2011 Winter Project Week:NAMICShapeAnalysis

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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

• UNC: Lucile Bompard, Martin Styner
• Utah: Chris Gloschat

Objective

Statistical shape analysis methods have emerged within the last decade to allow for a localized analysis of shape.The UNC shapeAnalysisMANCOVA pipeline is a unified method for local shape analysis that can accomodate different number of variates and contrasts. Unlike current shape analysis frameworks,it also allows to include any number of associated variables in the statistical analysis of the data. This tool has been designed to interact seamlessly with the existing UNC SPHARM-PDM based shape analysis toolbox. Indeed, the point-based models computed with the SPHARM-PDM tool can be used in combination with this pipeline to perform quantitative morphological assessment of structural changes at specific locations.

Approach, Plan

The developed shapeAnalysisMANCOVA is a C++ program that can be run both with shapeAnalysisMANCOVA_Wizard as well as a command line tool. In order to compile shapeAnalysisMANCOVA, ITK, VTK, GenerateCLP and Boost Libraries for C (v 0.39.1) must already be installed. The point-based models will be analyzed with methods using multivariate analysis of covariance (MANCOVA). The steps performed in order to obtain the statistical analysis are the following, for each iteration: computing a General Linear Model (GLM) to test group differences at every surface location, doing the Metric computation thanks to the Multivariate analysis of covariance (MANCOVA) computing the P-values and controlling for the mutliple testing problem doing the permutations tests (considerong the linear varibles) The data scenes (MRML scene) are created and can be displayed within 3D Slicer. <foo>.

Our plan for the project week is to first try out <bar>,...

Progress

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 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.

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 .