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Fibre Tracking: From Raw Images To Tract Visualisation. T.R. Barrick St. George’s Hospital Medical School, London, United Kingdom. Introduction.

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slide1

Fibre Tracking: From Raw Images ToTract Visualisation

T.R. Barrick

St. George’s Hospital Medical School, London, United Kingdom.

introduction
Introduction
  • Diffusion Tensor Magnetic Resonance Imaging has recently emerged as the technique of choice for representation of white matter pathways of the human brain in vivo
objectives
Objectives
  • To show how Diffusion Tensor Images (DTIs) are generated from Diffusion Weighted Images (DWIs)
  • To demonstrate how freely available software may be used to visualise coloured images and tractography results
slide4

Overview

  • Section 1: Computing the DTI
  • Section 2: Visualising Coloured Images
  • Section 3: Streamline Tractography
  • Section 4: Visualising Tractograms
water diffusion
Water Diffusion

Random, translational motion

diffusion characteristics
Diffusion Characteristics
  • In a large structure the self diffusion of water is more or less free (isotropy)
  • In small structures such as axons the diffusion is restricted in some directions more than others (anisotropy)
diffusion coefficient d
Diffusion Coefficient (D)
  • Diffusion is a time dependent process
  • Molecules diffuse further from their starting point as time increases
  • Units of D are mm2 s-1
  • D is temperature dependent
  • D depends species under consideration
  • Water at 37°C; D = 3.0 x 10-3 mm2 s-1
slide9

Diffusion-Weighting

  • Make pulse sequence sensitive to diffusion
  • Add additional gradients into sequence
  • Spins move in gradient – phase changes
  • These gradients cause signal dephasing
  • Results in signal loss
slide11

Diffusion Sensitivity: b value

  • Amount of diffusion sensitivity is called the b value
  • b value depends on the gradient strength, G, duration d and separation D
slide13

Diffusion-Weighted Images (DWI)

  • Signal loss is proportional to b and D
  • S(0) is signal without gradients and S(b) is signal with gradients
slide14

Diffusion Tensor Imaging (DTI)

  • Acquire DWI sensitised in at least 6 different directions
    • (x,y,0), (x,0,z), (0,y,z), (-x,y,0), (-x,0,z), (0,-y,z))
    • Plus image without diffusion weighting (T2)
slide15

Possible Diffusion Tensor Image Acquisition

  • 1.5T GE Signa MRI (max field 22 mT m-1)
  • Diffusion-weighted axial EPI
    • b=1000 s mm-2
    • 12 directions
    • 4 averages
  • Voxel size: 2.5mm2.5mm2.8mm
computation of the dti
Computation of the DTI
  • Subject DWIs coregistered to image without diffusion weighting(Haselgrove and Moore, 1996)
  • General linear model used to compute D at each voxel
    • Uses observed diffusion weightings and the b-matrix of diffusion sensitisation(Basser et al., 1996)
slide17

Diffusion Tensor Imaging

  • Provides a full description of the second order diffusion tensor,
  • At each voxel, D is then diagonalised
slide18

Diffusion Tensor Imaging

  • Eigenvalues and eigenvectors of D correspond to principal diffusivities and principal diffusion directions
  • Necessarily 3 eigenvalues,
    • Principal diffusivities 1, 2, and 3.
    • Invariant under rotation.
slide19

Diffusion Tensor Imaging

  • For each eigenvalue the corresponding diffusion direction is given by the eigenvector, v1, v2, and v3.
  • Direction of principal diffusivity is eigenvector corresponding to largest eigenvalue (diffusivity).
slide20

Diffusion Tensor Orientation and Shape

Oblate,1 2 >> 3

Prolate,1 >> 2  3

Disc

3

2

3

1

Spherical,1 2  3

v1

Anisotropic

Isotropic

slide21

Invariant Diffusion Measures: Mean Diffusivity

  • Apparent Diffusion Coefficient (ADC)
  • Quantitative
  • Bright pixels - high diffusion
  • Uniform across WM
  • Typical WM values;

ADC = 0.8 x 10-3 mm2 s-1

slide23

Invariant Diffusion Measures: Fractional Anisotropy

  • Fractional anisotropy (Basser et al., 1996)
  • Quantitative, visualizes WM
  • Bright pixels - high anisotropy

