Fibre Tracking: From Raw Images To Tract Visualisation

Fibre Tracking: From Raw Fibre Tracking: From Raw Images ToImages To

Tract VisualisationTract Visualisation

T.R. Barrick

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

IntroductionIntroduction

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

OverviewOverview

Section 1: Computing the DTI

Section 2: Visualising Coloured

Images

Section 3: Streamline Tractography

Section 4: Visualising Tractograms

Section 1: Computing The Section 1: Computing The Diffusion Tensor Diffusion Tensor

Brownian motion

Random, translational motion

Water DiffusionWater Diffusion

Diffusion CharacteristicsDiffusion 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 (Diffusion Coefficient (DD))

Diffusion is a time dependent processDiffusion is a time dependent process

Molecules diffuse further from their starting point as Molecules diffuse further from their starting point as

time increasestime increases

Units of Units of DD are mm are mm22 s s-1-1

DD is temperature dependent is temperature dependent

DD depends species under consideration depends species under consideration

Water at 37Water at 37°°C; C; DD = 3.0 x 10 = 3.0 x 10-3-3 mm mm22 s s-1-1

Diffusion-WeightingDiffusion-Weighting

Make pulse sequence sensitive to diffusionMake pulse sequence sensitive to diffusion

Add additional gradients into sequenceAdd additional gradients into sequence

Spins move in gradient – phase changesSpins move in gradient – phase changes

These gradients cause signal dephasingThese gradients cause signal dephasing

Results in signal lossResults in signal loss

Diffusion Gradients: Stejskal-Diffusion Gradients: Stejskal-Tanner SequenceTanner Sequence

90° 180° echo

RF

gradient

Diffusion Sensitivity: Diffusion Sensitivity: bb value value

Amount of diffusion sensitivity is called the Amount of diffusion sensitivity is called the bb

valuevalue

bb value depends on the gradient strength, value depends on the gradient strength, GG, ,

duration duration and separation and separation

3δ/ΔγGδb 2

Diffusion-Weighted Images (DWI)

increasing increasing bb factor factor

Diffusion-Weighted Images (DWI)Diffusion-Weighted Images (DWI)

Signal loss is proportional to Signal loss is proportional to bb and and DD

S(0)S(0) is signal without gradients and is signal without gradients and S(b)S(b) is is

signal with gradientssignal with gradients

bD0S

bSln

Diffusion Tensor Imaging Diffusion Tensor Imaging (DTI)(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)

Possible Diffusion Tensor Possible Diffusion Tensor Image AcquisitionImage 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: 2.5mm2.5mm2.8mm

Computation of the DTIComputation 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)

Diffusion Tensor ImagingDiffusion Tensor Imaging

Provides a full description of the second

order diffusion tensor,

zzyzxz

yzyyxy

xzxyxx

DDD

DDD

DDD

D

At each voxel, D is then diagonalised


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.


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).

Diffusion Tensor Orientation Diffusion Tensor Orientation and Shapeand Shape

Prolate, 1 >> 2 3

Oblate, 1 2 >> 3

Spherical, 1 2 3

3

3

2

1

v1

Anisotropic Isotropic

Disc

Invariant Diffusion Measures: Invariant Diffusion Measures: Mean DiffusivityMean Diffusivity

Apparent Diffusion Coefficient (ADC)

Quantitative Quantitative Bright pixels - high diffusionBright pixels - high diffusion

Uniform across WMUniform across WM

Typical WM values; Typical WM values;

ADC = 0.8 x 10ADC = 0.8 x 10-3-3 mm mm2 2 ss-1-1

3/D 21app

Diffusion Anisotropy

ADCx ADCy ADCz

Invariant Diffusion Measures: Invariant Diffusion Measures: Fractional AnisotropyFractional Anisotropy

Fractional anisotropy (Basser et al., 1996)(Basser et al., 1996)

Quantitative, visualizes WMQuantitative, visualizes WM

Bright pixels - high anisotropyBright pixels - high anisotropyData Range 0 to 1

(isotropic to anisotropic)

.

