Difference between revisions of "2011 Winter Project Week:Efficient co-registration of multiple MR modalities using the ABC"

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* Understand the requirements of neurosurgeons and clinicians
 
* Understand the requirements of neurosurgeons and clinicians
* Try the current Slicer registration modules. We found that the following.
+
* Try the current Slicer registration modules. We found the following problems.
 
** Co-registration can be done by writing script
 
** Co-registration can be done by writing script
 
** The User interface of current modules are not appropriate for multi-modal longitudinal data.  
 
** The User interface of current modules are not appropriate for multi-modal longitudinal data.  

Revision as of 10:55, 14 January 2011

Home < 2011 Winter Project Week:Efficient co-registration of multiple MR modalities using the ABC

Key Investigators

  • Utah: Bo Wang, Marcel Prastawa, Guido Gerig
  • UCLA: Jack Van Horn, Andrei Irimia, Micah Chambers


Objective

Up to now, the UCLA clinicians haven't done any studies on multiple MR modalities. We want to explore the use of ABC for coregistering different modalities for multiple modalities analysis, joint visualization of multiple co-registered modalities.


Approach, Plan

ABC (Atlas-Based Classification) is a fully automatic segmentation method developed in Utah. It can process arbitrary number of channels/modalities by co-registration, it integrates brain stripping, bias correction and segmentation into one optimization framework. A brain atlas is used as spatial priors for tissue categories. We want to use ABC to do efficient co-registration of multiple MR modalities, joint visualization of multiple co-registered modalities.

Progress


We did the following works during the project week.

  • Understand the requirements of neurosurgeons and clinicians
  • Try the current Slicer registration modules. We found the following problems.
    • Co-registration can be done by writing script
    • The User interface of current modules are not appropriate for multi-modal longitudinal data.