Difference between revisions of "NA-MIC/Projects/Collaboration/SBIA UPenn"

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* [http://www.rad.upenn.edu/sbia Link to SBIA web site: ]
 
* [http://www.rad.upenn.edu/sbia Link to SBIA web site: ]
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* [J. Yang, D. Shen, C. Davatzikos and R. Verma, Diffusion Tensor Image Registration Using Tensor Geometry and Orientation Features, in MICCAI 2008, New York. ]
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* J. Yang, D. Shen, C. Davatzikos and R. Verma, Diffusion Tensor Image Registration Using Tensor Geometry and Orientation Features, in MICCAI 2008, New York.
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* [J. Yang, D. Shen, C. Davatzikos and R. Verma, Spatial Normalization of Diffusion Tensor Images Based on Anisotropic Segmentation, in SPIE Medical Imaging 2008, San Diego. ]
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* J. Yang, D. Shen, C. Davatzikos and R. Verma, Spatial Normalization of Diffusion Tensor Images Based on Anisotropic Segmentation, in SPIE Medical Imaging 2008, San Diego.
 
* [https://www.rad.upenn.edu/sbia/papers/181.pdf  R. Verma, P. Khurd, C. Davatzikos, On Analyzing Diffusion Tensor Images by Identifying Manifold Structure using Isomaps, IEEE Transactions on Medical Imaging, 772-778, Vol. 26, No. 6, 2007 (need access to IEEE explore to view papers) ]
 
* [https://www.rad.upenn.edu/sbia/papers/181.pdf  R. Verma, P. Khurd, C. Davatzikos, On Analyzing Diffusion Tensor Images by Identifying Manifold Structure using Isomaps, IEEE Transactions on Medical Imaging, 772-778, Vol. 26, No. 6, 2007 (need access to IEEE explore to view papers) ]
 
* [https://www.rad.upenn.edu/sbia/papers/177.pdf P. Khurd, R. Verma and C. Davatzikos, Kernel-based Manifold Learning for Statistical Analysis of Diffusion Tensor Images, Information Processing in Medical Imaging (IPMI), 581-593, Vol. 4584, 2007 ]
 
* [https://www.rad.upenn.edu/sbia/papers/177.pdf P. Khurd, R. Verma and C. Davatzikos, Kernel-based Manifold Learning for Statistical Analysis of Diffusion Tensor Images, Information Processing in Medical Imaging (IPMI), 581-593, Vol. 4584, 2007 ]

Revision as of 14:28, 18 June 2008

Home < NA-MIC < Projects < Collaboration < SBIA UPenn
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Key Investigators

  • Ragini Verma, SBIA, UPenn <Ragini.Verma@uphs.upenn.edu>
  • Christos Davatzikos <Christos.Davatzikos@uphs.upenn.edu>
  • Yang Li <Yang.Li@uphs.upenn.edu>
  • Luke Bloy <lbloy@seas.upenn.edu>

Objective

To incorporate into Slicer, processing and analysis methods for DTI and HARDI being developed at the Section of Biomedical Image Analysis (SBIA), UPenn [1]. The main components will be plugins for DTI registration and manifold-based statistics followed by methods for HARDI registration and statistics.

Approach, Plan

  • DTI registration: has been developed in SBIA over a period of 2 years, mainly using in-house code (not in ITK). This will be incorporated as a plugin.
  • DTI Statistics: SPM-like package for statistics on the full tensor. The proposed format is again a plugin as it is based on code already existing in SBIA in C, C++.
  • HARDI registration: This is being developed in ITK, building on HARDI representation format currently under development
  • HARDI statistics: being developed in ITK

Progress

  • DTI Registration: plugin is ready and will be demonstrated

To do: Replace the affine registration component, help file, speed, version control

  • Need help with:

1. General representation for registration in ITK

2. Code for Demons DTI registration for comparison

3. Visualization: tensor glyphs and ODFs


References

This work was supported in part by NIH grants .

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