Difference between revisions of "Algorithm:MGH:TenorBasedGroupComparison"
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== DTI Analysis: Tensor-based Group Comparison == | == DTI Analysis: Tensor-based Group Comparison == | ||
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''Objective:'' To boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. | ''Objective:'' To boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. | ||
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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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* '''Use case'''<nowiki>: 'Compare DTI images between groups using the full tensor information.' </nowiki> | * '''Use case'''<nowiki>: 'Compare DTI images between groups using the full tensor information.' </nowiki> | ||
Revision as of 20:32, 20 September 2007
Home < Algorithm:MGH:TenorBasedGroupComparisonDTI Analysis: Tensor-based Group Comparison
Back to NA-MIC_Collaborations, MGH Algorithms
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:
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.
- Use case: 'Compare DTI images between groups using the full tensor information.'
- Difficulty: Medium
- Impact: Medium-High
- 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
