Difference between revisions of "2010 Winter Project Week Tractography using DTI Atlasing"

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Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
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Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Project List]]
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Image:Anuja_scatter1.png|Kernel regression along the fiber tract, also showing the scatter plot for DTI data.
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Image:fid_seed.jpg|
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Image:Anuja_plot1.png.png|Sub plots showing distribution of FA values in various cross-sections along the fiber tract length.
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==Key Investigators==
 
==Key Investigators==
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* Utah: Anuja Sharma, Guido Gerig
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* Utah: Gopalkrishna Veni, Ross Whitaker, Sarang Joshi
  
 
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<h3>Objective</h3>
 
<h3>Objective</h3>
  
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The aim is to understand probabilistic models that can account for the behavior of water diffusion in white matter tracts. The long term goal is to use this to understand the changes in white matter structure with age, gender or a specific disease.  
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To study the effect of drugs on teenagers in the age group of 12-30.
  
  
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<h3>Approach, Plan</h3>
 
<h3>Approach, Plan</h3>
  
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Initially, the DWI data is preprocessed to address Eddy current correction and EPI distortion correction. Next, DTI Atlas building is performed using Casey’s DTI analysis pipeline* to bring all the DTI images into a common co-ordinate system and then fiber tract analysis is performed to compare the differences in specific fiber tracts. Our aim for the project week is to explore various DTI analysis tools developed by NAMIC to perform tractography.  
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Various tract-oriented scalar diffusion measures are treated as a continuous function of fiber arc-length. To analyze the trend along the fiber tract, a command line tool performs kernel regression on this data. The idea is to try out different noise models and maximum likelihood estimates within kernel windows, such that they best represent the data and are robust to noise and Partial Volume effect.
 
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Casey Goodlett's functional data analysis pipeline is then applied to this data. Here, multivariate hypothesis test is used to test for differences between populations.  
 
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Our plan for the project week is to try out the complete pipeline for a few datasets and see if we get statistically significant results. We also plan to work on the possibility of integrating this module into the Slicer environment.
 
  
 
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<h3>Progress</h3>
 
<h3>Progress</h3>
  
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The first version of the command line tool is ready. It provides the flexibility to choose the scalar diffusion measure to be tested; a choice between Gaussian and Beta noise models and Mean, median or mode as MLE. It also incorporates several visualization options which help in analyzing the best noise models and the best representative statistics to explain the distribution of DTI data along the fiber tract.  
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During this week, two tractographic modules using Fiducial seeding and Python stochastic tractography have been explored using our dataset.
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I am still working on the problem related to the registration of T2 weighted image with the DTI baseline image.  
 
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==References==
 
==References==
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*Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142
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*Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
  
 
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Latest revision as of 17:29, 8 January 2010

Home < 2010 Winter Project Week Tractography using DTI Atlasing

Key Investigators

  • Utah: Gopalkrishna Veni, Ross Whitaker, Sarang Joshi

Objective

To study the effect of drugs on teenagers in the age group of 12-30.


Approach, Plan

Initially, the DWI data is preprocessed to address Eddy current correction and EPI distortion correction. Next, DTI Atlas building is performed using Casey’s DTI analysis pipeline* to bring all the DTI images into a common co-ordinate system and then fiber tract analysis is performed to compare the differences in specific fiber tracts. Our aim for the project week is to explore various DTI analysis tools developed by NAMIC to perform tractography.

Progress

During this week, two tractographic modules using Fiducial seeding and Python stochastic tractography have been explored using our dataset. I am still working on the problem related to the registration of T2 weighted image with the DTI baseline image.

References

  • Casey B. Goodlett, P. Thomas Fletcher, John H. Gilmore, Guido Gerig. Group Analysis of DTI Fiber Tract Statistics with Application to Neurodevelopment. NeuroImage 45 (1) Supp. 1, 2009. p. S133-S142.
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