smse3ms {aws}  R Documentation 
The functions perform adaptive weights smoothing for data in orientation space SE(3),
e.g. diffusion weighted MR data,
with spatial coordinates given by voxel location within a mask and spherical information given
by gradient direction. Observations can belong to different shells characterized by bvalue bv
.
The data provided should only refer to voxel within mask.
smse3ms(sb, s0, bv, grad, kstar, lambda, kappa0, mask, sigma, ns0 = 1, ws0 = 1, vext = NULL, ncoils = 1, verbose = FALSE, usemaxni = TRUE) smse3(sb, s0, bv, grad, mask, sigma, kstar, lambda, kappa0, ns0 = 1, vext = NULL, vred = 4, ncoils = 1, model = 0, dist = 1, verbose = FALSE)
sb 
2D array of diffion weighted data, first dimension refers to index ov voxel within the mask, second dimension to the number diffusion weighted images. 
s0 
vector of length 
bv 
vector of bvalues. 
grad 
matrix of gradient directions with 
kstar 
number of steps in adaptive weights smoothing. 
lambda 
Scale parameter in adaptation 
kappa0 
determines amount of smoothing on the sphere. Larger values correspond to stronger smoothing
on the sphere. If 
mask 
3D image defining a mask (logical) 
sigma 
Error standard deviation. Assumed to be known and homogeneous in the current implementation.
A reasonable estimate may be defined
as the modal value of standard deviations obtained using method 
ns0 
Actual number of nondiffusionweigthed images used to obtain 
ws0 
Weight for nondiffusionweigthed images in statistical penalty. 
vext 
Voxel extensions. 
ncoils 
Effective number of receiver coils (in case of e.g. GRAPPA reconstructions),
should be 1 in case of SENSE reconstructions. 
verbose 
If 
usemaxni 
If 
vred 
Used if 
model 
Determines which quantities are smoothed. Possible values are

dist 
Distance in SE3. Reasonable values are 1 (default, see Becker et.al. 2012), 2 ( a slight modification of 1: with k6^2 instead of abs(k6)) and 3 (using a 'naive' distance on the sphere) 
The functions return lists with main results in components
th
and th0
containing the smoothed data.
These functions are intended to be used internally in package dti
only.
J\"org Polzehl polzehl@wiasberlin.de
Joerg Polzehl, Karsten Tabelow (2019). Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R. Springer, Use R! series. Doi:10.1007/9783030291846.
S. Becker, K. Tabelow, H.U. Voss, A. Anwander, R. Heidemann, J. Polzehl. Positionorientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS). Medical Image Analysis, 2012, 16, 11421155. DOI:10.1016/j.media.2012.05.007.
S. Becker, K. Tabelow, S. Mohammadi, N. Weiskopf, J. Polzehl. Adaptive smoothing of multishell diffusionweighted magnetic resonance data by msPOAS. Neuroimage, 2014, 95, 90105. DOI:10.1016/j.neuroimage.2014.03.053.