Difference between revisions of "User:Inorton/Slicer4:DTMRI Thoughts"

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=Clinical perspective=
 
We are using Slicer3 for 2-4 Neurosurgery planning cases per week. We have used the Slicer3 FiducialSeeding functionality in the OR (with OpenIGTLink/BioImageSuite/BrainLab driving the seeding location).
 
==Workflow==
 
* Scan and push images to clinical PACS and our clinical fMRI processing workstation (has SCP and dicom db capability)
 
* Pull/copy images from clinical PACS and processing workstation to research laptop/workstation
 
* Load all images:
 
** T1, T2, (sometimes CT, PET, CBV from perfusion MR, etc.)
 
** 2-4 thresholded fMRI activation volumes (coregistered and resliced to structural in SPM)
 
** DTI
 
* Preprocess DTI
 
* Coregister all images using BrainsFit
 
* Segment tumor or pathology region
 
* Bring to clinician to use FiducialSeeding to explore area around tumor.
 
* (if requested, re-load DICOMs on clinical navigation suite and re-select tracts identified in Slicer)
 
  
=Background Thoughts=
 
==Audiences==
 
* Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations.
 
* Pipeline users: the underlying implementations need to be abstracted sufficiently to allow creation of pipeline tools for large-study purposes.
 
* Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
 
* DTI Researchers:
 
** Could use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: data-reading and visualization boilerplate code already exists. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; relative stability: matlab rarely crashes.
 
** Implementation of new algorithms in Slicer opens up larger potential userbase.
 
 
==Slicer advantages==
 
See big list of DTI software here: [[User:inorton/DTI_Software_List]].
 
 
There are several excellent DTI-centric applications. What advantages does Slicer have for DTI work?
 
 
* More user-friendly data loading: TrackVis requires command line preprocessing; MedInria and TrackVis require manual gradient entry;  DTI studio is limited to ROI exploration only (as far as I know)
 
* Many segmentation options already available - no external tool (TrackVis, DTI Studio) or separate interface (MedInria) required.
 
* Already integrated with intra-operative systems via OpenIGTLink functionality
 
* Open-source license (TrackVis closed, MedInria non-commercial, DTI Studio closed)
 
* DicomToNRRDConverter test suite: validate DICOM loading from many different scanner types, with special emphasis on DTI private header information.
 
 
==Slicer improvement areas==
 
(this is referring to Slicer3 interactive DTMRI tools: these areas need improvement in Slicer 4)
 
 
* FiducialSeeding is too slow with high-density seeding, low step-values, or large seeding regions. This is probably intrinsic. Possible improvements include using a multi-threaded seeding filter, or using the GPU (?). But that may be superfluous: with good selection tools, dense pre-seeding in whole-brain or at least extended area of interest may be a better option.
 
* Current fiber data model is inefficient for interactive use on large sets (tens of thousands) of fiber tracts.
 
* Missing good interactive ROI selection, clustering, and editing capability for pre-computed fibersets.
 
* Need subset selection and coloring.
 
* Labelmap seeding is not multi-threaded so whole-brain tractography takes.. longer than I would like.
 
* Disjointed interface: no one-stop integrated GUI for full DICOMs->tracts->measurements workflow.
 
 
=Existing NA-MIC resources=
 
 
* teem: the Slicer3 interactive DTI implementation uses teem through the vtkTeem libraries.
 
** http://teem.sourceforge.net/
 
* GTRACT: command-line tools for DTI pipeline processing
 
** http://www.nitrc.org/projects/vmagnotta/
 
** http://wiki.slicer.org/slicerWiki/index.php/GTRACT_V4
 
* UNC DTI Tools (FiberViewer):
 
** http://www.niral.unc.edu/
 
** http://www.ia.unc.edu/dev/download/index.htm
 
 
=Existing open-source external resources=
 
See big list of DTI software here: [[User:inorton/DTI_Software_List]]
 
* (BSD-like licenses)
 
* DiPy: diffusion imaging in python. Currently pre/alpha but in active development. (BSD license)
 
** http://nipy.sourceforge.net/dipy/
 
* DTI-query and CINCH: (BSD license)
 
** http://graphics.stanford.edu/projects/dti/
 

Latest revision as of 20:16, 13 July 2018