Transcript Diffusion Tensor Processing and Visualization
Diffusion Tensor Processing and Visualization Ross Whitaker University of Utah National Alliance for Medical Image Computing
Acknowledgments Contributors: • A. Alexander • G. Kindlmann • L. O’Donnell • J. Fallon National Alliance for Medical Image Computing (NIH U54EB005149)
Diffusion in Biological Tissue • Motion of water through tissue • Sometimes faster in some directions than others Kleenex newspaper • Anisotropy : diffusion rate depends on direction isotropic anisotropic G. Kindlmann
The Physics of Diffusion • Density of substance changes (evolves) over time according to a differential equation (PDE) Change in density Diffusion – matrix, tensor (2x2 or 3x3) Derivatives (gradients) in space
Solutions of the Diffusion Equation • Simple assumptions – Small dot of a substance (point) – D constant everywhere in space • Solution is a multivariate Gaussian – Normal distribution – D plays the role of the covariance matrix\ are needed to see this picture.
• This relationship is not a coincidence – Probabilistic models of diffusion (random walk)
D Is A Special Kind of Matrix • The universe of matrices Matrices Square Skew symmetric Nonsquare Symmetric D is a “square, symmetric, positive definite matrix” (SPD) Positive
Properties of SPD • Bilinear forms and quadratics Quadratic equation – implicit equation for ellipse (ellipsoid in 3D) • Eigen Decomposition – Lambda – shape information, independent of orientation – R – orientation, independent of shape – Lambda’s > 0
l 2
v
1 Eigen Directions and Values (Principle Directions)
v
3 l 1 l 1 l 3 l 2
v
1
v
2
v
2
Tensors From Diffusion-Weighted • Big assumption Images – At the scale of DW-MRI measurements – Diffusion of water in tissue is approximated by Gaussian • Solution to heat equation with constant diffusion tensor • Stejskal-
Tanner equation
k th – Relationship between the DW images and Physical constants D Strength of gradient Duration of gradient pulse Read-out time DW Image Base image Gradient direction
Tensors From Diffusion-Weighted Images • Stejskal-
Tanner equation
– Relationship between the DW images and D Physical constants Strength of gradient Duration of gradient pulse Read-out time k th DW Image Base image Gradient direction
Tensors From Diffusion-Weighted Images 2D • Solving S-T for D – Take log of both sides – Linear system for elements of D – Six gradient directions (3 in 2D) uniquely specify D – More gradient directions overconstrain D • Solve least-squares » (constrain lambda>0) S-T Equation
Shape Measures on Tensors • Represent or visualization shape • Quanitfy meaningful aspect of shape • Shape vs size Different sizes/orientations Different shapes
Measuring the Size of A Tensor • Length – ( l 1 – ( l 1 2 + l 2 2 + l 2 + l 3 2 ) 1/2 + l 3 )/3 • Area – ( l 1 l 2 + l 1 l 3 + l 2 l 3 ) • Volume – ( l 1 l 2 l 3 ) Sometimes used.
Generally used.
Also called: “Root sum of squares” “Diffusion norm” “Frobenius norm” Also called: “Mean diffusivity” “Trace”
l 1 l 3 Shape Other Than Size l 1 >= l 2 >= l 3 l 2 Barycentric shape space (C S ,C L ,C P ) Westin, 1997 G. Kindlmann
Reducing Shape to One Number Fractional Anisotropy Properties: Normalized variance of eigenvalues Difference from sphere FA (not quite)
FA As An Indicator for White Matter • Visualization – ignore tissue that is not WM • Registration – Align WM bundles • Tractography – terminate tracts as they exit WM • Analysis – Axon density/degeneration – Myelin • Big question – What physiological/anatomical property does FA measure?
Various Measures of Anisotropy A 1 VF RA FA A. Alexander
Visualizing Tensors: Direction and Shape • Color mapping • Glyphs
Coloring by Principal Diffusion Direction • Principal eigenvector, linear anisotropy determine color e 1 Coronal R = |
e
1
.
x | G = |
e
1
.
y | B = |
e
1
.
z | Axial Pierpaoli, 1997 Sagittal G. Kindlmann
Issues With Coloring by Direction • Set transparency according to FA (highlight tracts) • Coordinate system dependent • Primary colors dominate – Perception: saturated colors tend to look more intense – Which direction is “cyan”?
Visualization with Glyphs • Density and placement based on FA or detected features • Place ellipsoids at regular intervals
Backdrop: FA Color: RGB(
e
1 ) G. Kindlmann
Glyphs: ellipsoids
Problem:
Visual ambiguity
Worst case scenario: one viewpoint: ellipsoids another viewpoint:
Glyphs: cuboids
Problem:
missing symmetry
Superquadrics Barr 1981
Superquadric Glyphs for Visualizing DTI Kindlmann 2004
Worst case scenario, revisited
Backdrop: FA Color: RGB(
e
1 )
Backdrop: FA Color: RGB(
e
1 )
Backdrop: FA Color: RGB(
e
1 )
Backdrop: FA Color: RGB(
e
1 )
Backdrop: FA Color: RGB(
e
1 )
Backdrop: FA Color: RGB(
e
1 )
Backdrop: FA Color: RGB(
e
1 )
Going Beyond Voxels: Tractography • Method for visualization/analysis • Integrate vector field associated with grid of principle directions • Requires – Seed point(s) – Stopping criteria • FA too low • Directions not aligned (curvature too high) • Leave region of interest/volume
DTI Tractography Seed point(s) Move marker in discrete steps and find next direction Direction of principle eigen value
Tractography J. Fallon
Whole-Brian White Matter Architecture L. O’Donnell 2006
Atlas Generation Analysis Automatic Segmentation
Find the path(s) between A and B that is most consistent with the data Path of Interest D. Tuch and Others A
B
The Problem with Tractography How Can It Work?
• Integrals of uncertain quantities are prone to error – Problem can be aggravated by nonlinearities • Related problems – Open loop in controls (tracking) – Dead reckoning in robotics Wrong turn Nonlinear: bad information about where to go
Mathematics and Tensors • Certain basic operations we need to do on tensors – Interpolation – Filtering – Differences – Averaging – Statistics • Danger – Tensor operations done element by element • Mathematically unsound • Nonintuitive
Averaging Tensors • What should be the average of these two tensors?
Linear Average Componentwise
Arithmetic Operations On Tensor • Don’t preserve size – Length, area, volume • Reduce anisotropy • Extrapolation –> nonpositive, nonsymmetric • Why do we care?
– Registration/normalization of tensor images – Smoothing/denoising – Statistics mean/variance
What Can We Do?
(Open Problem) • Arithmetic directly on the DW images – How to do statics?
– Rotational invariance • Operate on logarithms of tensors (Arsigny) – Exponent always positive • Riemannian geometry (Fletcher, Pennec) – Tensors live in a curved space
Riemannian Arithmetic Interpolation Example Interpolation
Low-Level Processing DTI Status • Set of tools in ITK – Linear and nonlinear filtering with Riemannian geometry – Interpolation with Riemannian geometry – Set of tools for processing/interpolation of tensors from DW images • More to come…
Questions