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| − | =Clinical perspective=
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| − | 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).
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| − | ==Workflow==
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| − | * Scan and push images to clinical PACS and our clinical fMRI processing workstation (has SCP and dicom db capability)
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| − | * Pull/copy images from clinical PACS and processing workstation to research laptop/workstation
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| − | * Load all images:
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| − | ** T1, T2, (sometimes CT, PET, CBV from perfusion MR, etc.)
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| − | ** 2-4 thresholded fMRI activation volumes (coregistered and resliced to structural in SPM)
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| − | ** DTI
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| − | * Preprocess DTI
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| − | * Coregister all images using BrainsFit
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| − | * Segment tumor or pathology region
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| − | * Bring to clinician to use FiducialSeeding to explore area around tumor.
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| − | * (if requested, re-load DICOMs on clinical navigation suite and re-select tracts identified in Slicer)
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| − | =Background Thoughts=
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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. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; relative stability: matlab rarely crashes.
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| − | ** Implementation of new algorithms in Slicer opens up larger potential userbase.
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| − | ==Slicer advantages==
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| − | See big list of DTI software here: [[User:inorton/DTI_Software_List]].
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| − | There are several excellent DTI-centric applications. 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 non-commercial, DTI Studio closed)
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| − | * DicomToNRRDConverter test suite: validate DICOM loading from many different scanner types, with special emphasis on DTI private header information.
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| − | ==Slicer improvement areas==
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| − | (this is referring to Slicer3 interactive DTMRI tools: these areas need improvement in Slicer 4)
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| − | * 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.
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| − | * Current fiber data model is inefficient for interactive use on large sets (tens of thousands) of fiber tracts.
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| − | * Missing good interactive ROI selection, clustering, and editing capability for pre-computed fibersets.
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| − | * Need subset selection and coloring.
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| − | * Labelmap seeding is not multi-threaded so whole-brain tractography takes.. longer than I would like.
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| − | * Disjointed interface: no one-stop integrated GUI for full DICOMs->tracts->measurements workflow.
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| − | =Existing NA-MIC resources=
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| − | * teem: the Slicer3 interactive DTI implementation uses teem through the vtkTeem libraries.
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| − | ** http://teem.sourceforge.net/
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| − | * GTRACT: command-line tools for DTI pipeline processing
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| − | ** http://www.nitrc.org/projects/vmagnotta/
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| − | ** http://wiki.slicer.org/slicerWiki/index.php/GTRACT_V4
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| − | * UNC DTI Tools (FiberViewer):
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| − | ** http://www.niral.unc.edu/
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| − | ** http://www.ia.unc.edu/dev/download/index.htm
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| − | =Existing open-source external resources=
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| − | See big list of DTI software here: [[User:inorton/DTI_Software_List]]
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| − | * (BSD-like licenses)
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| − | * DiPy: diffusion imaging in python. Currently pre/alpha but in active development. (BSD license)
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| − | ** http://nipy.sourceforge.net/dipy/
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| − | * DTI-query and CINCH: (BSD license)
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| − | ** http://graphics.stanford.edu/projects/dti/
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