Difference between revisions of "Algorithm:MGH:New"

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== [[Engineering:Project:Non-rigid_EPI_registration|Engineering:Project:Non-rigid_EPI_registration]] ==
 
== [[Engineering:Project:Non-rigid_EPI_registration|Engineering:Project:Non-rigid_EPI_registration]] ==
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Statistical power benefit of ITK nonlinear registration
 
  
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* '''Use case'''<nowiki>: 'Evaluate benefit of using ITK nonlinear registration for group FA comparisons' </nowiki>
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My objective is to evaluate the benefit of using ITK nonlinear registration for group FA comparisons. [[Engineering:Project:Non-rigid_EPI_registration|More...]]
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* Difficulty: Low-Medium
 
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* Impact: Medium
 
  
 
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== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==
 
== [[Algorithm:MGH:NRDDFreesurfer|Adding NRRD I/O to Freesurfer]] ==
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* '''Use case'''<nowiki>: 'Open a NRRD volume in FreeSurfer.' </nowiki>
 
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* '''Use case'''<nowiki>: 'Convert an MGH volume to a NRRD volume with Freesurfer.' </nowiki>
 
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* Difficulty: Low
 
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* Impact: Medium
 
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# Write unit tests for new IO functions (Snyder): '''in progress'''
 
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# Add NrrdIO libraries from Teem to FS source tree, build with autoconf (Snyder): '''done'''
 
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# Write and test FS NRRD IO functions (Snyder, Kindlmann): '''in progress'''
 
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# Develop approriate headers for MGH DWI data (Teich): '''queued'''
 
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# Automate header generation when possible (Teich): '''queued'''
 
  
 
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== [[Algorithm:MGH:QBALLVisualization|QBall Visualization]] ==
 
== [[Algorithm:MGH:QBALLVisualization|QBall Visualization]] ==
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* '''Use case'''<nowiki>: 'Visualize q-ball data in Slicer.' </nowiki>
 
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* Difficulty: Low
 
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* Impact: Medium
 
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# Implement ODF polygon decimation algorithm (Tuch) : '''done'''
 
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# Port decimation fileformat into FreeDiffusion Visualizer (Snyder) : '''done'''
 
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# Port QBALL/ODF visualization into [[Slicer|Slicer]] (Estepar/Snyder/Kindlmann/Tuch/Westin): '''done'''
 
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## Implement (Estepar): '''done'''
 
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## Test on mock data set (Estepar): '''done'''
 
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## Demo for real data set (Estepar/Snyder/Kindlmann): '''done'''
 
  
 
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== [[Algorithm:MGH:Development:GroupComp|Tensor-based group comparison (Cramer test)]] ==
 
== [[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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Our objective is to boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. [[Algorithm:MGH:Development:GroupComp|More...]]
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* Difficulty: Medium
 
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* Impact: Medium-High
 
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# Implement in R (Whitcher/Tuch) : '''done'''
 
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# Power analysis (Whitcher) : '''done'''
 
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# Port to Matlab (Whitcher) : '''done'''
 
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# Validate Matlab version against R (Whitcher) : '''done'''
 
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# Test on group data : '''done'''
 
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# Release bootstrap-only version to test group: '''done'''
 
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# Port FFT method from R to matlab (Whitcher): '''done'''
 
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# Implement FFT method in diffusion development environment (Tuch): '''done'''
 
  
 
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== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==
 
== [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|Numerical Recipies Replacement]] ==
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Replacing Numerical Recipes in FreeSurfer (for open sourcing)
 
  
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* '''Use case'''<nowiki>: 'Unit tests pass with all replacements.' </nowiki>
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Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. [[Algorithm:MGH:FreeSurferNumericalRecipiesReplacement|More...]]
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* Difficulty: Medium-High
 
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* Impact: High
 
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# Write test cases for each algorithm (Snyder, Jen): '''done'''
 
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# Identify replacements (Snyder, Jen): '''done'''
 
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# Integrate required libraries into FreeSurfer build process (Snyder, Jen): '''done'''
 
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# Iteratively replace recipes with substitutes and run tests (Snyder, Jen): '''done'''
 
  
 
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== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==
 
== [[Algorithm:MGH:Development:AutoBrainSeg|Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms]] ==
  
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* '''Use Case'''<nowiki>: 'Atlas-based fully automated whole brain segmentation' </nowiki>
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Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. [[Algorithm:MGH:Development:AutoBrainSeg|More...]]
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* Difficulty: Medium-High
 
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* Impact: Medium-High
 
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# Implemented in C and distribute with the FreeSurface Package: '''done'''
 

Revision as of 02:24, 20 September 2007

Home < Algorithm:MGH:New

Back to NA-MIC Algorithms

Overview of MGH Algorithms

A brief overview of the MGH's algorithms goes here. This should not be much longer than a paragraph. Remember that people visiting your site want to be able to understand very quickly what you're all about and then they want to jump into your site's projects. The projects below are organized into a two column table: the left column is for representative images and the right column is for project overviews. The number of rows corresponds to the number of projects at your site. Put the most interesting and relevant projects at the top of the table. You do not need to organize the table according to subject matter (i.e. do not group all segmentation projects together and all DWI projects together).

MGH Projects

File:Placeholder.png

QDEC: An easy to use GUI for group morphometry studies

  • Use case: 'Compare the primary eigendirection in two groups to see if they are the same'
  • Difficulty: Low
  • Impact: Medium

See: Qdec user page

File:Placeholder.png

Optimal path calculator (Poistats)

  • Use case: 'Specify 2 points in a diffusion image and tell how connected they are.'
  • Difficulty: High
  • Impact: High
File:Placeholder.png

Engineering:Project:Non-rigid_EPI_registration

My objective is to evaluate the benefit of using ITK nonlinear registration for group FA comparisons. More...

File:Placeholder.png

Adding NRRD I/O to Freesurfer

File:Placeholder.png

Spherical Wavelets

Cortical Surface Shape Analysis Based on Spherical Wavelets

File:Placeholder.png

Topology Correction

Geometrically-Accurate Topology-Correction of Cortical Surfaces using Non-Separating Loops


File:Placeholder.png

QBall Visualization

File:Placeholder.png

Tensor-based group comparison (Cramer test)

Our objective is to boost statistical sensitivity for group comparisons in comparison to 'traditional' univariate tests. More...

File:Placeholder.png

Numerical Recipies Replacement

Our obejective is to replace algorithms using proprietary numerical recipes in FreeSurfer in efforts to open source FreeSurfer. More...

File:Placeholder.png

Atlas Renormalization for Improved Brain MR Image Segmentation across Scanner Platforms

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies. More...

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