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 11
  • The fiber selection function can be found in Diffusion -> Tractography -> FiberBundleLabelSelect module.
  • The fiber bundle cropping function can be found in Tractography Display module. http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.3/Modules/TractographyDisplay
  • Sidong's next task would be learn qt.QImage object and see how to manipulate it in Python. Check Steve's code at https://github.com/pieper/CompareVolumes/blob/master/CompareVolumes.py
  • A control panel style mosaic to flexibly display different teams or patients. A summary table showing the statistics.

• April 14-18
  • Try Brain Visa trick on mac http://brainvisa.info/forum/viewtopic.php?f=2&t=1713#p6399
  • Explore Nipype to see whether we can convert Gifti to VTK or STL, or other compatible format with Slicer http://www.mit.edu/~satra/nipype-nightly/users/examples/dmri_connectivity.html
  • Discussed the response letter for MICCAI submissions
  • Next task would be
    • Load the test data and create the scene views manually
    • Link the scene views to individual 3D mosaic viewers
    • Test one MRI volume in 4 viewers first; then test 4 MRI volumes in 4 viewers; finally test the DTI data

• 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 by changing RAS to LPI coordinate system; 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)

• May 05 - May 09
  • Reconstruct the tensors using eigen values and eigen vectors. Slicer could not handle eigen vectors directly, so we ned to calculate the 7 coefficients in the tensor matrix.
  • Our multi-channel pattern analysis paper was accepted by CMIG.
  • Run DTIPrep for all the datasets.
  • Clean up and classify all the processed datasets.
  • Send email to Francois checking what are the differences VC and QCed results.