2012 Summer Project Week:VertebraCTUSReg

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  Projects List

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  Scatter plot of the original FA data through the genu of the corpus callosum of a normal brain.

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  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

• Fletcher P, Tao R, Jeong W, Whitaker R. A volumetric approach to quantifying region-to-region white matter connectivity in diffusion tensor MRI. Inf Process Med Imaging. 2007;20:346-358. PMID: 17633712.
• Corouge I, Fletcher P, Joshi S, Gouttard S, Gerig G. Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Med Image Anal. 2006 Oct;10(5):786-98. PMID: 16926104.
• Corouge I, Fletcher P, Joshi S, Gilmore J, Gerig G. Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):131-9. PMID: 16685838.
• Goodlett C, Corouge I, Jomier M, Gerig G, A Quantitative DTI Fiber Tract Analysis Suite, The Insight Journal, vol. ISC/NAMIC/ MICCAI Workshop on Open-Source Software, 2005, Online publication: http://hdl.handle.net/1926/39 .