Difference between revisions of "2012 Summer Project Week:VertebraCTUSReg"

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==Key Investigators==
 
==Key Investigators==
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* UNC: Isabelle Corouge, Casey Goodlett, Guido Gerig
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* University of British Columbia, Robotics & Control Laboratory
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* Utah: Tom Fletcher, Ross Whitaker
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* Queen's University
  
 
<div style="margin: 20px;">
 
<div style="margin: 20px;">
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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.
+
We are developing a new module as a puzzle block for a spine injection image-guided intervention.<br />
 +
The ultimate goal is to plan for injection based on prior CT-image and perform the treatment using ultrasound-based needle guidance.
 +
A volumetric representation of the patient's lumbar section is being reconstructed using tracked frames.
 +
This volume should go through a registration algorithm to rigidly align with the model generated from CT image.
  
  
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
−
 
+
A bone probability volume is generated from the original ultrasound volume.  
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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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From the CT image, a subset of visible points is extracted.
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A guassian mixture model method is performed to solve for this surface to volume registration problem.
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Our plan for the project week is to first try out <bar>,...
 
  
 
</div>
 
</div>
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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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All implementations are based on a single vertebra registration for now.
 +
A loadable module is created which accepts a CT model (polydata) and an ultrasound volume (scalar) as inputs.
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All inputs need to be limited to region of interest (i.e. a single vertebra: L3).
 +
Core implementation of the algorithm is MATLAB-based, since the algorithm is quite fast and speed is not an issue.
 +
A gaussian mixture model is used to register surface to volume. output of the module is the rigid transformation matrix obtained in this way.
 +
A spine phantom data is used for validation. Patient recruitment is an ongoing task for this project.
  
  

Revision as of 03:22, 8 June 2012

Home < 2012 Summer Project Week:VertebraCTUSReg

  • 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

  • University of British Columbia, Robotics & Control Laboratory
  • Queen's University

Objective

We are developing a new module as a puzzle block for a spine injection image-guided intervention.
The ultimate goal is to plan for injection based on prior CT-image and perform the treatment using ultrasound-based needle guidance. A volumetric representation of the patient's lumbar section is being reconstructed using tracked frames. This volume should go through a registration algorithm to rigidly align with the model generated from CT image.




Approach, Plan

A bone probability volume is generated from the original ultrasound volume. From the CT image, a subset of visible points is extracted. A guassian mixture model method is performed to solve for this surface to volume registration problem.

Progress

All implementations are based on a single vertebra registration for now. A loadable module is created which accepts a CT model (polydata) and an ultrasound volume (scalar) as inputs. All inputs need to be limited to region of interest (i.e. a single vertebra: L3). Core implementation of the algorithm is MATLAB-based, since the algorithm is quite fast and speed is not an issue. A gaussian mixture model is used to register surface to volume. output of the module is the rigid transformation matrix obtained in this way. A spine phantom data is used for validation. Patient recruitment is an ongoing task for this project.


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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