Data Range 0 to 1

(isotropic to anisotropic)

section 2 visualising coloured images
Section 2: Visualising Coloured Images
  • mri3dX – Krish Singh, Aston University
  • Home page:
    • http://www.aston.ac.uk/lhs/staff/singhkd/mri3dX/index.shtml
  • Allows visualisation of:
    • 24 bit RGB images (shade files, *.shd)
    • Analyze format images (*.hdr, *.img)
slide25

Visualising Coloured Images

  • 24 bit RGB images
    • 3 stacked 8 bit volumes (each 256×256×N)
    • Order: Red, Green, Blue
    • No header
  • N.B. Due to the *.shd file’s lack of a header an image with identical height must be loaded prior to loading the *.shd file
slide26

mri3dX Environment

Main Window

Axial

Sagittal

Coronal

slide27

Right-left

Anterior-posterior

Superior-inferior

Principal Diffusion Direction

Direction Encoded

Colour map (DEC)

Red = | vx |

Green = | vy |

Blue = | vz |

Pajevic and Pierpaoli, 1999

slide28

Diffusion Tensor Shape

Shape Encoded

Colour map (SEC)

Red = 1/1 = 1

Green = 2/1

Blue = 3/1

Prolate

Oblate (Disc)

Sphere

section 3 streamline tractography
Section 3: Streamline Tractography
  • Attempt to ‘connect’ voxels on basis of directional similarity of coincident eigenvectors

Mori et al.,

Ann Neurol 1999

slide30

Streamline Tractography

  • Tracts generated from DTI
  • Define step vector length, e.g. t = 1.0 mm
  • Define tract termination criteria
    • Fractional anisotropy, e.g. FA < 0.1
    • Angle between consecutive eigenvectors, e.g. angle > 45°

Basser et al., 2000

Mori et al., 1999

slide31

Streamline Tractography

  • Tracts computed in orthograde and retrograde directions from initial seeds
  • By using multiple seed points white matter structures are extracted
slide32

Tractography Algorithm

Seed Point

Read

tensor

slide33

Tractography Algorithm

Seed Point

Diagonalise

tensor

Read

tensor

slide34

Tractography Algorithm

Seed Point

FA <

threshold?

Diagonalise

tensor

Read

tensor

slide35

Tractography Algorithm

Seed Point

FA <

threshold?

Diagonalise

tensor

Read

tensor

NO

Angle >

threshold?

Basser et al., 1999

Mori et al., 1999

slide36

Tractography Algorithm

Seed Point

FA <

threshold?

Diagonalise

tensor

Read

tensor

NO

Step distance, t,

along principal

eigenvector

Angle >

threshold?

NO

Basser et al., 1999

Mori et al., 1999

slide37

Tractography Algorithm

Seed Point

FA <

threshold?

Diagonalise

tensor

Read

tensor

NO

Interpolate

tensor field

Step distance, t,

along principal

eigenvector

Angle >

threshold?

NO

Basser et al., 1999

Mori et al., 1999

slide38

Tractography Algorithm

Seed Point

FA <

threshold?

YES

Diagonalise

tensor

Read

tensor

NO

Output

tract

vectors

Interpolate

tensor field

Step distance, t,

along principal

eigenvector

Angle >

threshold?

NO

YES

Basser et al., 2000

Mori et al., 1999

section 4 visualising tractograms
Section 4: Visualising Tractograms
  • GeomView - interactive 3D viewing program for Unix and Linux (openGL)
  • View and manipulate 3D objects
  • Allows rotation, translation, zooming
  • Geometry Center, University of Minnesota, USA (1992-1996).
geomview
GeomView
  • Although the Geometry Center closed in 1998, GeomView is still available and continues to evolve
  • Home page – http://www.geomview.org/
  • Download from:
    • http://www.geomview.org/download/
geomview environment
GeomView Environment

Main Window

Tool Bar

Camera Window

geomview file format
GeomView File Format
  • Documentation available online
  • GeomView input file format:
    • Object Oriented Graphics Library (OOGL)
    • OOGL files may be either text (ASCII) or binary files
vect file format
VECT File Format
  • VECT is an OOGL format that allows visualisation of vectors or strings of vectors in GeomView
    • Number of vectors (steps) in tractogram (N)
    • Start (s) and end (e) points for each vector
    • RGB colour (c) for each vector
vect file format44
VECT File Format
  • The conventional suffix for VECT files is ‘*.vect’.
  • The files must have the following syntax:
vect file format45
VECT File Format
  • VECT
  • #edges (N) #vertices (N×2) #colours (N)
  • #vertices per edge (i.e. 2, N times)
  • #colours for each vector (i.e. 1, N times)
  • N×2 vertices: N×6floats, s(x,y,z), e(x,y,z)
  • N vector colours: N×4 floats, R G B A)
vect file format46
VECT File Format
  • Example 1: Drawing two vectors
    • N = 2
    • Edge 1 (2 vertices v1 = (1 0 0), v2 = (0 1 0))
    • Edge 2 (2 vertices v1 = (0 1 0), v2 = (0 0 1))
    • Colours (absolute value DEC)
      • For Edge 1 (R G B A) = (1 1 0 1)
      • For Edge 2 (R G B A) = (0 1 1 1)
vect file format47
VECT File Format
  • Example 1: Drawing two vectors
visualising tractograms

