View source for Slicer3:Module:Rician Noise Removal

===Module Name===
Rician Noise Removal in Diffusion Tensor MRI (DWI and tensors)

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== General Information ==
===Module Type & Category===

Type: CLI

Category: Filtering DWI and tensors

===Authors, Collaborators & Contact===
* Saurav Basu: University of Utah
* Thomas Fletcher, University of Utah
* Ross Withaker, University of Utah
* Contact: Thomas Fletcher 

===Module Description===
Rician noise introduces a bias into MRI measurements that
can have a signiﬁcant impact on the shapes and orientations of ten-
sors in diﬀusion tensor magnetic resonance images. This is less of a
problem in structural MRI, because this bias is signal dependent and
it does not seriously impair tissue identiﬁcation or clinical diagnoses.
However, diﬀusion imaging is used extensively for quantitative evalua-
tions, and the tensors used in those evaluations are biased in ways that
depend on orientation and signal levels. This paper presents a strat-
egy for ﬁltering diﬀusion tensor magnetic resonance images that ad-
dresses these issues. The method is a maximum a posteriori estima-
tion technique that operates directly on the diﬀusion weighted images
and accounts for the biases introduced by Rician noise. We account for
Rician noise through a data likelihood term that is combined with a
spatial smoothing prior. The method compares favorably with several
other approaches from the literature, including methods that ﬁlter dif-
fusion weighted imagery and those that operate directly on the diﬀusion
tensors.

== Usage ==

===DWI filtering===

====Examples, Use Cases & Tutorials====

USAGE
--------------
dwiFilter <arguments>
Arguments:
1. Input File Name
2. Output File Name
3. NumIterations
4. Conductance
5. TimeStep
6. Filter Type : (Simple Aniso-0,Chi Squared-1,Rician-2,Gaussian-3)
7. Sigma for bias correction
8. Lamda (Rician Correction Term)
9. Lamda (Gaussian Correction Term)

Argument Description:

<Input File Name> 
Name of the DWI file to be filtered. For example
<noisyDWI_10.nhdr> is a noisy DWI file provided
in the data directory. It was generated by adding 
synthetic Rician noise with a sigma=10 to a cleanDWI.nhdr

<Output File Name>
Name of the filtered DWI file. For example
<filteredDWI.nhdr>

<NumIterations>
Number of iterations you want to run the filter for.

<Conductance>
The value of the conductance term in anisotropic 
diffusion filtering (Ex: 1.0)
Note: Large Conductance will oversmooth the image
It is important to tune the conductance to obtain
best results.

<Time Step>
This determines the step size in the gradient
descent. It can be atmost 0.0625.

<Filter Type>
Can Take 3 values:
0 means perform simple anisotropic diffusion
   
* 1 means perform Chi-Squared smoothing (square the image and perform anisotropic diffusion and then subtract the variance of the noise, and take square root. (The square of a Rice distribution is a Chi Squared distribution with known bias equal to the variance of the noise) (Refer:Max likelihood Est. of Rician Ditribution Parameters. Sijbers et. al)
* 2 means Perform Rician bias correction filtering.(Refer: Rician Noise Removal in DT-MRI.)
* 3 is same as 2 except use a Gaussian Attachment Term .

<Sigma>
Estimate of noise in the data.
This can be done by squaring the airvoxels
in the real data. The sum of square of all
the intensities in the air region should equal
2*variance of the noise in the data.
(Sijbers et. al)

<lamda1, lamda2>
The weights for the Rician and Gaussian 
attachment terms. 

EXAMPLE
-------------
dwiFilter ../data/noisyDWI_10.nhdr filteredDWI.nhdr 1 1.0 0.0625 2 10 100 0

Filters the noisyDWI_10.nhdr for 1 iteration with a conductance of 1.0
timeStep 0.0625 using Rician filtering with a Rician attachement term
weight of 100. The estimate of noise in the input image is a sigma of 10
The filtered image is filteredDWI.nhdr.

===Tensor filtering===

Usage
--------------
tensorDiffuse <Arguments>
1. FilterType:(0-Euclidean, 1-Log Space,2-Riemannian)
2. numIterations:Iterations For Anisotropic Diffusion
3. timeStep:timeStep Used in Anisotropic Diffusion
4. conductance:Conductance used for Anisotropic Diffusion
5. Input (filename of input data)
6. Output (filename of output data)

Arguments 2,3,4 have the same meaning as described for dwiFilter (see above).

Argument 1 describes the filter type
* - 0: Euclidean Space filtering (tensors are treated as 6-d vectors)
* - 1: Log Space filtering (Fast and Simple Calculus on Tensors in the Log-Euclidean Framework. In J. Duncan and G. Gerig, editors, Proceedings of the 8th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005, Part I, volume 3749 of LNCS, Palm Springs, CA, USA, October 26-29, pages 115-122, 2005. Springer Verlag)
* - 2. Riemannian Space Filtering(A Riemannian Framework for the Processing of Tensor-Valued Images. In Ole Fogh Olsen, Luc Florak, and Arjan Kuijper, editors, Deep Structure, Singularities, and Computer Vision (DSSCV), number 3753 of LNCS, pages 112-123, June 2005. Springer Verlag.)

Currently, the Riemannian filter adjustment for negative eigen-values
is hard-coded in the source file.

Argument 5 is the name of the noisyTensor input.
Argument 6 is the name of the output tensor file

Example Usage:
--------------
tensorFilter 2 1 0.0625 1.0 noisyTensor_10.nhdr FilteredTensor.nhrd


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== Development ==

===Dependencies===

Other modules or packages that are required for this module's use.

===Known bugs===

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===Usability issues===

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===Source code & documentation===

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== More Information == 

===Acknowledgement===
This work is part of the National Alliance for Medical Image Computing
(NAMIC), funded by the National Institutes of Health through the NIH Roadmap
for Medical Research, Grant U54 EB005149. Information on the National Centers
for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/
bioinformatics. Funding for this work has also been provided by Center for
Integrative Biomedical Computing, NIH NCRR Project 2-P41-RR12553-07. We
thank Weili Lin and Guido Gerig from the University of North Carolina for
providing us with the DW-MRI data. Glyph visualizations created with Teem
(http://teem.sf.net).


===References===
Basu, S., Fletcher, P.T., Whitaker, R.T. Rician Noise Removal in Diffusion Tensor MRI. In Medical Image Computing and Computer Assisted Intervention (MICCAI), LNCS 4190, pp. 117-125, October, 2006.
[[http://www.sci.utah.edu/~fletcher/BasuDTIFilteringMICCAI2006.pdf | Paper link ]]