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

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(Created page with '==Audiences== * Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations. * Pip…')
 
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=Background=
 
==Audiences==
 
==Audiences==
 
* Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations.
 
* Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations.
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* Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
 
* Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
 
* DTI Researchers:  
 
* DTI Researchers:  
−
** Could use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: data-reading and visualization boilerplate code already exists. Disadvantages: learning curve; the python suite is less integrated than matlab, but it's getting better. Stability: matlab rarely crashes.
+
** 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 userbase.
+
** Implementation of new algorithms in Slicer opens up larger potential userbase.
  
 
==Slicer advantages==
 
==Slicer advantages==
 +
See big list of DTI software here: [[User:inorton/DTI_Software_List]].
  
−
There are several excellent DTI-centric applications (see big list of DTI software: [[User:inorton/DTI_Software]]). What advantages does Slicer have for DTI work?
+
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)
 
* 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.
 
* 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
 
* Already integrated with intra-operative systems via OpenIGTLink functionality
−
* Open-source license (TrackVis closed, MedInria is non-commercial, DTI Studio closed)
+
* Open-source license (TrackVis closed, MedInria non-commercial, DTI Studio closed)
−
* DicomToNRRDConverter test suite: testing process in development for images from many different scanner types.
+
* DicomToNRRDConverter test suite: test suite exists to validate DICOM loading from many different scanner types, with special emphasis on DTI private header information.
  
 
==Slicer disadvantages==
 
==Slicer disadvantages==
−
(this is referring to Slicer3: these areas need improvement in Slicer 4)
+
(this is referring to Slicer3 interactive DTMRI tools: these areas need improvement in Slicer 4)
  
−
* Current fiber data model is inefficient for large (tens of thousands) of fiber tracts.
+
* Current fiber data model is inefficient for interactive use on large sets (tens of thousands) of fiber tracts.
−
* Missing good ROI selection, clustering, and editing capability for pre-computed fibersets.
+
* Missing good interactive ROI selection, clustering, and editing capability for pre-computed fibersets.
−
* Subset selection and separation is inefficient.
+
* Need subset selection and coloring.
 
* Labelmap seeding is not multi-threaded so whole-brain tractography takes forever.
 
* Labelmap seeding is not multi-threaded so whole-brain tractography takes forever.
 +
* Disjointed interface: no integrated GUI for full DICOMs->tracts->measurements workflow.
 +
 +
=Existing NA-MIC resources=
 +
 +
* teem: the Slicer3 interactive DTI implementation is based largely on 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/

Revision as of 16:07, 11 January 2011

Background

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: test suite exists to validate DICOM loading from many different scanner types, with special emphasis on DTI private header information.

Slicer disadvantages

(this is referring to Slicer3 interactive DTMRI tools: these areas need improvement in Slicer 4)

  • 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 forever.
  • Disjointed interface: no integrated GUI for full DICOMs->tracts->measurements workflow.

Existing NA-MIC resources

Existing open-source external resources

See big list of DTI software here: User:inorton/DTI_Software_List

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