Difference between revisions of "Projects:AtlasBasedDTIFiberAnalyzerFramework"

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=  Atlas Based DTI FIber Analyzer Framework =
 
=  Atlas Based DTI FIber Analyzer Framework =
  
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This projects gather some tools aiming at completing each step required to build an Atlas. Concerning the first step as the creation and the mapping of the Atlas, DWI QC will be the appropriate tool. Then comes the tractography followed by a postprocessing step easily done by FiberViewer Light. Finally we will gather statistics thanks to DTI Atlas Fiber Analyzer.
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This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.
  
 
= Description =
 
= Description =
 
[[Image:AtlasBuilderLogo.jpg|thumb|300px|Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest]]
 
[[Image:AtlasBuilderLogo.jpg|thumb|300px|Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest]]
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As regards the post-processing step, We developped FiberViewer Light. It includes every clustering methods of the full version FiberViewer such as : Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm. As in the full version you can also display a plane on the fiber. This tool works faster than the full version due to simplified visualizations.
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Finally, DTI Atlas Fiber Analyzer allows users to gather every information about a fiber by calling FiberProcess or DtiTractStat. Besides you can plot every statistical data such as FA or MD. Then, thanks to the tool MergeStatWithFiber you can insert those statistics on the original Fiber File in order to display it on Slicer.
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The general framework entails the following steps:
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'''DWI and DTI quality control: '''
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DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies.
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We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation.
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'''Unbiased DTI atlas building or atlas mapping'''
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*Unbiased DTI atlas building
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*Mapping of an existing DTI atlas
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'''Tractography within 3D Slicer'''
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* 3D Slicer modules: Label seeding and ROI select
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* Tractography with unscented kalman filter
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'''Fiber cleanup/clustering: '''
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FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.
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'''DTIAtlasFiberAnalyzer'''
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'''Statistical analysis performed by statistician'''
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'''Merging statistics back to the original fiber bundle'''
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MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.
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'''3D visualization within 3D Slicer'''
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Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.  
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= Publications =
 
= Publications =
  
 
= Key Investigators =
 
= Key Investigators =
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* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Martin Styner
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* UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
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* Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig
  
 
= Links =
 
= Links =
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*[http://www.niral.unc.edu/download-software UNC NIRAL software download page]
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*[http://www.nitrc.org/projects/dti_brain_atlas Human brain DTI atlas]
  
 
  [[Category: Diffusion MRI]]
 
  [[Category: Diffusion MRI]]

Revision as of 22:39, 28 November 2011

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Atlas Based DTI FIber Analyzer Framework

This project aims to define an automatic framework for statistical comparison of fiber bundle diffusion properties between populations of diffusion weighted images.

Description

Length analysis of Cingulum tractography. Colors go from red to blue where red ones are the longest and blue ones are the shortest

The general framework entails the following steps:

DWI and DTI quality control: DWI data suffers from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. We developed a tool called DTIPrep which pipelines the QC steps with designated protocol use and report generation.

Unbiased DTI atlas building or atlas mapping

  • Unbiased DTI atlas building
  • Mapping of an existing DTI atlas

Tractography within 3D Slicer

  • 3D Slicer modules: Label seeding and ROI select
  • Tractography with unscented kalman filter


Fiber cleanup/clustering: FiberViewerLight enables several clustering methods: Length, Gravity, Hausdorff, and Mean methods but also a Normalized Cut algorithm.


DTIAtlasFiberAnalyzer

Statistical analysis performed by statistician

Merging statistics back to the original fiber bundle MergeStatWithFiber - an application part of DTI Fiber Tracts Statistics package-, allows population statistical information to be merged back to the atlas fiber bundle.

3D visualization within 3D Slicer Statically significan group differences can directly be displayed on a fiber bundle in 3D Slicer.


Publications

Key Investigators

  • UNC Algorithms: Jean-Baptiste Berger, Benjamin Yvernault, Clement Vachet, Yundi Shi, Aditya Gupta, Martin Styner
  • Utah Algorithms: Anuja Sharma, Sylvain Gouttard, Guido Gerig

Links

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