1 / 68

Fibre Tracking: From Raw Images To Tract Visualisation

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

sorley
Download Presentation

Fibre Tracking: From Raw Images To Tract Visualisation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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

  3. 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

  4. Overview • Section 1: Computing the DTI • Section 2: Visualising Coloured Images • Section 3: Streamline Tractography • Section 4: Visualising Tractograms

  5. Section 1: Computing The Diffusion Tensor Brownian motion

  6. Water Diffusion Random, translational motion

  7. 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)

  8. 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

  9. 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

  10. Diffusion Gradients: Stejskal-Tanner Sequence 90° echo 180° RF gradient d d D

  11. 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

  12. Diffusion-Weighted Images (DWI) increasing b factor

  13. 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

  14. 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)

  15. 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

  16. 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)

  17. Diffusion Tensor Imaging • Provides a full description of the second order diffusion tensor, • At each voxel, D is then diagonalised

  18. 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.

  19. 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).

  20. 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

  21. 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

  22. Diffusion Anisotropy ADCx ADCy ADCz

  23. 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)

  24. 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)

  25. 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

  26. mri3dX Environment Main Window Axial Sagittal Coronal

  27. Right-left Anterior-posterior Superior-inferior Principal Diffusion Direction Direction Encoded Colour map (DEC) Red = | vx | Green = | vy | Blue = | vz | Pajevic and Pierpaoli, 1999

  28. Diffusion Tensor Shape Shape Encoded Colour map (SEC) Red = 1/1 = 1 Green = 2/1 Blue = 3/1 Prolate Oblate (Disc) Sphere

  29. Section 3: Streamline Tractography • Attempt to ‘connect’ voxels on basis of directional similarity of coincident eigenvectors Mori et al., Ann Neurol 1999

  30. 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

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

  32. Tractography Algorithm Seed Point Read tensor

  33. Tractography Algorithm Seed Point Diagonalise tensor Read tensor

  34. Tractography Algorithm Seed Point FA < threshold? Diagonalise tensor Read tensor

  35. Tractography Algorithm Seed Point FA < threshold? Diagonalise tensor Read tensor NO Angle > threshold? Basser et al., 1999 Mori et al., 1999

  36. 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

  37. 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

  38. 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

  39. 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).

  40. 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/

  41. GeomView Environment Main Window Tool Bar Camera Window

  42. 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

  43. 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

  44. VECT File Format • The conventional suffix for VECT files is ‘*.vect’. • The files must have the following syntax:

  45. 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)

  46. 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)

  47. VECT File Format • Example 1: Drawing two vectors

  48. 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

  49. 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

  50. 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

More Related