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:USP-logo.png|University of Sao Paulo}}
 
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{{documentation/{{documentation/version}}/module-section|Extension Description}}
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{|
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[[Image:DiffusionComplexityMap-logo.png|left]]
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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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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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* '''Diffusion Complexity Map (DC)''': [[Documentation/{{documentation/version}}/Modules/DCMapping|DC Mapping]]
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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|Panels and their use}}
 
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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}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]]
  
 
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{{documentation/{{documentation/version}}/extension-section|References}}
 
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*  
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* Manuscript in review process
  
 
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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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