Difference between revisions of "2011 Winter Project Week:LandmarkRegularization"

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Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
 
Image:genuFA.jpg|Regression of FA data; solid line represents the mean and dotted lines the standard deviation.
 
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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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#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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*MGH:Nadya Shusharina, Gregory Sharp
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* Utah: Tom Fletcher, Ross Whitaker
 
  
 
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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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We are introducing a new approach for landmark-based
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deformable image registration.  Our method uses
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Gaussian radial basis functions (RBFs)
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to define a vector field from point landmarks,
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and applies regularization based on the vector field second order derivative. 
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  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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<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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Our approach is based on the fact that the Gaussian RBF has infinite support, but the influence of each
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RBF is localized, making this method well suited for local corrections.
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Our plan for the project week is to first try out <bar>,...
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In addition, Gaussian RBFs have a distinct advantage over competing
 +
approaches such as Wendland functions, because the
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regularized vector field can be solved exactly with a simple equation.
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Our plan for the project week is to implement our registration algorithm as a Slicer plugin.
  
 
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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.
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Algorithm for the landmark-based registration has has been implemented as a part of out in-house software Plastimatch. The method have been validated on a large landmark sets available for download on DIR-LAB web site. This work has been submitted for publication to IEEE Transactions on Medical Imaging.  
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==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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*http://DIR-LAB.com
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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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*Plastomatch. http://plastimatch.org
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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 .
 
  
 
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Revision as of 19:52, 13 December 2010

Home < 2011 Winter Project Week:LandmarkRegularization

  • 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.

Key Investigators

  • MGH:Nadya Shusharina, Gregory Sharp

Objective

We are introducing a new approach for landmark-based deformable image registration. Our method uses Gaussian radial basis functions (RBFs) to define a vector field from point landmarks, and applies regularization based on the vector field second order derivative. 

 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.




Approach, Plan

Our approach is based on the fact that the Gaussian RBF has infinite support, but the influence of each RBF is localized, making this method well suited for local corrections. In addition, Gaussian RBFs have a distinct advantage over competing approaches such as Wendland functions, because the regularized vector field can be solved exactly with a simple equation. Our plan for the project week is to implement our registration algorithm as a Slicer plugin.

Progress

Algorithm for the landmark-based registration has has been implemented as a part of out in-house software Plastimatch. The method have been validated on a large landmark sets available for download on DIR-LAB web site. This work has been submitted for publication to IEEE Transactions on Medical Imaging.

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a (please select the appropriate options by noting YES against them below)

  1. ITK Module
  2. Slicer Module
    1. Built-in
    2. Extension -- commandline
    3. Extension -- loadable
  3. Other (Please specify)

References

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