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

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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
A bone probability volume is generated from the original ultrasound volume.  
+
We assume a single vertebra model and volume is provided using existing slicer modules like crop-volume and segmentation (editor).
From the CT image, a subset of visible points is extracted.
+
From the CT model, a subset of visible points is extracted regarding position of the probe in ultrasound data acquisition step.
A guassian mixture model method is performed to solve for this surface to volume registration problem.
+
3D ultrasound volume (reconstructed before) is processed to generate a bone probability volume. <br/>
 
+
An iterative optimization algorithm is performed using Guassian Mixture Model method to solve for this surface to volume registration problem.
 +
Results of each iteration step is visualaized in slicer to show the convergence of the algorithm.<br/>
 +
Output from each step of the algorithm and all transforms from each iteration are saved to disk.
 
</div>
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
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)
A loadable module is created which accepts a CT model (polydata) and an ultrasound volume (scalar) as inputs.  
+
# Core implementation of the algorithm is MATLAB-based, since the algorithm is quite fast and speed is not an issue. Re-distributable MATLAB Compiler Runtime (MCR) is a prerequisite for the slicer module.
All inputs need to be limited to region of interest (i.e. a single vertebra: L3).
+
# A spine phantom data (L1-5) is used for the experiment.
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.
 
 
 
 
 
 
</div>
 
</div>
 
</div>
 
</div>

Revision as of 18:37, 9 June 2012

Home < 2012 Summer Project Week:VertebraCTUSReg

Key Investigators

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

Objective

To bring the realtime needle navigation for spine injections into 3D-slicer framework, a chain of tools are required. Ultrasound machine is one of the most applicable devices for realtime guidance due to its non-poisonous physics and low cost availability. Out of the patient's lumbar section of the spine, a 3D volume is reconstructed using tracked frames acquired from ultrasound machine. This volume needs to be registered to prior CT-image of the same area of the patient. At the end a realtime guidance is performed based on outputs generated.

In this phase, We are focused on developing a new module for slicer that performs a rigid registration between segmented CT (model) and ultrasound volumetric representation (reconstructed volume). Current algorithm is based on single vertebra inputs and will be extended to include mechanical characteristics of spine for a multi-vertebrae case.

Approach, Plan

We assume a single vertebra model and volume is provided using existing slicer modules like crop-volume and segmentation (editor). From the CT model, a subset of visible points is extracted regarding position of the probe in ultrasound data acquisition step. 3D ultrasound volume (reconstructed before) is processed to generate a bone probability volume.
An iterative optimization algorithm is performed using Guassian Mixture Model method to solve for this surface to volume registration problem. Results of each iteration step is visualaized in slicer to show the convergence of the algorithm.
Output from each step of the algorithm and all transforms from each iteration are saved to disk.

Progress

  1. 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)
  2. Core implementation of the algorithm is MATLAB-based, since the algorithm is quite fast and speed is not an issue. Re-distributable MATLAB Compiler Runtime (MCR) is a prerequisite for the slicer module.
  3. A spine phantom data (L1-5) is used for the experiment.

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 --> Yes
  3. Other (Please specify)