Module Type & Category
Category: Diffusion MRI Applications
Authors, Collaborators & Contact
- Author: Antonio Tristán Vega and Santiago Aja Fernández
- Contact: firstname.lastname@example.org
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-Fernández, 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.
Examples, Use Cases & Tutorials
A brief description of the most important parameters is given below.
Quick Tour of Features and Use
It is very easy to use it. Just select a DWI, set the parameters (if you really need it), and you're ready to go.
- Input DWI Volume: set the DWI volume
- Output DWI Volume: the filtered DWI volume
- Estimation radius: This is the 3D radius of the neighborhood used for noise estimation. Noise power is estimated as the mode of the histogram of local variances
- Filtering radius: This is the 3D radius of the neighborhood used for filtering: local means and covariance matrices are estimated within this neighborhood
Volumes. Needed to load DWI volumes
Source code & documentation
Source Code: Follow this link
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially funded by grant number TEC2007-67073/TCM from the Ministerio de Ciencia e Innovación (Spain) and "FEDER" European Regional Development Fund.