Difference between revisions of "2013 Project Week:FastFiducialRegistrationModule"

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==Key Investigators==
 
==Key Investigators==
  
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* LiangJia Zhu, Allen Tannenbaum, UAB
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* Dave Welch, Hans Johnson, UIowa SENAP
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* Yi Gao, BWH
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* Ron Kikinis, Nicole Aucoin, MIT
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* Josh Cates, Rob MacLeod, SCI
 
  
 
==Project Description==
 
==Project Description==
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<h3>Objective</h3>
 
<h3>Objective</h3>
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* We are developing methods for identifying scar tissue from CARMA data. Our previous method demonstrates an effective identification ability for DE-MRI data. In this method, the intensity distribution inside the LA myocardial wall is modeled as a mixture of Gaussians. To improve the performance of this method, we will integrate the intensity information from the LA chamber into the overall identification procedure.  
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* We are developing a fast rigid registration module using an initial fiducial registration for the AMIGO surgical suite. This project is dependent on the new MRML Annotations scheme developed by Nicole. We will implement this in a Python-scripted module, if possible.
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* We will discuss possible improvements for scar identification.  
 
 
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
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* Design an identification scheme using the LA intensity as a prior
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* Identify new/modified requirements for this project with Ron
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* Test the method using CARMA data
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* Implement annotation picking within the module with testing
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* Deliver the implementation in CLI module.
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* Perform fiducial registration and save intermediate result for debugging
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* Perform rigid registration and save final result
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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*  
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* This is a continuation of [[2012 Summer Project Week:Fast Fiducial Registration]]
 
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Revision as of 16:16, 7 January 2013

Home < 2013 Project Week:FastFiducialRegistrationModule

Key Investigators

  • Dave Welch, Hans Johnson, UIowa SENAP
  • Ron Kikinis, Nicole Aucoin, MIT

Project Description

Objective

  • We are developing a fast rigid registration module using an initial fiducial registration for the AMIGO surgical suite. This project is dependent on the new MRML Annotations scheme developed by Nicole. We will implement this in a Python-scripted module, if possible.

Approach, Plan

  • Identify new/modified requirements for this project with Ron
  • Implement annotation picking within the module with testing
  • Perform fiducial registration and save intermediate result for debugging
  • Perform rigid registration and save final result

Progress

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