Slicer4:VectorImageVisualization

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The main goal of this page is to set the grounds and goals for a common visualization framework for vector images.

Description of vector images

We consider vector-valued images for the purpose of this project any image composed by several scalar images in which the dimensions and spatial transforms are the same.

Examples of these are:

  • DCM-MRI / functional MRI, nowadays handled by Slicer using Junichi's FourDAnalysis module
  • Deformation fields which are the result of a registration process.
  • DWI-MRI, currently handled by Slicer.
  • DTI-Images could be set under this category but their visualization use-cases are, maybe, far too different from the others.
  • More complex representations of diffusion MRI (DSI, Q-Ball, Multishell)
Image type 4th axis semantics Number of samples on the 4th axis
DCM-MRI / fMRI Time not fixed
Multi-Channel Data Contrast not fixed
Deformation field X,Y,Z components of the transform 3
DWI-MRI a function with domain angle x b-value not fixed
DTI-MRI Components of a symmetric positive definite matrix 9 (or 6 as there are only 6 independent components)
More complex representations of diffusion MRI spherical harmonic coefficients, angular functions, etc not fixed

Basic functionalities

  • Multi-component scalar-volume visualization: as with the DWI-images where there is a slider enabling the user to choose which component to visualize
  • Split and merge into individual volumes. Concept is partially implemented in split and merge in the interactive editor
  • Visualization of derived values:
    • dMRI: Glyph-based visualization of tensors, scalar visualization of color by orientation, fractional anisotropy
      • Visualization as vectors (deformation fields, principal diffusion direction)
      • Visualization as Ellipsoids or multiquadrics (representation of DT-MRI)
    • DCE: visualization of the parameter values extracted from model fitting such as tofts model.
  • 2D-plot exploring of a single voxel along the 4th axis as in FourDAnalysis module
  • Colormap visualization: Mapping from a 3D vector to the RGB components. Examples are
    • DT-MRI visualization
    • Direction of the deformation field at each voxel
    • combinations of multichannel volumes (microscopy)