Documentation/4.6/Extensions/AnomalousFilters

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Introduction and Acknowledgements

This work was partially funded by CAPES and CNPq, a Brazillian Agencies. Information on CAPES can be obtained on the CAPES website and CNPq website. 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   CNPq Brazil   CAPES Brazil

Extension Description

Anomalous diffusion processes (ADP) are mathematically denoted by a power law in the Fokker-Planck equation, leading to the generalized form. There are several generalizations of the Fokker-Plank equation, which should give many different partial differential equations (PDEs). Here we adopt only the so-called porous media form, allowing the super-diffusive and the subdiffusive processes. In porous media, channels are created promoting or blocking the flow of the density function, which has been proved to provide a suitable application on MRI filtering.

Basically, there are two different filters already implementing the anomalous diffusion process: the isotropic anomalous diffusion and anisotropic anomalous diffusion filters. Each of these filters were already applied on different imaging MR modalities, such as structural T1 and T2 images, diffusion weighted images (DWI and DTI), MRI relaxation T1 and T2 relaxometry and in fMRI as an initial study.

Modules

• AAD Image Filter
• IAD Image Filter
• AAD on DWI Image

Use Cases

Most frequently used for these scenarios:

• Use Case 1: Noise reduction as a preprocessing step for tissue segmentation
  • When dealing with single voxel classification schemes running noise reduction as a preprocessing scheme will reduce the number of single misclassified voxels.
• Use Case 2: Preprocessing to volume rendering
  • Noise reduction will result in nicer looking volume renderings
• Use Case 3: Noise reduction as part of image processing pipeline
  • Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation

• 

  Raw T1 weighted MRI Image

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  T1 weighted MRI Image with AAD filter (q=1.2)

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  T1 weighted MRI Image with IAD filter (q=1.2)

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  DTI-FA map without image filtering process

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  DTI-FA map with AAD image filtering (q=0.4)

Similar Extensions

N/A

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
• Filho, A.C. da S.S. et al., 2014. Anisotropic Anomalous Diffusion Filtering Applied to Relaxation Time Estimation in Magnetic Resonance Imaging. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 3893–3896.
• Filho, A.C. da S.S., Barizon, G.C. & Junior, L.O.M., 2014. Myocardium Segmentation Improvement with Anisotropic Anomalous Diffusion Filter Applied to Cardiac Magnetic Resonance Imaging. In Annual Meeting of Computing in Cardiology.
• Filho, A.C. da S.S. et al., 2014. Brain Activation Inhomogeneity Highlighted by the Isotropic Anomalous Diffusion Filter. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, pp. 3313–3316.
• Senra Filho, A.C. da S., Duque, J.J. & Murta, L.O., 2013. Isotropic anomalous filtering in Diffusion-Weighted Magnetic Resonance Imaging. I. E. in M. and B. Society, ed. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013, pp.4022–5.

Information for Developers

Repositories:

• Source code: GitHub repository
• Issue tracker: open issues and enhancement requests