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| − | ===Module Name===
| + | <big>'''Note:''' We are migrating this content to the slicer.org domain - <font color="orange">The newer page is [https://www.slicer.org/wiki/Slicer3:Module:Rician_Noise_Removal here]</font></big> |
| − | Rician Noise Removal in Diffusion Tensor MRI
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| − | {|
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| − | |[[Image:screenshotBlank.png|thumb|280px|Caption 1]]
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| − | |[[Image:screenshotBlank.png|thumb|280px|Caption 2]]
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| − | |[[Image:screenshotBlank.png|thumb|280px|Caption 3]]
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| − | |}
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| − | | |
| − | == General Information ==
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| − | ===Module Type & Category===
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| − | Type: CLI
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| − | Category: Filtering DWI and tensors
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| − | | |
| − | ===Authors, Collaborators & Contact===
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| − | * Saurav Basu: University of Utah
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| − | * Thomas Fletcher, University of Utah
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| − | * Ross Withaker, University of Utah
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| − | * Contact: Thomas Fletcher
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| − | | |
| − | ===Module Description===
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| − | Rician noise introduces a bias into MRI measurements that
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| − | can have a significant impact on the shapes and orientations of ten-
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| − | sors in diffusion tensor magnetic resonance images. This is less of a
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| − | problem in structural MRI, because this bias is signal dependent and
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| − | it does not seriously impair tissue identification or clinical diagnoses.
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| − | However, diffusion imaging is used extensively for quantitative evalua-
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| − | tions, and the tensors used in those evaluations are biased in ways that
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| − | depend on orientation and signal levels. This paper presents a strat-
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| − | egy for filtering diffusion tensor magnetic resonance images that ad-
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| − | dresses these issues. The method is a maximum a posteriori estima-
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| − | tion technique that operates directly on the diffusion weighted images
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| − | and accounts for the biases introduced by Rician noise. We account for
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| − | Rician noise through a data likelihood term that is combined with a
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| − | spatial smoothing prior. The method compares favorably with several
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| − | other approaches from the literature, including methods that filter dif-
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| − | fusion weighted imagery and those that operate directly on the diffusion
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| − | tensors.
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| − | | |
| − | == Usage ==
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| − | ===DWI filtering===
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| − | ====Examples, Use Cases & Tutorials====
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| − | USAGE
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| − | --------------
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| − | dwiFilter <arguments>
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| − | Arguments:
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| − | 1. Input File Name
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| − | 2. Output File Name
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| − | 3. NumIterations
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| − | 4. Conductance
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| − | 5. TimeStep
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| − | 6. Filter Type : (Simple Aniso-0,Chi Squared-1,Rician-2,Gaussian-3)
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| − | 7. Sigma for bias correction
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| − | 8. Lamda (Rician Correction Term)
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| − | 9. Lamda (Gaussian Correction Term)
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| − | | |
| − | Argument Description:
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| − | <Input File Name>
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| − | Name of the DWI file to be filtered. For example
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| − | <noisyDWI_10.nhdr> is a noisy DWI file provided
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| − | in the data directory. It was generated by adding
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| − | synthetic Rician noise with a sigma=10 to a cleanDWI.nhdr
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| − | <Output File Name>
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| − | Name of the filtered DWI file. For example
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| − | <filteredDWI.nhdr>
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| − | | |
| − | <NumIterations>
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| − | Number of iterations you want to run the filter for.
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| − | | |
| − | <Conductance>
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| − | The value of the conductance term in anisotropic
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| − | diffusion filtering (Ex: 1.0)
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| − | Note: Large Conductance will oversmooth the image
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| − | It is important to tune the conductance to obtain
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| − | best results.
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| − | <Time Step>
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| − | This determines the step size in the gradient
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| − | descent. It can be atmost 0.0625.
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| − | | |
| − | <Filter Type>
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| − | Can Take 3 values:
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| − | 0 means perform simple anisotropic diffusion
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| − |
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| − | * 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)
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| − | * 2 means Perform Rician bias correction filtering.(Refer: Rician Noise Removal in DT-MRI.)
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| − | * 3 is same as 2 except use a Gaussian Attachment Term .
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| − | <Sigma>
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| − | Estimate of noise in the data.
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| − | This can be done by squaring the airvoxels
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| − | in the real data. The sum of square of all
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| − | the intensities in the air region should equal
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| − | 2*variance of the noise in the data.
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| − | (Sijbers et. al)
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| − | | |
| − | <lamda1, lamda2> | |
| − | The weights for the Rician and Gaussian
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| − | attachment terms.
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| − | EXAMPLE
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| − | -------------
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| − | dwiFilter ../data/noisyDWI_10.nhdr filteredDWI.nhdr 1 1.0 0.0625 2 10 100 0
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| − | | |
| − | Filters the noisyDWI_10.nhdr for 1 iteration with a conductance of 1.0
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| − | timeStep 0.0625 using Rician filtering with a Rician attachement term
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| − | weight of 100. The estimate of noise in the input image is a sigma of 10
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| − | The filtered image is filteredDWI.nhdr.
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| − | | |
| − | * Note use cases for which this module is especially appropriate, and/or link to examples.
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| − | * Link to examples of the module's use
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| − | * Link to any existing tutorials
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| − | | |
| − | ===Quick Tour of Features and Use===
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| − | List all the panels in your interface, their features, what they mean, and how to use them. For instance:
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| − | | |
| − | * '''Input panel:'''
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| − | * '''Parameters panel:'''
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| − | * '''Output panel:'''
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| − | * '''Viewing panel:'''
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| − | | |
| − | == Development ==
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| − | | |
| − | ===Dependencies===
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| − | Other modules or packages that are required for this module's use.
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| − | | |
| − | ===Known bugs===
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| − | Follow this link to the Slicer3 bug tracker:
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| − | http://na-mic.org/Mantis/main_page.php
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| − | | |
| − | ===Usability issues===
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| − | Follow this link to the Slicer3 bug tracker. Please select the '''usability issue category''' when browsing or contributing:
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| − | http://na-mic.org/Mantis/main_page.php
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| − | | |
| − | ===Source code & documentation=== | |
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| − | Customize following links for your module:
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| − | http://www.na-mic.org/ViewVC/index.cgi/
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| − | Links to documentation generated by doxygen:
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| − | http://www.na-mic.org/Slicer/Documentation/Slicer3/html/
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| − | | |
| − | == More Information ==
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| − | ===Acknowledgement===
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| − | Include funding and other support here.
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| − | | |
| − | ===References===
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| − | Publications related to this module go here. Links to pdfs would be useful.
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