smoothIRSolid: Smooth object generated by function 'estimateIRsolid'

In qMRI: Methods for Quantitative Magnetic Resonance Imaging ('qMRI')

Smooth object generated by function estimateIRsolid

Description

Adaptive smoothing of Rx and Sx maps over WM and GM areas.

Usage

smoothIRSolid(IRmixedobj, kstar = 24, patchsize = 1, alpha = 0.025,
                   mscbw = 5, bysegment=TRUE, partial=TRUE, verbose=TRUE)

Arguments

IRmixedobj	object of class IRmixed generated by function estimateIRsolid
kstar	number of steps for AWS algorithm
patchsize	patchsize in paws
alpha	significance level for decisions in aws algorithm (suggestion: between 1e-5 and 0.025)
mscbw	bandwidth for 3D median smoother used to stabilize the covariance estimates.
bysegment	TRUE: restrict smoothing to segments from segmentation, FALSE: restrict smoothing to solid mask.
partial	TRUE: ignore information concerning parameter fx when smoothing.
verbose	logical: Monitor process.

Details

This uses a vectorized version of the AWS algorithm that emloys inverse covariance estimates of the estimated parameters. Local smoothing is done for Rx and Sx maps in ergs which can be assumed to be locally smooth within tissue. No smoothing for fx maps since they may vary.

Value

an object of class "IRmixed", but with components Sx and Rx replaced. The object carries an additional component bi containing an array of sum of weights characterizing the amount of smoothing.

Author(s)

Karsten Tabelow [e-mail]
J\"org Polzehl [e-mail]

See Also

estimateIRfluid, estimateIRsolid, estimateIRsolidfixed,estimateIR

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (ergs, segm, kstar = 24, ladjust = 1)
{
    mask <- segm > 1
    nvoxel <- sum(mask)
    bpars <- array(0, c(2, nvoxel))
    icovbpars <- array(0, c(2, 2, nvoxel))
    bpars[1, ] <- ergs$Rx[mask]
    bpars[2, ] <- ergs$Sx[mask]
    ICovx <- ergs$ICovx
    dim(ICovx) <- c(3, 3, prod(dim(mask)))
    icovbpars <- ICovx[-1, -1, mask]
    z <- vpawscov2(bpars, kstar, icovbpars/ladjust, segm > 1)
    ergs$Rx[mask] <- z$theta[1, ]
    ergs$Sx[mask] <- z$theta[2, ]
    bi <- array(0, dim(mask))
    bi[mask] <- z$bi
    ergs$bi <- bi
    ergs
  }

qMRI documentation built on Sept. 18, 2023, 9:08 a.m.