Documentation/Nightly/Modules/AADDiffusionWeightedData

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For the stable Slicer documentation, visit the 4.10 page.

Introduction and Acknowledgements

Extension: AnomalousFilters
Webpage: http://dcm.ffclrp.usp.br/csim/
Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)
Contact: Antonio Carlos da S. Senra Filho, <email>acsenrafilho@usp.br</email>

CSIM Laboratory  
University of Sao Paulo  
CAPES Brazil  

Module Description

This module offers a simple application of the AAD filter on diffusion-weighted imaging data. Since the noise through the image space is present in the tensorial acquisition, the AAD filter could be applied in order to decrease the noise amplitude and maintain the geometrical details of the image.

NOTE: This module works with NRRD DWI volumes. If your diffusion data is on different file formats (e.g. FSL output files), please convert then in advance. A useful tool to do that on 3D Slicer is the DWI Converter

Use Cases

  • Use Case 1: Decrease noise in DWI data
    • The raw DWI data could be filtered using the AAD approach, which was previously applied on structural MRI data[1] and Diffusion Tensor Imaging images as well[2]

Tutorials

N/A

Panels and their use

User Interface

IO:

  • Input Volume
    • Select the DWI input image
  • Output Volume
    • Set the output DWI image file which the filters should place the final result

Diffusion Parameters:

  • Conductance
    • A float value for edge preservation adjustment. The conductance regulates the diffusion intensity in the neighbourhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space. If you want an automatic evaluation to conductance variable, please select the following methods
  • Use Auto Conductance
    • Choose if you want to use an automatic adjustment of conductance parameter. If this is checked, the inserted value is ignored and the optimization function below is used.
  • Optmization Function
    • A set of optimization function for automatic estimation of conductance parameter. This is helpful is you do not have an initial guess on what value is appropriate to the conductance setting. (Canny, MAD and Morphological). Please see the Insight-Journal article that explain each of these automatic conductance adjusment methods.
  • Number of Iteractions
    • A integer value that defines the number of iterations.
  • Time Step
    • A double value that regulates the numerical stability of the algorithm. It is safe to maintain the upper limit with the formulation given by Anisotropic Diffusion algorithm (See GradientAnisotropiDiffusionImageFilter in ITK documentation).
  • Anomalous Parameter
    • A double value that defines the q-Gaussian probability distribution function which is convoluated in each neighborhood in the image.

Similar Modules

References

  • Senra Filho, A.C. da S. et al., 2017. Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter. Research on Biomedical Engineering, 33(3), pp.247–258. DOI: 10.1590/2446-4740.02017
  • da S Senra Filho, A.C., Garrido Salmon, C.E. & Murta Junior, L.O., 2015. Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), pp.2355–2373. DOI: 10.1088/0031-9155/60/6/2355


Information for Developers

  1. Da S Senra Filho, A. C., Garrido Salmon, C. E., & Murta Junior, L. O. (2015). Anomalous diffusion process applied to magnetic resonance image enhancement. Physics in Medicine and Biology, 60(6), 2355–2373. doi:10.1088/0031-9155/60/6/2355
  2. Senra Filho, A. C. da S. et al. (2017) Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter, Research on Biomedical Engineering. doi: 10.1590/2446-4740.02017.