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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 (DWI and tensors)
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| − | | |
| − | {|
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| − | |[[Image:RicianTensorCorrectionImage.png|thumb|280px|Caption 1]]
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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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| − | * Tom Fletcher, University of Utah
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| − | * Ross Whitaker, University of Utah
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| − | * Sylvain Gouttard, University of Utah
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| − | * Contact: Tom 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>
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| − | 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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| − | | |
| − | ===Tensor filtering===
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| − | | |
| − | Usage
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| − | --------------
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| − | tensorDiffuse <Arguments>
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| − | 1. FilterType:(0-Euclidean, 1-Log Space,2-Riemannian)
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| − | 2. numIterations:Iterations For Anisotropic Diffusion
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| − | 3. timeStep:timeStep Used in Anisotropic Diffusion
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| − | 4. conductance:Conductance used for Anisotropic Diffusion
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| − | 5. Input (filename of input data)
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| − | 6. Output (filename of output data)
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| − | | |
| − | Arguments 2,3,4 have the same meaning as described for dwiFilter (see above).
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| − | | |
| − | Argument 1 describes the filter type
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| − | * - 0: Euclidean Space filtering (tensors are treated as 6-d vectors)
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| − | * - 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)
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| − | * - 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.)
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| − | | |
| − | Currently, the Riemannian filter adjustment for negative eigen-values
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| − | is hard-coded in the source file.
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| − | | |
| − | Argument 5 is the name of the noisyTensor input.
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| − | Argument 6 is the name of the output tensor file
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| − | | |
| − | EXAMPLE
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| − | --------------
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| − | tensorFilter 2 1 0.0625 1.0 noisyTensor_10.nhdr FilteredTensor.nhrd
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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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| − | | |
| − | ===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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| − | This work is part of the National Alliance for Medical Image Computing
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| − | (NAMIC), funded by the National Institutes of Health through the NIH Roadmap
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| − | for Medical Research, Grant U54 EB005149. Information on the National Centers
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| − | for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/
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| − | bioinformatics. Funding for this work has also been provided by Center for
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| − | Integrative Biomedical Computing, NIH NCRR Project 2-P41-RR12553-07. We
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| − | thank Weili Lin and Guido Gerig from the University of North Carolina for
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| − | providing us with the DW-MRI data. Glyph visualizations created with Teem
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| − | (http://teem.sf.net).
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| − | | |
| − | | |
| − | ===References===
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| − | 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.
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| − | [[http://www.sci.utah.edu/~fletcher/BasuDTIFilteringMICCAI2006.pdf | Paper link ]]
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