Difference between revisions of "Summer project week 2011 Finsler Streamlines"

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(Created page with '__NOTOC__ <gallery> Image:PW-SLC2011.png|Projects List Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus…')
 
 
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<gallery>
 
<gallery>
 
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
 
Image:PW-SLC2011.png|[[2011_Winter_Project_Week#Projects|Projects List]]
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Image:genuFAp.jpg|Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.
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Image:screen1.png | Step 1 - You can start by loading a DWI image (should be HARDI) and a mask (not necessary, but very convenient to accelerate computations).
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Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
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Image:screen2.png | Step 2 - Use the segmentation editor (or load any other label map at your convenience). This method requires target regions as well as seeding regions.
 +
Image:screen3.png | Step 3 - With the Finsler tractography module, generate a cost map ("Output scalar volume" in the picture) and a map of arrival directions ("Output directions volume").
 +
Image:screen4.png | Step 4 - This is how the costs map looks like. Now, you can switch to the Finsler Backtracing module using as input the costs and directions generated in Step 3. Choose a target region different from the seeding region.
 +
Image:screen5.png | Step 5 - The fiber bundles are drawn in Slicer's 3-D view.
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Image:screen6.png | Some other examples. Fibers are always saved to disk in RAS, and colored according to the value of the Finsler cost at the points they go through.
 
</gallery>
 
</gallery>
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==Instructions for Use of this Template==
 
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#Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
 
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#Copy the entire text of this page into the page created above
 
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#Link the created page into the list of projects for the project event
 
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#Delete this section from the created page
 
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#Send an email to tkapur at bwh.harvard.edu if you are stuck
 
  
 
==Key Investigators==
 
==Key Investigators==
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* LMI: Antonio Tristán-Vega
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* Utah: Tom Fletcher, Ross Whitaker
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* LMI: Demian Wasserman
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* LMI: Carl-Fredrik Westin
  
 
<div style="margin: 20px;">
 
<div style="margin: 20px;">
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<h3>Objective</h3>
 
<h3>Objective</h3>
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We are developing methods for analyzing diffusion tensor data along fiber tracts. The goal is to be able to make statistical group comparisons with fiber tracts as a common reference frame for comparison.
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In the last project week we delivered an implementation of the Finsler method to compute the connectivity among regions in the white matter through High Angular Resolution Diffusion Imaging. Such method provides a costs map from a given seeding point/region to any other point within the brain. The aim in this project is tracing the minimum cost paths between two given regions in the white matter, which will in turn provide the desired streamlines.
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
  
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Our approach for analyzing diffusion tensors is summarized in the IPMI 2007 reference below. The main challenge to this approach is <foo>.
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The method is described in detail in the references below. To compute the costs map we use the Fast Sweeping algorithm: upon convergence, this method provides the minimum cost at each image voxel together with the direction such cost was reached from. Thus, the "backtracing" of these directions from a given point to the seeding point/region provides the minimum cost path.
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Our plan for the project week is to first try out <bar>,...
 
  
 
</div>
 
</div>
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<h3>Progress</h3>
 
<h3>Progress</h3>
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Software for the fiber tracking and statistical analysis along the tracts has been implemented. The statistical methods for diffusion tensors are implemented as ITK code as part of the [[NA-MIC/Projects/Diffusion_Image_Analysis/DTI_Software_and_Algorithm_Infrastructure|DTI Software Infrastructure]] project. The methods have been validated on a repeated scan of a healthy individual. This work has been published as a conference paper (MICCAI 2005) and a journal version (MEDIA 2006). Our recent IPMI 2007 paper includes a nonparametric regression method for analyzing data along a fiber tract.
 
  
 +
By this time the following code available in C++/ITK:
 +
 +
* HARDI estimation based on Spherical Harmonics (to compute Finsler local costs).
 +
* Parallel fast sweeping algorithm (to compute the globally optimal costs).
 +
* Slicer module implementing the computation of the costs map and arrival directions from input DWI data [http://www.nitrc.org/snapshots.php?group_id=464].
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* A first version of the backtracing algorithm is ready [http://www.nitrc.org/snapshots.php?group_id=464].
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To do:
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* Add testing.
  
