USAGE:
SlicerJointRicianAnisotropicLMMSEFilter [--returnparameterfile
]
[--processinformationaddress
] [--xml] [--echo]
[--maskImage ]
[--noiseLevel ]
[--overrideNoise] [--re
>] [--onlyUNLM]
[--setZeroBck] [--compressOutput]
[--rfeat >] [--ng
] [--h ] [--rf
>] [--]
[--version] [-h]
Where:
--returnparameterfile
Filename in which to write simple return parameters (int, float,
int-vector, etc.) as opposed to bulk return parameters (image,
geometry, transform, measurement, table).
--processinformationaddress
Address of a structure to store process information (progress, abort,
etc.). (default: 0)
--xml
Produce xml description of command line arguments (default: 0)
--echo
Echo the command line arguments (default: 0)
--maskImage
This is an optional binary mask to apply to the DWI. The values inside
the mask are filtered, while those outside the mask either remain
unaltered or are set to zero (see 'Advanced parameters of the
algorithm') to save computations. The mask will not effect noise
estimation
--noiseLevel
This value applies only when 'Override noise estimation' is selected.
It represents the standard deviation of noise, sigma, in the complex
domain. (default: 5)
--overrideNoise
When this flag is selected, the noise parameter is manually introduced
instead of automatically estimated. Use this flag in case you suspect
the noise is not correctly estimated for your data set. (default: 0)
--re >
The standard deviation of noise in the complex domain, sigma, is
computed as the mode of the distribution of local variances of the
image in those zones where the signal is actually present. These local
variances are computed in a neighborhood with this 'Estimation
Radius'. (default: 1,1,0)
--onlyUNLM
If this flag is set, the filter turns out into an Unbiased Non-Local
Means: the output pixel is set as the square root of the second order
moment (=-2*sigma^2) estimated from the anisotropic
neighborhood. This is faster than the original LMMSE, but the
performance is also worse in terms of both noise removal and details
preservattion. (default: 0)
--setZeroBck
Useful only when a mask is used. When set, background voxels (those
outside of the mask) are set to 0 instead of preserving their original
value (default: 0)
--compressOutput
Compress the data in the output file using gzip (default: 0)
--rfeat >
To assess the similarity between two pixels in the filtering
neighborhood, we average the similarities in three channels RGB
corresponding to projections of the data set in the corresponding axes
'x', 'y', and 'z'. For each channel, we compute the local mean value
and local directional derivatives inside a neighborhood with this
'Features Radius'. The similarity is finally computed as the distance
in this features space (mean value and derivatives). (default: 1,1,1)
--ng
The number of the closest gradients that are used to jointly filter a
given gradient direction (0 to use all). Using all the gradients is
faster (and typically provides better results) than taking a subset.
(default: 0)
--h
This parameter should be in the range 1-5 for optimum performance:
larger values produce a more agressive denoising. Smaller values
better preserve the details. When h is infinity (i.e. a very large
number), we have the old isotropic jLMMSE, and a smaller filtering
radius should be chosen to avoid over-blurring. (default: 2)
--rf >
Filtering radius. The algorithm searchs for similar voxels inside a
3-D neighborhood with this radius, and uses this sample to estimate
the noise-free value. Since only similar voxels are used, larger radii
do not necessarily produce over-blurring, but computations will take
longer. (default: 4,4,4)
--, --ignore_rest
Ignores the rest of the labeled arguments following this flag.
--version
Displays version information and exits.
-h, --help
Displays usage information and exits.
(required) Input DWI volume.
(required) Output DWI volume.
Description: This module is a reimplementation of the older 'DWI Joint
Rician LMMSE Filter'. There are two main differences with this former
approach:
1) Instead of computing sample moments inside isotropic neighborhoods,
we use a non-local means-like scheme to average only those voxels
silmilar enough to the central voxel. This similarity is based on three
RGB channels corresponding to the projections of the DWI data set in
three mutually orthogonal spatial directions.
2) The standard deviation of noise in the complex domain, sigma, is
estimated here as the mode of the histogram of the (corrected) local
variances in the signal area, which is a more robust estimation
procedure.
This module reduces Rician noise on a set of diffusion weighted images.
For this, it filters the image in the mean squared error sense using a
Rician noise model. The N closest gradient directions to the direction
being processed are filtered together to improve the results: the
noise-free signal is seen as an n-dimensional vector which has to be
estimated with the LMMSE method from a set of corrupted measurements. To
that end, the covariance matrix of the noise-free vector and the cross
covariance between this signal and the noise have to be estimated, which
is done taking into account the image formation process. All the
estimations are performed as sample estimates in a 'shaped neighborhood'
defined by the weights extracted from the structural similarity of the
voxels.
A complete description of the isotropic algorithm may be found
in:
Antonio Tristan-Vega and Santiago Aja-Fernandez, 'DWI filtering using
joint information for DTI and HARDI', Medical Image Analysis, Volume 14,
Issue 2, Pages 205-218. 2010.
The anisotropic method is further described in:
Antonio Tristan-Vega, Veronique Brion, Gonzalo Vegas-Sanchez-Ferrero,
and Santiago Aja-Fernandez, 'Merging squared-magnitude approaches to DWI
denoising: An adaptive Wiener filter tuned to the anatomical contents of
the image', In Proceedings of IEEE EMBC 2013.
Author(s): Antonio Tristan Vega (University of Valladolid,
Spain)
Acknowledgements: Work partially funded by grant numbers TEC2010-17982
from the Ministerio de Ciencia y Educacion (Spain) and VA376A11-2,
SAN103/VA40/1 from the Junta de Castilla y Leon (Spain).