e

Visualising Tractograms
  • Example 2: Corticospinal pathway
  • Patient: Biopsy proven right temporal glioblastoma
  • ROIs in Brodmann Area 6 and through the base of the corticospinal tract

Clark et al., 2003

visualising tractograms49
Visualising Tractograms
  • Example 2: Corticospinal pathway
  • Seed regions of interest drawn using…
  • mriCro – Chris Rorden, Nottingham University
  • Home page:
    • http://www.psychology.nottingham.ac.uk/staff/cr1/mricro.html
visualising tractograms50
Visualising Tractograms
  • Example 2: Corticospinal pathway
    • Streamline tractography (Basser et al., 2000)
    • Angle threshold: 45°
    • FA threshold: 0.1
    • Vector length: 2.0mm
    • Whole brain tractography
visualising tractograms51
Visualising Tractograms
  • Example 2: Corticospinal pathway
cquad file format
CQUAD File Format
  • CQUAD is an OOGL format that allows visualisation of coloured quadrilaterals in GeomView
    • Positions of the 4 vertices
    • RGB colour for each of the 4 vertices
  • For visualisation of image slices in GeomView
slide53

CQUAD File Format

  • The conventional suffix for CQUAD files is ‘*.cquad’.
  • The files must have the following syntax:
cquad file format54
CQUAD File Format
  • CQUAD
  • N×4 vertices for N quadrilaterals (each consisting of N×4, x,y,z coordinates)
  • Corresponding N×4 vertex colours (each consisting of N×4 floats, R G B A)
visualising image slices
Visualising Image Slices
  • Example 3: Drawing a square
    • CQUAD
    • 4 vertices with associated colours
      • v1 = (1 1 0) c1 = (1 0 0 1)
      • v2 = (1 -1 0) c2 = (1 0 0 1)
      • v3 = (-1 -1 0) c3 = (0 1 0 1)
      • v4 = (-1 1 0) c4 = (0 1 0 1)
slide56

Visualising Image Slices

  • Example 3: Drawing a square

Lighting On

Lighting Off

slide57

e

Visualising Image Slices

  • Example 4: Constructing an image slice

Clark et al., 2003

slide58

Visualising Image Slices

  • Example 4: Constructing an image slice
off file format
OFF File Format
  • OFF is an OOGL format that allows visualisation of polygons in GeomView
  • For visualisation of triangulated surfaces output from the marching cubes algorithm (Lorenson and Cline, 1987)
off file format60
OFF File Format
  • The conventional suffix for OFF files is ‘*.off’.
  • The files must have the following syntax:
off file format61
OFF File Format
  • OFF
  • #edges #faces (N) #vertices
  • Vertex positions for face N (N×3x,y,z coordinates)
  • For face N,
    • #vertices followed by vertex order
    • Face colour (4 floats, R G B A)
off file format62
OFF File Format
  • Example 5: Drawing a triangle
visualising surfaces
Visualising Surfaces
  • Example 6: Constructing a surface
    • Draw the region of interest
    • Triangulated surface patch coordinates via the marching cubes algorithm
visualising surfaces64
Visualising Surfaces
  • Example 6: Constructing a surface
full visualisation
Full Visualisation
  • Example 7: Tractogram/Slice/Surface

Clark et al., 2003

creating geomview movies
Creating GeomView Movies
  • Stage Tools is required
  • Download: http://www.geom.uiuc.edu/ software /download/StageTools.html
  • Stage Tools includes software for:
    • Loading and unloading image objects
    • Specifying rotation, translation and zooming parameters to GeomView objects
creating geomview movies67
Creating GeomView Movies

Tiff snapshots output

from GeomView

Movie created in

Paint Shop Pro 7

slide68

Conclusion

  • Computation of the Diffusion Tensor from Magnetic Resonance Images has been described
  • Freely available software has been shown to be capable of visualising coloured images and tractograms
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