 
</div>
 
</div>
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==Delivery Mechanism==
 
==Delivery Mechanism==
  
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This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)
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This work will be delivered to the NA-MIC Kit as a Slicer Module
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#ITK Module
 
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#Slicer Module
 
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##Built-in
 
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##Extension -- commandline
 
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##Extension -- loadable
 
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#Other (Please specify)
 
  
 
==References==
 
==References==
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*Fletcher P, Tao R, Jeong W, Whitaker R. [http://www.na-mic.org/publications/item/view/634 A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI.] Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
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* Melonakos, J.;  Pichon, E.;  Angenent, S.;  Tannenbaum, A.; "Finsler active contours." IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3): 412-423, 2008.
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* Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. [http://www.na-mic.org/publications/item/view/292 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
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* Melonakos, J.; Mohan, V.; Niethammer, M.; Smith, K.; Kubicki M.; Tannenbaum, A.; "Finsler tractography for white matter connectivity analysis of the cingulum bundle", Procs. MICCAI 2007, LNCS 4791, pp. 36-43.
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* Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. [http://www.na-mic.org/publications/item/view/1122 Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.] Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
 
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* Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .
 
  
 
</div>
 
</div>

Latest revision as of 13:12, 24 June 2011

Home < Summer project week 2011 Finsler Streamlines

  • Step 1 - You can start by loading a DWI image (should be HARDI) and a mask (not necessary, but very convenient to accelerate computations).

  • Step 2 - Use the segmentation editor (or load any other label map at your convenience). This method requires target regions as well as seeding regions.

  • Step 3 - With the Finsler tractography module, generate a cost map ("Output scalar volume" in the picture) and a map of arrival directions ("Output directions volume").

  • Step 4 - This is how the costs map looks like. Now, you can switch to the Finsler Backtracing module using as input the costs and directions generated in Step 3. Choose a target region different from the seeding region.

  • Step 5 - The fiber bundles are drawn in Slicer's 3-D view.

  • Some other examples. Fibers are always saved to disk in RAS, and colored according to the value of the Finsler cost at the points they go through.

Key Investigators

  • LMI: Antonio Tristán-Vega
  • LMI: Demian Wasserman
  • LMI: Carl-Fredrik Westin

Objective

In the last project week we delivered an implementation of the Finsler method to compute the connectivity among regions in the white matter through High Angular Resolution Diffusion Imaging. Such method provides a costs map from a given seeding point/region to any other point within the brain. The aim in this project is tracing the minimum cost paths between two given regions in the white matter, which will in turn provide the desired streamlines.



Approach, Plan

The method is described in detail in the references below. To compute the costs map we use the Fast Sweeping algorithm: upon convergence, this method provides the minimum cost at each image voxel together with the direction such cost was reached from. Thus, the "backtracing" of these directions from a given point to the seeding point/region provides the minimum cost path.

Progress

By this time the following code available in C++/ITK:

  • HARDI estimation based on Spherical Harmonics (to compute Finsler local costs).
  • Parallel fast sweeping algorithm (to compute the globally optimal costs).
  • Slicer module implementing the computation of the costs map and arrival directions from input DWI data [1].
  • A first version of the backtracing algorithm is ready [2].

To do:

  • Add testing.

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a Slicer Module

References

  • Melonakos, J.; Pichon, E.; Angenent, S.; Tannenbaum, A.; "Finsler active contours." IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3): 412-423, 2008.
  • Melonakos, J.; Mohan, V.; Niethammer, M.; Smith, K.; Kubicki M.; Tannenbaum, A.; "Finsler tractography for white matter connectivity analysis of the cingulum bundle", Procs. MICCAI 2007, LNCS 4791, pp. 36-43.
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