposal density std (MCMC) (default is 40)
seed	for pseudo random number generator
noard	Turn ARD off on all fibres
allard	Turn ARD on on all fibres
nospat	Initialise with tensor, not spatially
nonlinear	Initialise with nonlinear fitting
cnonlinear	Initialise with constrained nonlinear fitting
rician	Use Rician noise modelling
f0	Add to the model an unattenuated signal compartment
ardf0	Use ard on f0
opts	Additional options for xfibres. There should not be any left out in the current arguments, but opts may be a way some prefer to input options.

Value

Output from system

fslr documentation built on May 29, 2024, 2:31 a.m.