Difference between revisions of "Project Week/Template"

From NAMIC Wiki
Jump to: navigation, search
Line 32: Line 32:
 
<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
  
−
The method is described in detail in the reference 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.
+
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.
  
 
</div>
 
</div>
Line 39: Line 39:
  
 
<h3>Progress</h3>
 
<h3>Progress</h3>
−
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.
  
 
</div>
 
</div>
Line 52: Line 56:
  
 
==References==
 
==References==
−
*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.
+
* Melonakos, J.;  Pichon, E.;  Angenent, S.;  Tannenbaum, A.; "Finsler active contours." IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3): 412-423, 2008.
−
* 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.
+
* 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.
−
* 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.
 
−
* 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>

Revision as of 21:23, 16 June 2011

Home < Project Week < Template

  • Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.

  • Regression of FA data; solid line represents the mean and dotted lines the standard deviation.

Instructions for Use of this Template

  1. Please create a new wiki page with an appropriate title for your project using the convention Project/<Project Name>
  2. Copy the entire text of this page into the page created above
  3. Link the created page into the list of projects for the project event
  4. Delete this section from the created page
  5. Send an email to tkapur at bwh.harvard.edu if you are stuck

Key Investigators

  • BWH: Antonio Tristán-Vega, Demian Wasserman, 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.

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.
Retrieved from "https://www.na-mic.org/w/index.php?title=Project_Week/Template&oldid=68316"