Difference between revisions of "NA"

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'''Objective:''' 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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'''Objective:'''
  
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'''Progress:''' 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 has been submitted.
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We want to develop new elastic registration methods for brain imaging. The idea is to use optimal transport as the similarity metric underlying the registration procedure. In order to use this technique on 3D manifolds, they must first be warped to the 2D plane using conformal mapping techniques. Additionally, we would like to allow users to be able to specify anatomical landmarks in the form of artificial slits placed in the segmented brain surfaces. During the registration these slits would be forced to register to one another thus guiding the process and producing more accurate results.
  
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''References:''
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'''Progress:'''
  
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* Corouge, I., Fletcher, T., Joshi, S., Gilmore J.H., and Gerig, G., Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis, Lecture Notes in Computer Science LNCS, James S. Duncan and Guido Gerig, editors, Springer Verlag, Vol. 3749, Oct. 2005, pp. 131 -- 138
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* This techniques has been implemented for standard 2D images and simple surfaces in [1] and [2] along with application to medical images.
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* C. Goodlett, I. Corouge, M. Jomier, and G. Gerig, 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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* Code has been developed to accomplish the warping of the brain from a complex 3D surface containing several holes onto the plane.
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* Optimal transport has been used to register heart [3] and vessel imagery [4]. These applications required surfaces to be more complex in that they contained holes and other topological challenges such as branches. As a result, they needed more complex flattening procedures.
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'''Ongoing:'''
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* Further development of algorithms to enable highly complex surfaces with embedded landmark information to be registered using optimal transport.
  
 
'''Key Investigators:'''
 
'''Key Investigators:'''
  
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* Steve Haker - Harvard
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* Utah: Tom Fletcher, Ross Whitaker
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* Shawn Lankton - Georgia Tech
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* Lei Zhu - Georgia Tech
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* Allen Tannenbaum - Georgia Tech
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* Ron Kikinis - Harvard
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'''References:'''
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* [1] Haker S, Tannenbaum A, Kikinis R. Mass Preserving Mappings and Image Registration. Proc MICCAI 2001, LCNS 2208; p 120-127
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* [2] Haker S, Zhu L, Tannenbaum A, Angenent S. Optimal Mass Transport for Registration and Warping. IJCV, 60(3),225-240,2004; p 225-240
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* [3] Zhu L, Haker S, Tannenbaum A. Mass Preserving Registration for Heart MR Images. Proc MICCAI 2005, LCNS 3750; p 147-154
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* [4] Zhu L, Haker S, Tannenbaum A. Area-Preserving Mappings for the Visualization of Medical Structures. Proc MICCAI 2003, LCNS 2879; p 277-284
  
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'''Links:'''
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<br />'''Links:'''
  
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* [[Progress_Report:Diffusion_Tensor_Statistics|Diffusion Tensor Statistics Progress Report]]
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* [[Algorithm:GATech|GATech Algorithms]]
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* [http://www.bme.gatech.edu/groups/bil/ GATech Group Website]

Revision as of 14:03, 18 December 2006

Home < NA

Objective:

We want to develop new elastic registration methods for brain imaging. The idea is to use optimal transport as the similarity metric underlying the registration procedure. In order to use this technique on 3D manifolds, they must first be warped to the 2D plane using conformal mapping techniques. Additionally, we would like to allow users to be able to specify anatomical landmarks in the form of artificial slits placed in the segmented brain surfaces. During the registration these slits would be forced to register to one another thus guiding the process and producing more accurate results.

Progress:

  • This techniques has been implemented for standard 2D images and simple surfaces in [1] and [2] along with application to medical images.
  • Code has been developed to accomplish the warping of the brain from a complex 3D surface containing several holes onto the plane.
  • Optimal transport has been used to register heart [3] and vessel imagery [4]. These applications required surfaces to be more complex in that they contained holes and other topological challenges such as branches. As a result, they needed more complex flattening procedures.

Ongoing:

  • Further development of algorithms to enable highly complex surfaces with embedded landmark information to be registered using optimal transport.

Key Investigators:

  • Steve Haker - Harvard
  • Shawn Lankton - Georgia Tech
  • Lei Zhu - Georgia Tech
  • Allen Tannenbaum - Georgia Tech
  • Ron Kikinis - Harvard

References:

  • [1] Haker S, Tannenbaum A, Kikinis R. Mass Preserving Mappings and Image Registration. Proc MICCAI 2001, LCNS 2208; p 120-127
  • [2] Haker S, Zhu L, Tannenbaum A, Angenent S. Optimal Mass Transport for Registration and Warping. IJCV, 60(3),225-240,2004; p 225-240
  • [3] Zhu L, Haker S, Tannenbaum A. Mass Preserving Registration for Heart MR Images. Proc MICCAI 2005, LCNS 3750; p 147-154
  • [4] Zhu L, Haker S, Tannenbaum A. Area-Preserving Mappings for the Visualization of Medical Structures. Proc MICCAI 2003, LCNS 2879; p 277-284


Links:

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