Difference between revisions of "SidongLiu Update"

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*** Build a Python wrapper which point to the Brain Visa executable files and run Brain Visa in Slicer.   
 
*** Build a Python wrapper which point to the Brain Visa executable files and run Brain Visa in Slicer.   
 
*** Write a simple script to 1) convert nhdr to Nifty; 2) filp left / right; 3) run Morphologist pipeline in Brain Visa; 4) load output .ply file in Slicer; and 5) apply 'BrainVisatoSlicer' transform if the coordinate differences were independent to the datasets.
 
*** Write a simple script to 1) convert nhdr to Nifty; 2) filp left / right; 3) run Morphologist pipeline in Brain Visa; 4) load output .ply file in Slicer; and 5) apply 'BrainVisatoSlicer' transform if the coordinate differences were independent to the datasets.
** Test the DWIConverter module to convert DICOM to nhdr with '...high-b.raw.gz' dataset and compare the result to '...high-b.nhdr'.
+
** Test the DWIConverter module to convert DICOM to nhdr using the datasets '...high-b.raw.gz' dataset and compare the output to '...high-b.nhdr'.
 
** New tasks regarding the test dataset:  
 
** New tasks regarding the test dataset:  
 
*** Create the tractography for extracted dataset only using single tensor algorithm
 
*** Create the tractography for extracted dataset only using single tensor algorithm
*** Compare the result to that of original dataset generated by UKF extension
+
*** Compare the result to that of original dataset using UKF extension
 
*** Run the DTIPrep extension on both datasets (original and extracted)
 
*** Run the DTIPrep extension on both datasets (original and extracted)

Revision as of 17:28, 1 May 2014

Home < SidongLiu Update
  • Feb 28
    • MICCAI paper submissions
  • Mar 12 - 14
    • Start to work at SPL, 75 Fransic St
  • Mar 17 - 21
    • EMBC paper submission
    • Implement a new functionality for peritumoral tract exploration
  • Mar 24 - 28
    • MICCAI paper reviews
    • SNMMI abstract papers accepted
    • Reformat the Hausdorff outputs
  • Mar 31 - Apr 4
    • Postdoctoral fellowship application
    • Start to work at SPL, Boylston St
  • April 7-11
    • CMIG journal revised version submission
    • ACM MM abstract preparation
    • Work on DTI Editor module ("tract extractor")
  • April 21-25
    • Made progress to the Mosaic Viewer module. Now it could display and manipulate multiple independent 3D viewers at the same time
    • Discussed DTI Challenge preparation. Need to install Evernote and Live Minutes to enhance collaboration
    • Next step would be to finalize the Mosaic Viewer by creating a scene view for the it
    • Submitted the Grants-in-Aid application
    • The original test dataset is a HARDI dataset with 69 gradients plus 4 baselines, and we extracted 30 volumes whose b-values equal to 1000.
  • April 28 - May 02
    • Finalized the Mosaic Viewer module.
    • Successfully loaded and corrected the Brain Visa surface files into Slicer. Next step would be streamline the process. There might be three potential solutions:
      • Build CLI modules (SlicerToBrainVisa Import and Export) to integrate Brain Visa morphologist tools in Slicer. Check NiftyReg extension as an example (https://github.com/spujol/niftyregExtension) and Hello World tutorial to learn how to integrate .cxx plugins (http://www.slicer.org/slicerWiki/images/2/22/ProgrammingIntoSlicer3.6_SoniaPujol.pdf). Note this tutorial might need to be updated to Slicer 4.
      • Build a Python wrapper which point to the Brain Visa executable files and run Brain Visa in Slicer.
      • Write a simple script to 1) convert nhdr to Nifty; 2) filp left / right; 3) run Morphologist pipeline in Brain Visa; 4) load output .ply file in Slicer; and 5) apply 'BrainVisatoSlicer' transform if the coordinate differences were independent to the datasets.
    • Test the DWIConverter module to convert DICOM to nhdr using the datasets '...high-b.raw.gz' dataset and compare the output to '...high-b.nhdr'.
    • New tasks regarding the test dataset:
      • Create the tractography for extracted dataset only using single tensor algorithm
      • Compare the result to that of original dataset using UKF extension
      • Run the DTIPrep extension on both datasets (original and extracted)