Difference between revisions of "Documentation/Nightly/Extensions/DiffusionComplexityMap"

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This module offer a simple application to the Anisotropic Anomalous Diffusion (AAD) filter, which is able to increase the image SNR and preserve fine object's details around the image space. This method was studied on MRI structural images (T1 and T2), which other imaging modalities could be properly investigated in the future.
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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 adopted the so-called porous media equation, allowing the super-diffusive and the sub-diffusive 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 for MRI noise attenuation <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>. 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 to 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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* '''Structural image denoising with tissues border preservation function''': [[Documentation/{{documentation/version}}/Modules/AADImageFilter|AAD Image Filter]]
 
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* '''Structural image denoising without tissues border preservation function''': [[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]]
 
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* '''Diffusion-weighted MR image denoising with tissues border preservation''': [[Documentation/{{documentation/version}}/Modules/AADDiffusionWeightedData|AAD on DWI Image]]
 
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* '''Echo-planar imaging denoising with tissues border preservation (fMRI and ASL)''': [[Documentation/{{documentation/version}}/Modules/AADEPIData|AAD on EPI Image]]
 
  
 
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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 tissue segmentation
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* Use Case 1: Noise reduction as a pre-processing step for tissue 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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**When dealing with single voxel classification schemes, a noise reduction pre-processing step is usually helpful to reduce data fluctuation due to acquisition artifacts (e.g. reducing the number of misclassified voxels).
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* Use Case 2: Preprocessing to volume rendering
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* Use Case 2: Volume rendering
 
 
**Noise reduction will result in nicer looking volume renderings
 
**Noise reduction will result in nicer looking volume renderings
 
* Use Case 3: Noise reduction as part of image processing pipeline
 
* 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
 
**Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation
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<gallery widths="400px" heights="400px" perrow="3">
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<gallery widths="300px" perrow="3">
 
Image:MRI_raw.png|Raw T1 weighted MRI Image
 
Image:MRI_raw.png|Raw T1 weighted MRI Image
 
Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
 
Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
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Image:MRI_IAD.png|T1 weighted MRI Image with IAD filter (q=1.2)
 
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Image:DTI_FA_raw.png|DTI-FA map without image filtering process
 
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Image:DTI_FA_AAD.png|DTI-FA map with AAD image filtering (q=0.4)
 
 
</gallery>
 
</gallery>
  
 
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{{documentation/{{documentation/version}}/extension-section|Similar Extensions}}
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{{documentation/{{documentation/version}}/module-section|Panels and their use}}
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*[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion|Gradient Anisotropic Diffusion]]
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[[Image:aad_scalar_gui.png|thumb|380px|User Interface]]
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'''IO:'''
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*'''Input Volume'''
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**Select the input image
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*'''Output Volume'''
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**Set the output image file which the filters should place the final result
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'''Diffusion Parameters:'''
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*'''Conductance'''
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**The conductance regulates the diffusion intensity in the neighborhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
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*'''Use Auto Conductance'''
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**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.
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*'''Optimization Function'''
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**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 [http://www.insight-journal.org/browse/publication/983 Insight-Journal article] that explain each of these automatic conductance adjustment methods.
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*'''Number of Iteractions'''
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**The number of iterations regulates the numerical simulation of the anomalous process over the image. This parameters is also related with the de-noising intensity, however it is more sensible to the noise intensity. Choose the higher number of iterations if the image presents high intensity noise which is not well treated by the conductance parameter.
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*'''Time Step'''
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**The time step is a normalization parameters for the numerical simulation. The maximum value, given as default, is set to 3D images. Lower time step restrict the numerical simulation of the anomalous process.
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*'''Anomalous parameter'''
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**The anomalous parameter (or q value) is the generalization parameters responsible to give the anomalous process approach on the diffusion equation. See the reference paper<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> to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
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*[[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]] TODO Colocar outros links
  
 
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{{documentation/{{documentation/version}}/extension-section|References}}
 
{{documentation/{{documentation/version}}/extension-section|References}}
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* 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
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*  
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* 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.
 
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* 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.
 
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* 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.
 
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* 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.
 
  
 
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Repositories:
 
Repositories:
  
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*Source code: [https://github.com/CSIM-Toolkits/AnomalousFiltersExtension GitHub repository]
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*Source code: [https://github.com/CSIM-Toolkits/SlicerDiffusionComplexityMap GitHub repository]
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*Issue tracker:  [https://github.com/CSIM-Toolkits/AnomalousFiltersExtension/issues open issues and enhancement requests]
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*Issue tracker:  [https://github.com/CSIM-Toolkits/SlicerDiffusionComplexityMap/issues open issues and enhancement requests]
  
 
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Revision as of 12:36, 12 March 2024

Home < Documentation < Nightly < Extensions < DiffusionComplexityMap


For the latest Slicer documentation, visit the read-the-docs.


Introduction and Acknowledgements

This work was funded by University of Campinas, Brazil. More information on the website Unicamp website.
Author: Antonio Carlos da S. Senra Filho, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )
Author: Andre Monteiro Paschoal, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )
Author: Luiz Otávio Murta Junior, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics )
Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@alumni.usp.br</email>

CSIM Laboratory  
LOAMRI Laboratory  
University of Campinas  
University of Sao Paulo  


Module Description

This module offer a simple application to the Anisotropic Anomalous Diffusion (AAD) filter, which is able to increase the image SNR and preserve fine object's details around the image space. This method was studied on MRI structural images (T1 and T2), which other imaging modalities could be properly investigated in the future.

Use Cases

  • 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

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

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 neighborhood area. Choose a higher conductance if the input image has strong noise seem in the whole image space.
  • 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.
  • Optimization 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 adjustment methods.
  • Number of Iteractions
    • The number of iterations regulates the numerical simulation of the anomalous process over the image. This parameters is also related with the de-noising intensity, however it is more sensible to the noise intensity. Choose the higher number of iterations if the image presents high intensity noise which is not well treated by the conductance parameter.
  • Time Step
    • The time step is a normalization parameters for the numerical simulation. The maximum value, given as default, is set to 3D images. Lower time step restrict the numerical simulation of the anomalous process.
  • Anomalous parameter
    • The anomalous parameter (or q value) is the generalization parameters responsible to give the anomalous process approach on the diffusion equation. See the reference paper[1] to choose the appropriate q value (at moment, only tested in MRI T1 and T2 weighted images).


Similar Modules

References

Information for Developers

Work in progress
 Section under construction.


Repositories:

  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
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