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| − | {{documentation/{{documentation/version}}/module-section|Introduction and Acknowledgements}}
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| − | This work was partially funded by CAPES, a Brazillina Finantial Agency. Information on CAPES can be obtained from the [http://www.capes.gov.br/ CAPES website].<br>
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| − | Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory<br>
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| − | Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@usp.br</email><br>
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| − | |Image:CSIM-logo.png|GE
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| − | {{documentation/{{documentation/version}}/module-section|Module Description}}
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| − | 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. <ref>Tsallis, C. (2009). Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World. Springer.</ref> 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.<ref>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</ref>
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| − | Basically, there are two different filters already implementing the anomalous diffusion process: the isotropic anomalous diffusion and anisotropic anomalous diffusion filters.<ref>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</ref> Each of these filters were already applied on different imaging MR modalities, such as structural T1 and T2 images<ref>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</ref>, diffusion weighted images (DWI and DTI)<ref>Senra Filho, A. C. da S., Duque, J. J., & Murta, L. O. (2013). Isotropic anomalous filtering in Diffusion-Weighted Magnetic Resonance Imaging. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013, 4022–5. doi:10.1109/EMBC.2013.6610427</ref><ref>Senra Filho, A. C. da S., Simozo, F. H., Salmon, C. E. G., & Murta Junior, L. O. (2014). Anisotropic anomalous filter as a tool for decreasing patient exam time in diffusion-weighted MRI protocols. In XXIV Brazilian Congress on Biomedical Engineering (pp. 0–3). Uberlandia.</ref>, MRI relaxation T1 and T2 relaxometry<ref>Filho, A. C. da S. S., Barbosa, J. H. O., Salmon, C. E. G. S., & Junior, L. O. M. (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 (pp. 3893–3896). IEEE. doi:10.1109/EMBC.2014.6944474</ref> and in fMRI<ref>Filho, A. C. da S. S., Rondinoni, C., Santos, A. C. dos, & Junior, L. O. M. (2014). Brain Activation Inhomogeneity Highlighted by the Isotropic Anomalous Diffusion Filter. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3313–3316). Chicago: IEEE. doi:10.1109/EMBC.2014.6944331</ref> as an initial study.
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| − | {{documentation/{{documentation/version}}/module-section|Use Cases}}
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| − | Most frequently used for these scenarios:
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| − | * Use Case 1: Noise reduction as a preprocessing step for segmentation
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| − | **when dealing with single voxel classification schemes running noise reduction as a preprocessing scheme will reduce the number of single misclassified voxels.
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| − | * Use Case 2: Preprocessing to volume rendering
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| − | **Noise reduction will result in nicer looking volume renderings
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| − | <gallery>
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| − | Image:MRIUnfiltered.png|Unfiltered MRI image
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| − | Image:MRIGradientAnisotropicDiffusion.png|Gradient filtered with conductance=1 and iterations=10
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| − | Image:LungVolumeRenderingUnfilteredDetail.png|Volume Rendering Unfiltered CT
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| − | Image:LungVolumeRenderingFilteredDetail.png|Volume Rendering Filtered CT
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| − | </gallery>
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| − | {{documentation/{{documentation/version}}/module-section|Similar Modules}}
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| − | {{documentation/{{documentation/version}}/module-section|Information for Developers}}
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