Difference between revisions of "Algorithm:MGH:TenorBasedGroupComparison"

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'''Status''' The tensor based group comparison method was released into the FreeDiffusion environment at MGH. Paper submitted: "Statistical Group Comparison of Diffusion Tensors via Multivariate Hypothesis Testing." The implementation is in Matlab. Results are able to be visualized in Slicer.
 
'''Status''' The tensor based group comparison method was released into the FreeDiffusion environment at MGH. Paper submitted: "Statistical Group Comparison of Diffusion Tensors via Multivariate Hypothesis Testing." The implementation is in Matlab. Results are able to be visualized in Slicer.
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== [[Algorithm:MGH:Development:GroupComp|Tensor-based group comparison (Cramer test)]] ==
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* '''Use case'''<nowiki>: 'Compare DTI images between groups using the full tensor information.' </nowiki>
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* Difficulty: Medium
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* Impact: Medium-High
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# Implement in R (Whitcher/Tuch) : '''done'''
 +
# Power analysis (Whitcher) : '''done'''
 +
# Port to Matlab (Whitcher) : '''done'''
 +
# Validate Matlab version against R (Whitcher) : '''done'''
 +
# Test on group data : '''done'''
 +
# Release bootstrap-only version to test group: '''done'''
 +
# Port FFT method from R to matlab (Whitcher): '''done'''
 +
# Implement FFT method in diffusion development environment (Tuch): '''done'''

Revision as of 02:09, 20 September 2007

Home < Algorithm:MGH:TenorBasedGroupComparison

DTI Analysis: Tensor-based Group Comparison

Objective: To boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests.

Example

The figure shows an example of the potential gain in sensitivity using a multivariate tensor test in comparison to the univariate FA test:

Multivariate vs. Univariate Test Comparison
GrpCmpGrph.jpg

Status The tensor based group comparison method was released into the FreeDiffusion environment at MGH. Paper submitted: "Statistical Group Comparison of Diffusion Tensors via Multivariate Hypothesis Testing." The implementation is in Matlab. Results are able to be visualized in Slicer.

Tensor-based group comparison (Cramer test)

  • Use case: 'Compare DTI images between groups using the full tensor information.'
  • Difficulty: Medium
  • Impact: Medium-High
  1. Implement in R (Whitcher/Tuch) : done
  2. Power analysis (Whitcher) : done
  3. Port to Matlab (Whitcher) : done
  4. Validate Matlab version against R (Whitcher) : done
  5. Test on group data : done
  6. Release bootstrap-only version to test group: done
  7. Port FFT method from R to matlab (Whitcher): done
  8. Implement FFT method in diffusion development environment (Tuch): done