Acsenrafilho at 12:07, 15 April 2024

2024-04-15T12:07:09Z

Acsenrafilho: Adding general information

2024-03-13T11:43:59Z

Adding general information

Acsenrafilho: Created page with "{{documentation/versioncheck}} {{documentation/{{documentation/version}}/module-header}}

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 {{documentation/{{documentation/version}}/module-section|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
+*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
+*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.
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 [[Image:dc_map_gui.png|thumb|380px|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)
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 '''Additional Parameters:'''
 *'''Disequilibrium Function'''
 **A set of disequilibrium functions that could be used. (Tip: The LMC complexity measure uses the Euclidean function)
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 {{documentation/{{documentation/version}}/module-section|Similar Modules}}
 *[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]]
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 {{documentation/{{documentation/version}}/module-section|References}}
 * Manuscript in review process
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-Extension: [[Documentation/{{documentation/version}}/Extensions/AnomalousFilters|AnomalousFilters]]<br>
+Extension: [[Documentation/{{documentation/version}}/Extensions/DiffusionComplexityMap|Diffusion Complexity Map]]<br>
-Webpage: http://dcm.ffclrp.usp.br/csim/<br>
+Webpage: https://loamri.com/<br>
-Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)<br>
+Author: Antonio Carlos da S. Senra Filho, LOAMRI Laboratory (University of Campinas, Department of Cosmic Rays and Chronology)<br>
-Contact: Antonio Carlos da S. Senra Filho, <email>acsenrafilho@usp.br</email><br>
+Contact: Antonio Carlos da S. Senra Filho, email: acsenrafilho@alumni.usp.br<br>
 {{documentation/{{documentation/version}}/module-introduction-row}}
 {{documentation/{{documentation/version}}/module-introduction-logo-gallery
-|Image:CSIM-logo.png|CSIM Laboratory
+|Image:CSIM-logo.png| CSIM Laboratory
 |Image:USP-logo.png|University of Sao Paulo
 }}
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 {{documentation/{{documentation/version}}/module-section|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.
+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}}
-* Use Case 1: Noise reduction as a preprocessing step for tissue segmentation
+* Use Case 1: Obtain a complementary scalar information using clinical DTI image acquisition protocol (DC Map)
-**When dealing with single voxel classification schemes running noise reduction as a preprocessing scheme will reduce the number of single misclassified voxels.
+**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: Preprocessing to volume rendering
+* Use Case 2: Gain focus on Gray Matter analysis using diffusion images
-**Noise reduction will result in nicer looking volume renderings
+**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: Noise reduction as part of image processing pipeline
+* Use Case 3: Interpret the diffusion data in light of statistical physics information theory
-**Could offer a better segmentation and classification on specific brain image analysis such as in Multiple Sclerosis lesion segmentation
+**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">
-Image:MRI_raw.png|Raw T1 weighted MRI Image
+Image:FA_diff_example.png|Axial slice FA map example
-Image:MRI_AAD.png|T1 weighted MRI Image with AAD filter (q=1.2)
+Image:ADC_diff_example.png|Axial slice ADC map example
 </gallery>
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 {{documentation/{{documentation/version}}/module-section|Panels and their use}}
-[[Image:aad_scalar_gui.png|thumb|380px|User Interface]]
+[[Image:dc_map_gui.png|thumb|380px|User Interface]]
 '''IO:'''
-*'''Input Volume'''
+*'''Input DWI Volume'''
-**Select the input image
+**Input DWI sequence volume. (Tip: Use DWiConvert module to create an NRRD DWI sequence file from other image file types)
-*'''Output Volume'''
+*'''Brain Mask'''
-**Set the output image file which the filters should place the final result
+**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.
-'''Diffusion Parameters:'''
+'''Additional Parameters:'''
-*'''Conductance'''
+*'''Disequilibrium Function'''
-**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.
+**A set of disequilibrium functions that could be used. (Tip: The LMC complexity measure uses the Euclidean function)
-*'''Use Auto Conductance'''
+*'''Q Value'''
-**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.
+**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).
-*'''Optimization Function'''
+*'''Use Manual Number of Bins'''
-**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.
+**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 Iteractions'''
+*'''Number of Bins'''
-**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.
+**A integer value that defines the number of bins used in the histogram estimate. This parameter is used only when the --useManualBins is True
 <!-- ---------------------------- -->
 {{documentation/{{documentation/version}}/module-section|Similar Modules}}
-*[[Documentation/{{documentation/version}}/Modules/IADImageFilter|IAD Image Filter]]
+*[[Documentation/{{documentation/version}}/Modules/DiffusionTensorScalarMeasurements|Diffusion Tensor Scalar Measurements]]
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 {{documentation/{{documentation/version}}/module-section|References}}
-* 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
+* Manuscript in review process
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Documentation/Nightly/Modules/DCMapping - Revision history

Acsenrafilho at 12:07, 15 April 2024

Acsenrafilho: Adding general information

Acsenrafilho: Created page with "{{documentation/versioncheck}} {{documentation/{{documentation/version}}/module-header}}