Difference between revisions of "Link Progress"

From NAMIC Wiki
Jump to: navigation, search
Line 9: Line 9:
 
; A - Development of tractography workflow :
 
; A - Development of tractography workflow :
 
:*  DWI image preprocessing  
 
:*  DWI image preprocessing  
  :* denoising
+
:** denoising
  :* Eddy currents corrections
+
:** Eddy currents corrections
 
:*  WM mask & ROIs creation
 
:*  WM mask & ROIs creation
 
:*  tensor estimation & visualization (this step is somewhat hidden)
 
:*  tensor estimation & visualization (this step is somewhat hidden)
Line 30: Line 30:
 
:* test Slicer GUI access with python (done)
 
:* test Slicer GUI access with python (done)
 
:* implement tractography algorythm in python (based on Ola Friman Matlab implementation) (done)
 
:* implement tractography algorythm in python (based on Ola Friman Matlab implementation) (done)
:* test based on sample dataset show identical computational results between Matlab and python implementation (done)
+
:** test based on sample dataset show identical computational results between Matlab and python implementation (done)
  
 
; B - Visualization of diffusion properties :
 
; B - Visualization of diffusion properties :

Revision as of 15:48, 3 November 2008

Home < Link Progress

Back to NA-MIC Collaborations, Harvard DBP 2, DBP2:Harvard:Brain_Segmentation_Roadmap


Stochastic Tractography Progress

Status

Stochastic tractography requires careful maintenance and on-going development improving its usability and performance. 
A - Development of tractography workflow 
  • DWI image preprocessing
    • denoising
    • Eddy currents corrections
  • WM mask & ROIs creation
  • tensor estimation & visualization (this step is somewhat hidden)
  • tracts reconstruction & visualization (should be useful to see tracts and not only the connectivity map)
  • connectivity maps creation & visualization
B - Visualization of diffusion properties - test utilities 
  • reveal more features related to diffusion - develop visualization
  • finalize batch - testing different parameters of the stochastic tractography with different datasets - wrap up of the results
C – Study of limit cases (tract reconstruction issue) 
  • sensitivity to inputs (WM/ROIs) if exist - needing criteria definition and algorythmic extensions
  • further algorythmic extensions if needed for dealing with specific reconstruction cases (tracts with multiple branches)


Updates/Progress

A - Development of tractography workflow 
  • initial skeleton incorporating already existing stochastic tractography components (in progress)
  • test Slicer GUI access with python (done)
  • implement tractography algorythm in python (based on Ola Friman Matlab implementation) (done)
    • test based on sample dataset show identical computational results between Matlab and python implementation (done)
B - Visualization of diffusion properties 
  • create visual map showing regions defined by WM mask where FA (fractional anisotropy is upper the defined threshold e.g. 0.3) and where the mask forbids (defining as a non white matter region) the stochastic algorithm to go in
C – Study of limit cases (tract reconstruction issue) 
  • relate C to B


Schedule

  • 08/2008 - Introduction to tractography, Slicer software, articles related to tractography.
  • 09/2008 - Setup of Slicer3, first tests with stochastic tractography, work with training datasets
  • 10/2008 - Introduction of python interpreter for implementation of batch utility + diffusion features visualization
  • 11/2008 - Completion of batch utility, testing for NAMIC meeting preparation, tractography workflow first prototype ready
  • 12/2008 - wrap up, finalization of NAMIC meeting presentation for the stochastic tractography module