Difference between revisions of "SidongLiu Update"
From NAMIC Wiki
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*** Test one MRI volume in 4 viewers first; then test 4 MRI volumes in 4 viewers; finally test the DTI data | *** Test one MRI volume in 4 viewers first; then test 4 MRI volumes in 4 viewers; finally test the DTI data | ||
− | * April 21- | + | * April 21-25 |
− | ** Made progress to the Mosaic Viewer module. Now it could display and manipulate multiple independent 3D viewers at the same time | + | ** 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 | + | ** 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. | + | ** 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 with '...high-b.raw.gz' dataset and compare the result 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 generated by UKF extension | ||
+ | *** Run the DTIPrep extension on both datasets (original and extracted) |
Revision as of 18:57, 30 April 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 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; 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'.
- 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 generated by UKF extension
- Run the DTIPrep extension on both datasets (original and extracted)