Difference between revisions of "Documentation/Nightly/Modules/DCMapping"

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Extension: [[Documentation/{{documentation/version}}/Extensions/AnomalousFilters|AnomalousFilters]]<br>
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Extension: [[Documentation/{{documentation/version}}/Extensions/DiffusionComplexityMap|Diffusion Complexity Map]]<br>
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Webpage: http://dcm.ffclrp.usp.br/csim/<br>
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Webpage: https://loamri.com/<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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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
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|Image:Unicamp-logo.png| University of Campinas
 
|Image:USP-logo.png|University of Sao Paulo
 
|Image:USP-logo.png|University of Sao Paulo
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|Image:CAPES-logo.png|CAPES Brazil
 
 
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{{documentation/{{documentation/version}}/module-section|Module Description}}
 
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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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This module offer the Diffusion Complexity (DC) scalar map, which is able to calculate the diffusion complexity measure using several statistical physics approaches. This method assumes the standard diffusion MRI acquisition protocol for DTI images. Detail about the method can be found at the paper<ref>Manuscript in revire process.</ref>
  
 
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{{documentation/{{documentation/version}}/module-section|Use Cases}}
 
{{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}}
 
{{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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[[Image:dc_map_gui.png|thumb|380px|User Interface]]
 
'''IO:'''
 
'''IO:'''
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*'''Input Volume'''
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*'''Input DWI Volume'''
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**Select the input image
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**Input DWI sequence volume. (Tip: Use DWiConvert module to create an NRRD DWI sequence file from other image file types)
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*'''Output Volume'''
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*'''Brain Mask'''
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**Set the output image file which the filters should place the final result
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**Brain mask volume informing the image regions that should be assumed as the total image space were the complexity calculations should be made. If left blank, the entire input image will be used as input.
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*'''DC Mapping'''
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**Output Volume representing the Diffusion Complexity (DC) map.
  
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'''Diffusion Parameters:'''
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'''Additional Parameters:'''
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*'''Conductance'''
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*'''Disequilibrium Function'''
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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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**A set of disequilibrium functions that could be used. (Tip: The LMC complexity measure uses the Euclidean function)
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*'''Use Auto Conductance'''
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*'''Q Value'''
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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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**A double value that defines the q-Statistics probability distribution function which is used in the image entropy calculation. For Q=1, the Shannon's entropy function is adopted (default).
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*'''Optimization Function'''
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*'''Use Manual Number of Bins'''
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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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**Choose if you want to use set manually the number of bins to represent the diffusion histogram estimate. If not, an automatic adjustment is adopted.
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*'''Number of Iteractions'''
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*'''Number of Bins'''
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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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**A integer value that defines the number of bins used in the histogram estimate. This parameter is used only when the --useManualBins is True
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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}}
 
{{documentation/{{documentation/version}}/module-section|Similar Modules}}
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*[[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]]
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*[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]]
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*[[Documentation/{{documentation/version}}/Modules/GradientAnisotropicDiffusion|Gradient Anisotropic Image Filter]]
 
  
 
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{{documentation/{{documentation/version}}/module-section|References}}
 
{{documentation/{{documentation/version}}/module-section|References}}
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* Senra Filho, A.C. & Murta Junior, L. O., 2017. Automatic Conductance Estimation Methods for Anisotropic Diffusion ITK Filters. Insight-Journal. website: http://www.insight-journal.org/browse/publication/983
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* Manuscript in review process
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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
 
−
* 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.
 
−
* 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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Revision as of 11:43, 13 March 2024

Home < Documentation < Nightly < Modules < DCMapping


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


Introduction and Acknowledgements

Extension: Diffusion Complexity Map
Webpage: https://loamri.com/
Author: Antonio Carlos da S. Senra Filho, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology)
Contact: Antonio Carlos da S. Senra Filho, email: acsenrafilho@alumni.usp.br

CSIM Laboratory  
LOAMRI Laboratory  
University of Campinas  
University of Sao Paulo  

Module Description

This module offer the Diffusion Complexity (DC) scalar map, which is able to calculate the diffusion complexity measure using several statistical physics approaches. This method assumes the standard diffusion MRI acquisition protocol for DTI images. Detail about the method can be found at the paper[1]

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


Panels and their use

User Interface

IO:

  • Input DWI Volume
    • Input DWI sequence volume. (Tip: Use DWiConvert module to create an NRRD DWI sequence file from other image file types)
  • Brain Mask
    • Brain mask volume informing the image regions that should be assumed as the total image space were the complexity calculations should be made. If left blank, the entire input image will be used as input.
  • DC Mapping
    • Output Volume representing the Diffusion Complexity (DC) map.

Additional Parameters:

  • Disequilibrium Function
    • A set of disequilibrium functions that could be used. (Tip: The LMC complexity measure uses the Euclidean function)
  • Q Value
    • A double value that defines the q-Statistics probability distribution function which is used in the image entropy calculation. For Q=1, the Shannon's entropy function is adopted (default).
  • Use Manual Number of Bins
    • Choose if you want to use set manually the number of bins to represent the diffusion histogram estimate. If not, an automatic adjustment is adopted.
  • Number of Bins
    • A integer value that defines the number of bins used in the histogram estimate. This parameter is used only when the --useManualBins is True

Similar Modules

References

  • Manuscript in review process

Information for Developers

Work in progress
 Section under construction.
  1. ↑ Manuscript in revire process.
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