awssigmc: Estimate noise variance for multicoil MR systems

Description

The distribution of image intensity values $S_i$ divided by the noise standard deviation in $K$-space $\sigma$ in dMRI experiments is assumed to follow a non-central chi-distribution with $2L$ degrees of freedom and noncentrality parameter $\eta$, where $L$ refers to the number of receiver coils in the system and $\sigma \eta$ is the signal of interest. This is an idealization in the sense that each coil is assumed to have the same contribution at each location. For realistic modeling $L$ should be a locally smooth function in voxel space that reflects the varying local influence of the receiver coils in the the reconstruction algorithm used. The function assumes $L$ to be known and estimates a global $\sigma$ employing an assumption of local homogeneity for the noncentrality parameter $\eta$.

Usage

awssigmc(y, steps, mask = NULL, ncoils = 1, vext = c(1, 1), lambda = 10, h0 = 2, verbose = FALSE, 
         model = "chisq", sequence = FALSE, eps = 1e-05, hadj = 1, q = 0.25)

Arguments

3D array, usually obtained from an object of class dwi as obj@si[,,,i] for some i, i.e. one 3D image from an dMRI experiment.

steps

number of steps in adapive weights smoothing, used to reveal the unerlying mean structure.

mask

restrict computations to voxel in mask, if is.null(mask) all voxel are used.

ncoils

number of coils, or equivalently number of effective degrees of freedom of non-central chi distribution divided by 2.

vext

voxel extentions

lambda

scale parameter in adaptive weights smoothing

initial bandwidth

verbose

if verbose==TRUE density plots and quantiles of local estimates of sigma are provided.

model

either "chi" or "chisq". In the latter case smoothing and variance estimation are performed for y^2 instead of y which is considerably faster.

sequence

if sequence=TRUE a vector of estimates for the noise standard deviation sigma for the individual steps is returned instead of the final value only.

eps

accuracy when solving fixpoint equation for noncentrality parameter in case of model="chi".

hadj

adjustment factor for bandwidth (chosen by bw.nrd) in mode estimation

quantile to be used for interquantile-differences.

Value

a list with components
sigmaeither a scalar or a vector of estimated noise standard deviations.
thetathe estimated mean structure

References

K. Tabelow and J. Polzehl (2013). Estimating the noise level in MRI using structural adaptive smoothing. Manuscript in preparation.