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

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This work was partially funded by CAPES and CNPq, a Brazillian Agencies. Information on CAPES can be obtained on the [http://www.capes.gov.br/ CAPES website] and [http://www.cnpq.br/ CNPq website].<br>
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This work was funded by University of Campinas, Brazil. More information on the website [https://www.unicamp.br/ Unicamp website].<br>
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Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)<br>
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Author: Antonio Carlos da S. Senra Filho, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )<br>
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Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@usp.br</email><br>
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Author: Andre Monteiro Paschoal, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )<br>
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Author: Luiz Otávio Murta Junior, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics )<br>
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Contact: Antonio Carlos da S. Senra Filho, email: acsenrafilho@alumni.usp.br<br>
 
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|Image:CSIM-logo.png|CSIM Laboratory  
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[[Image:AnomalousDiffusionExtension-logo.png|left]]
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[[Image:DiffusionComplexityMap-logo.png|left]]
  
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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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Diffusion-weighted images (DWI) and Diffusion Tensor Imaging (DTI) are well-known and powerful imaging techniques in MRI. For DTI images, the most used measurements are the fractional anisotropy (FA) and apparent diffusion coefficient (ADC). However, the limitations of FA and ADC formalism are also vastly debated due to low tissue contrast for ADC maps and measurement artifacts present in crossing-fiber orientation for FA maps. Although the DTI evaluation has evolved continually in recent years, there are still struggles regarding the quantitative measurement that can benefit brain areas that are consistently difficult to measure on diffusion-based methods, e.g., the grey matter (GM). The present Slicer extension proposes offer an image processing technique using the principle of diffusion distribution evaluation regarding the LMC complexity measure, named Diffusion Complexity (DC). <ref>Manuscript in revire process.</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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* '''Diffusion Complexity Map (DC)''': [[Documentation/{{documentation/version}}/Modules/DCMapping|DC Mapping]]
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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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Most frequently used for these scenarios:
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* Use Case 1: Obtain a complementary scalar information using clinical DTI image acquisition protocol (DC Map)
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* Use Case 1: Noise reduction as a pre-processing step for tissue segmentation
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**The DC map is a new scalar mapping that can give an additional information to analyse diffusion image sequences, without changing the MRI imaging protocol.
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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: Gain focus on Gray Matter analysis using diffusion images
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* Use Case 2: Volume rendering
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**DC map has a signal peak in GM tissue, which can be important to discriminate brain diseases in this particular brain tissue that is challenging to other classical DTI maps (e.g. FA and ADC)
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**Noise reduction will result in nicer looking volume renderings
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* Use Case 3: Interpret the diffusion data in light of statistical physics information theory
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* Use Case 3: Noise reduction as part of image processing pipeline
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**The DC map is based on the López-Ruiz, Mancini, and Calbet (LMC) information theory definition, giving the contribution of classical entropy (Shannon's entropy) and the disequilibrium function. Hence, another way to interpret the diffusion data can be given by this new technique.
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**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">
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Image:MRI_raw.png|Raw T1 weighted MRI Image
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Image:FA_diff_example.png|Axial slice FA map example
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Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
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Image:ADC_diff_example.png|Axial slice ADC map example
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Image:MRI_IAD.png|T1 weighted MRI Image with IAD filter (q=1.2)
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Image:DC_diff_example.png|Axial slice DC map example
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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)
 
 
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{{documentation/{{documentation/version}}/extension-section|Similar Extensions}}
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
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*[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion|Gradient Anisotropic Diffusion]]
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*[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]]
  
 
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{{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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* Manuscript in review process
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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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Latest revision as of 11:29, 13 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

CSIM Laboratory  
LOAMRI Laboratory  
University of Campinas  
University of Sao Paulo  

Extension Description

DiffusionComplexityMap-logo.png

Diffusion-weighted images (DWI) and Diffusion Tensor Imaging (DTI) are well-known and powerful imaging techniques in MRI. For DTI images, the most used measurements are the fractional anisotropy (FA) and apparent diffusion coefficient (ADC). However, the limitations of FA and ADC formalism are also vastly debated due to low tissue contrast for ADC maps and measurement artifacts present in crossing-fiber orientation for FA maps. Although the DTI evaluation has evolved continually in recent years, there are still struggles regarding the quantitative measurement that can benefit brain areas that are consistently difficult to measure on diffusion-based methods, e.g., the grey matter (GM). The present Slicer extension proposes offer an image processing technique using the principle of diffusion distribution evaluation regarding the LMC complexity measure, named Diffusion Complexity (DC). [1].


Modules


Use Cases

  • Use Case 1: Obtain a complementary scalar information using clinical DTI image acquisition protocol (DC Map)
    • The DC map is a new scalar mapping that can give an additional information to analyse diffusion image sequences, without changing the MRI imaging protocol.
  • Use Case 2: Gain focus on Gray Matter analysis using diffusion images
    • DC map has a signal peak in GM tissue, which can be important to discriminate brain diseases in this particular brain tissue that is challenging to other classical DTI maps (e.g. FA and ADC)
  • Use Case 3: Interpret the diffusion data in light of statistical physics information theory
    • The DC map is based on the López-Ruiz, Mancini, and Calbet (LMC) information theory definition, giving the contribution of classical entropy (Shannon's entropy) and the disequilibrium function. Hence, another way to interpret the diffusion data can be given by this new technique.

  • Axial slice FA map example

  • Axial slice ADC map example

  • Axial slice DC map example


Similar Modules

References

  • Manuscript in review process

Information for Developers

Work in progress
 Section under construction.


Repositories:

  1. ↑ Manuscript in revire process.
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