Module Type & Category
Authors, Collaborators & Contact
- Author: Antonio Tristán Vega and Santiago Aja Fernández
- Contact: email@example.com
Filters a set of diffusion weighted images in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results. The noise parameter is automatically estimated (noise estimation improved but slower). A complete description of the algorithm may be found in "DWI filtering using joint information for DTI and HARDI", by Antonio Tristan Vega and Santiago Aja-Fernandez (under review).
Examples, Use Cases & Tutorials
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
- Number of neighborhood gradients: This filter works gathering joint information from the N closest gradient directions to the one under study. This parameter is N. If N=0 is fixed, then all gradient directions are filtered together.
- NOTE: If N=1 is used this filter is similar (but not equal) to RicianLMMSEImageFilter. Two main differences exist: 1) 4-th order moments have to be computed only for baseline(s) image(s), and 2) if more than one baseline is present all of them are filtered together even if N=1.
Volumes. Needed to load DWI volumes
Source code & documentation
Antonio Tristan Vega, Santiago Aja Fernandez. University of Valladolid (SPAIN). Partially founded by grant number TEC2007-67073/TCM from the Comision Interministerial de Ciencia y Tecnologia (Spain).