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| − | ==Audiences==
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| − | * Clinical/research end users: need simple, efficient, relatively intuitive workflow to generate tractography and perform selection and statistics operations.
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| − | * Pipeline users: the underlying implementations need to be abstracted sufficiently to allow creation of pipeline tools for large-study purposes.
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| − | * Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
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| − | * DTI Researchers:
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| − | ** 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.
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| − | ** Implementation of new algorithms in Slicer opens up larger userbase.
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| − | ==Slicer advantages==
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| − | 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?
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| − | * 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)
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| − | * Many segmentation options already available - no external tool (TrackVis, DTI Studio) or separate interface (MedInria) required.
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| − | * Already integrated with intra-operative systems via OpenIGTLink functionality
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| − | * Open-source license (TrackVis closed, MedInria is non-commercial, DTI Studio closed)
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| − | * DicomToNRRDConverter test suite: testing process in development for images from many different scanner types.
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| − | ==Slicer disadvantages==
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| − | (this is referring to Slicer3: these areas need improvement in Slicer 4)
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| − | * Current fiber data model is inefficient for large (tens of thousands) of fiber tracts.
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| − | * Missing good ROI selection, clustering, and editing capability for pre-computed fibersets.
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| − | * Subset selection and separation is inefficient.
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| − | * Labelmap seeding is not multi-threaded so whole-brain tractography takes forever.
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