Documentation/4.0/Modules/DWIRicianLMMSEFilter
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Introduction and Acknowledgements
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This work is part of the National Alliance for Medical Image Computing (NA-MIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on NA-MIC can be obtained from the NA-MIC website. | |||||
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Module Description
This module reduces noise (or unwanted detail) on a set of diffusion weighted images. For this, it filters the image in the mean squared error sense using a Rician noise model. Images corresponding to each gradient direction, including baseline, are processed individually. The noise parameter is automatically estimated (noise estimation improved but slower). Note that this is a general purpose filter for MRi images. The module jointLMMSE has been specifically designed for DWI volumes and shows a better performance, so its use is recommended instead. A complete description of the algorithm in this module can be found in: S. Aja-Fernandez, M. Niethammer, M. Kubicki, M. Shenton, and C.-F. Westin. Restoration of DWI data using a Rician LMMSE estimator. IEEE Transactions on Medical Imaging, 27(10): pp. 1389-1403, Oct. 2008.
Use Cases
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Tutorials
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Panels and their use
Parameters:
- DWI Noise Removal Parameters
- Number of iterations: Number of iterations for the noise removal filter.
- Estimation Radius: Estimation radius.
- Filtering Radius: Filtering radius.
- Minimum voxels # for filtering.: Minimum number of voxels in kernel used for filtering.
- Minimum voxels # for estimation.: Minimum number of voxels in kernel used for estimation.
- Minimum noise STD.: Minimum allowed noise standard deviation.
- Maximum noise STD.: Maximum allowed noise standard deviation.
- Histogram resolution factor.: How many histogram bins per unit interval.
- Use absolute value.: Use absolute value in case of negative square.
- IO
- Input Volume: Input DWI volume.
- Output Volume: Output DWI volume.
Similar Modules
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References
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Information for Developers
| Section under construction. |
