Difference between revisions of "Slicer3:Module:Rician Noise Removal"

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(New page: ===Module Name=== MyModule {| |thumb|280px|Caption 1 |thumb|280px|Caption 2 |[[Image:screenshotBlank.png|thumb|280px|Caption 3]...)
 
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===Module Name===
 
===Module Name===
−
MyModule
+
Rician Noise Removal in Diffusion Tensor MRI
  
 
{|
 
{|
Line 11: Line 11:
 
===Module Type & Category===
 
===Module Type & Category===
  
−
Type: Interactive or CLI
+
Type: CLI
  
−
Category: Base or (Filtering, Registration, ''etc.'')
+
Category: Filtering DWI and tensors
  
 
===Authors, Collaborators & Contact===
 
===Authors, Collaborators & Contact===
−
* Author1: Affiliation & logo, if desired
+
* Saurav Basu: University of Utah
−
* Contributor1: Affiliation & logo, if desired
+
* Thomas Fletcher, University of Utah
−
* Contributor2: Affiliation & logo, if desired
+
* Ross Withaker, University of Utah
−
* Contact: name, email
+
* Contact: Thomas Fletcher
  
 
===Module Description===
 
===Module Description===
−
Overview of what the module does goes here.
+
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 ==
 
== Usage ==
  
−
===Examples, Use Cases & Tutorials===
+
===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.
 
* Note use cases for which this module is especially appropriate, and/or link to examples.
 
* Note use cases for which this module is especially appropriate, and/or link to examples.
 
* Link to examples of the module's use
 
* Link to examples of the module's use

Revision as of 15:56, 19 September 2007

Home < Slicer3:Module:Rician Noise Removal

Module Name

Rician Noise Removal in Diffusion Tensor MRI

Caption 1
Caption 2
Caption 3

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.

<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.

  • Note use cases for which this module is especially appropriate, and/or link to examples.
  • Link to examples of the module's use
  • Link to any existing tutorials

Quick Tour of Features and Use

List all the panels in your interface, their features, what they mean, and how to use them. For instance:

  • Input panel:
  • Parameters panel:
  • Output panel:
  • Viewing panel:

Development

Dependencies

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

Known bugs

Follow this link to the Slicer3 bug tracker:

http://na-mic.org/Mantis/main_page.php

Usability issues

Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing:

http://na-mic.org/Mantis/main_page.php

Source code & documentation

Customize following links for your module:

http://www.na-mic.org/ViewVC/index.cgi/

Links to documentation generated by doxygen:

http://www.na-mic.org/Slicer/Documentation/Slicer3/html/

More Information

Acknowledgement

Include funding and other support here.

References

Publications related to this module go here. Links to pdfs would be useful.

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