User:Inorton/Slicer4:DTMRI Thoughts

Clinical perspective

We are using DTI in the context of 2-4 Neurosurgery planning cases per week. We use Slicer FiducialSeeding, tractography selection on a commercial neuronavigation program, and occasionally other Slicer DTI tools as case and surgeon requests dictate. 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 DICOM SCP and database 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
  • convert to NRRD
  • estimate tensors
• Coregister all images using BrainsFit
  • DTI Baseline -> T2
  • T2 -> T1
  • additional images as available
• Segment tumor or pathology region
  • manually in editor with levelset selection or drawing tools
  • semi-automatically in editor using GrowCutSegment
• 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.
• Clinical developers: integrate DTI functionality for domain-specific purposes (neurosurgery, neurology, etc.)
• Pipeline developers: the underlying implementations need to be abstracted sufficiently to allow creation of pipeline tools for large-study purposes.
• DTI Researchers:
  • Can use Slicer+ipython+numpy+... instead of matlab and custom code. Advantages: image conversion (DICOM) and visualization taken care of by Slicer. Challenges: learning curve; the python suite is less integrated than matlab, but it's getting better; pure matlab is relatively more stable (mex stuff can introduce problems).
  • 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.
• Need interactive, user-friendly tract/bundle statistics generation.
• 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/