DDD

2

3FA

23

22

21

2app3

2app2

2app1

Section 2: Visualising Section 2: Visualising Coloured ImagesColoured Images

mri3dX – Krish Singh, Aston University

Home page:

– http://www.aston.ac.uk/lhs/staff/singhkd/mri

3dX/index.shtml

Allows visualisation of:

– 24 bit RGB images (shade files, *.shd)

– Analyze format images (*.hdr, *.img)

Visualising Coloured ImagesVisualising 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

mri3dX Environmentmri3dX EnvironmentMain Window

Sagittal Coronal Axial

Principal Diffusion DirectionPrincipal Diffusion Direction

Right-left

Anterior-posterior

Superior-inferior

Red = | vx |Green = | vy |Blue = | vz |

Direction Encoded Colour map (DEC)

Pajevic and Pierpaoli, 1999

Diffusion Tensor ShapeDiffusion Tensor Shape

Red = 1/1 = 1Green = 2/1

Blue = 3/1

Shape Encoded Colour map (SEC)

Prolate

Oblate (Disc)

Sphere

Section 3: Streamline Section 3: Streamline TractographyTractography

Attempt to ‘connect’ voxels on basis of Attempt to ‘connect’ voxels on basis of

directional similarity of coincident eigenvectorsdirectional similarity of coincident eigenvectors

Mori et al.,Mori et al.,Ann Neurol 1999Ann Neurol 1999

Streamline TractographyStreamline 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., 2000Mori et al., 1999

Streamline TractographyStreamline Tractography

Tracts computed in orthograde and

retrograde directions from initial seeds

By using multiple seed points white

matter structures are extracted

Tractography AlgorithmTractography Algorithm

Readtensor

Seed Point

Diagonalisetensor

Readtensor

Seed Point


Diagonalisetensor

Readtensor

Seed Point

FA <threshold?


NO

Diagonalisetensor

Readtensor

Seed Point

FA <threshold?

Angle >threshold?



NO

Diagonalisetensor

Readtensor

Seed Point

Step distance, t, along principal

eigenvector

FA <threshold?

Angle >threshold?

NO



NOInterpolate tensor field

Diagonalisetensor

Readtensor

Seed Point


eigenvector

FA <threshold?

Angle >threshold?

NO



YES

NOInterpolate tensor field

Diagonalisetensor

Readtensor

Seed Point


eigenvector

FA <threshold?

Angle >threshold?

YES

NO

Outputtract

vectors



Section 4: Visualising Section 4: Visualising TractogramsTractograms

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).

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/

GeomViewGeomView

GeomView EnvironmentGeomView Environment

Main Window Tool Bar Camera Window

GeomView File FormatGeomView 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 FormatVECT 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


The conventional suffix for VECT files is

‘*.vect’.

The files must have the following

syntax:


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×6 floats, s(x,y,z), e(x,y,z)

N vector colours: N×4 floats, R G B A)

VECT File FormatVECT File FormatExample 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 FormatVECT File FormatExample 1: Drawing two vectors

Visualising TractogramsVisualising TractogramsExample 2: Corticospinal pathway

e Patient: Biopsy proven

right temporal

glioblastoma

ROIs in Brodmann

Area 6 and through

the base of the

corticospinal tractClark et al., 2003


Seed regions of

interest drawn using…

mriCro – Chris

Rorden, Nottingham

University Home page:

–http://www.psychology.nottingham.ac.uk/staff/cr1/mricro.html


– Streamline tractography (Basser et al., 2000)

– Angle threshold: 45°

– FA threshold: 0.1

– Vector length: 2.0mm

– Whole brain tractography

CQUAD File FormatCQUAD 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


The conventional suffix for CQUAD files

is ‘*.cquad’.


syntax:


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 SlicesVisualising 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)

Visualising Image SlicesVisualising Image SlicesExample 3: Drawing a square

Lighting On Lighting Off

Visualising Image SlicesVisualising Image SlicesExample 4: Constructing an image slice

e

Clark et al., 2003

Visualising Image SlicesVisualising Image SlicesExample 4: Constructing an image slice

OFF File FormatOFF 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)


The conventional suffix for OFF files is

‘*.off’.


syntax:

OFF File FormatOFF File FormatOFF

#edges #faces (N) #vertices

Vertex positions for face N (N×3 x,y,z coordinates)

For face N,

– #vertices followed by vertex order

– Face colour (4 floats, R G B A)


Example 5: Drawing a triangle

Visualising SurfacesVisualising Surfaces

Example 6: Constructing a surface

Draw the region of interest

Triangulated surface patch coordinates via

the marching cubes algorithm

Visualising SurfacesVisualising Surfaces

Example 6: Constructing a surface

Full VisualisationFull Visualisation

Example 7: Tractogram/Slice/Surface

Clark et al., 2003

Creating GeomView MoviesCreating GeomView MoviesStage Tools is required

Download:

http://www.geom.uiuc.edu/ soft

ware /download/StageTools.html

Stage Tools includes software for:

– Loading and unloading image objects

– Specifying rotation, translation and

zooming parameters to GeomView objects

Creating GeomView MoviesCreating GeomView Movies

Movie created inPaint Shop Pro 7

Tiff snapshots output from GeomView

ConclusionConclusion

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

Fibre Tracking: From Raw Images To Tract Visualisation

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