Difference between revisions of "2017 Winter Project Week/LORDWI"

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Image:PW-Winter2017.png|link=2017_Winter_Project_Week#Projects|[[2017_Winter_Project_Week#Projects|Projects List]]
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==Key Investigators==
 
==Key Investigators==
 
* Henrik Groenholt Jensen, UCPH
 
* Henrik Groenholt Jensen, UCPH
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* Evaluate/validate the density-based registration framework for DWI developed at UCPH (this is a nonrigid model - see the link below for a paper on the global algorithm).
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* '''Objective''': Evaluate/validate a density-based non-rigid registration framework for DWI (see the link below for a paper on similarity measure used). <br>''In short: Is this a good registration?''
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* '''Short description''': The model is based on Free-Form Deformation B-splines where the diffusion gradient directions are updated using the normalized Jacobian. B-spline interpolation is used spatially, the Watson Distribution is used (gradient) directionally. Histogram is smoothed. Similarity is NMI and optimisation is L-BFGS.
 
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* Figure out what diffusion data is available (preferably data already evaluated by another registration model).
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* Discuss best ways to validate results (tractography, biomarkers, synthetic data, phantoms, others?). So far we have visually tested inter-subject registrations of HCP data,  intra-subject multi-shell, and intra-subject on child brain tumor subjects.
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* Discuss best ways to validate results (tractography, biomarkers, synthetic data, phantoms, others?). So far we have visually tested inter-subject registrations of HCP data and intra-subject multi-shell, and intra-subject on young brain tumor subjects.
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* Figure out anyone is in need of DWI registration for testing, have a dataset already registered (for comparison), or can help with anatomical landmarks.
 
* Consider if Slicer can be used in tandem for evaluation.  
 
* Consider if Slicer can be used in tandem for evaluation.  
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* This method introduces Mutual Information to nonrigid registration of DWI. Discuss if other similarity measures (e.g. correlation measures) would be better suited for specific problems.
 
 
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<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
 
<!-- Progress and Next steps bullet points (fill out at the end of project week) -->
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The goal was too lofty but the week was excellent.
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* Had a lot of great discussions and good insight into what others are doing.
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* Got working on acquiring data from different sources (more problems are always welcome!)
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* Shared some experiences.
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* Got a nice introduction to Slicer.
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Latest revision as of 15:45, 13 January 2017

Home < 2017 Winter Project Week < LORDWI

Key Investigators

  • Henrik Groenholt Jensen, UCPH
  • Lauren J. O'Donnell, BWH
  • Tina Kapur, BWH
  • Fan Zhang, BWH
  • Carl-Fredrik Westin, BWH

Project Description

Objective Approach and Plan Progress and Next Steps
  • Objective: Evaluate/validate a density-based non-rigid registration framework for DWI (see the link below for a paper on similarity measure used).
    In short: Is this a good registration?
  • Short description: The model is based on Free-Form Deformation B-splines where the diffusion gradient directions are updated using the normalized Jacobian. B-spline interpolation is used spatially, the Watson Distribution is used (gradient) directionally. Histogram is smoothed. Similarity is NMI and optimisation is L-BFGS.
  • Discuss best ways to validate results (tractography, biomarkers, synthetic data, phantoms, others?). So far we have visually tested inter-subject registrations of HCP data, intra-subject multi-shell, and intra-subject on child brain tumor subjects.
  • Figure out anyone is in need of DWI registration for testing, have a dataset already registered (for comparison), or can help with anatomical landmarks.
  • Consider if Slicer can be used in tandem for evaluation.

The goal was too lofty but the week was excellent.

  • Had a lot of great discussions and good insight into what others are doing.
  • Got working on acquiring data from different sources (more problems are always welcome!)
  • Shared some experiences.
  • Got a nice introduction to Slicer.

Background and References

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