camino_dtifit: Fit Model using Camino

In neuroconductor/rcamino: Port of the Camino Software

Description

Fit Model using Camino

Usage

camino_dtifit(infile, bvecs, bvals, mask, model = c("dt", "ldt",
  "nldt_pos", "nldt", "ldt_wtd", "restore"), verbose = TRUE,
  inputmodel = c("dt", "twotensor", "threetensor", "multitensor"), ...)

Arguments

infile	Input 4D image
bvecs	The file containing the b-vectors.
bvals	The file containing the b-values.
mask	Provides the name of a file containing a brain / background mask. The file can be raw binary or a NIFTI image. Raw binary files must be big endian; the default data type is 16-bit shorts, but can be changed using the -maskdatatype option. The program does not process background voxels, but outputs the same number of values in background voxels and foreground voxels. Each value is zero in background voxels apart from the exit code which is -1.
model	Type of model used: 1-tensor: dt (linear diffusion tensor, default), ldt - same as dt nldt_pos (nonlinear optimization, constrained to be positive semi-definite), nldt (unconstrained nonlinear optimization), ldt_wtd (weighted linear)
verbose	Print diagnostic messages
inputmodel	model used for tensors, passed to camino_compute
...	Additional arguments passed to camino_fsl2scheme or camino_modelfit or camino_dt2nii

Value

List of values and such

Examples

## Not run: 
library(neurobase)
library(rcamino)
img = "~/Downloads/data.nii.gz"
bvals = "~/Downloads/bvals"
bvecs = "~/Downloads/bvecs"
mask = "~/Downloads/nodif_brain_mask.nii.gz"
gradadj = "~/Downloads/grad_dev.nii.gz"
sub = subset_dti(img = img, bvals = bvals, bvecs = bvecs,
                 maximum = 1500,
b_step = 1)
infile = sub$img
bvals = sub$bvals
bvecs = sub$bvecs
inputmodel = "dt"
verbose = TRUE
args = list()
camino_set_heap(heap_size = 10000)
res = camino_dtifit(infile = infile, bvecs = bvecs,
bvals = bvals, mask = mask, model = "dt", inputmodel = "dt")

## End(Not run)

neuroconductor/rcamino documentation built on May 25, 2021, 6:28 p.m.