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

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Author: Andre Monteiro Paschoal, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )<br>
 
Author: Andre Monteiro Paschoal, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology )<br>
 
Author: Luiz Otávio Murta Junior, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics )<br>
 
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</email><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:CSIM-logo.png| CSIM Laboratory
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|Image:LOAMRI-logo.png|LOAMRI Laboratory|Image:Unicamp-logo.png|University of Campinas|Image:USP-logo.png|University of Sao Paulo}}
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|Image:LOAMRI-logo.png| LOAMRI Laboratory
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|Image:Unicamp-logo.png| University of Campinas
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[[Image:DiffusionComplexityMap-logo.png|left]]
 
[[Image:DiffusionComplexityMap-logo.png|left]]
  
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XXX <ref>Tsallis, C. (2009). Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World. Springer.</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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{{documentation/{{documentation/version}}/module-section|Use Cases}}
 
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* Use Case 1: Noise reduction as a preprocessing step for tissue segmentation
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* Use Case 1: Obtain a complementary scalar information using clinical DTI image acquisition protocol (DC Map)
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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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**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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* Use Case 2: Preprocessing to volume rendering
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* Use Case 2: Gain focus on Gray Matter analysis using diffusion images
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**Noise reduction will result in nicer looking volume renderings
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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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* Use Case 3: Noise reduction as part of image processing pipeline
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* Use Case 3: Interpret the diffusion data in light of statistical physics information theory
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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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**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.
  
 
<gallery widths="300px" perrow="3">
 
<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:DC_diff_example.png|Axial slice DC map example
 
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{{documentation/{{documentation/version}}/module-section|Similar Modules}}
 
{{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}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]]
  
 
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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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