Documentation/Nightly/Modules/AADDiffusionWeightedData

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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.

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 and Diffusion Tensor Imaging images as well

• MRI DTI raw.png

  Raw DTI data

• MRI DTI AAD.png

  DTI data filtered with AAD filter (q=1.2)

Tutorials

N/A

Panels and their use

User Interface

IO:

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

Diffusion Parameters:

• Conductance
  • 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.
• Number of Iteractions

Similar Modules

• Joint Rician LMMSE Image Filter

References

• 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