User:Inorton/Slicer4:DTMRI 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. Disadvantages: learning curve; the python suite is less integrated than matlab, but it's getting better. Stability: matlab rarely crashes.
  • Implementation of new algorithms in Slicer opens up larger userbase.

Slicer advantages

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?

• 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 is non-commercial, DTI Studio closed)
• DicomToNRRDConverter test suite: testing process in development for images from many different scanner types.

Slicer disadvantages

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

• Current fiber data model is inefficient for large (tens of thousands) of fiber tracts.
• Missing good ROI selection, clustering, and editing capability for pre-computed fibersets.
• Subset selection and separation is inefficient.
• Labelmap seeding is not multi-threaded so whole-brain tractography takes forever.