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MR Diffusion Tensor Imaging, Tractography. Richard Watts, D.Phil. Citigroup Biomedical Imaging Center Weill Medical College of Cornell University Box 234, 1300 York Avenue, New York, NY 10021 Email [email protected] , Telephone 212 746-5781. Acknowledgements.

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mr diffusion tensor imaging tractography

MR Diffusion Tensor Imaging, Tractography

Richard Watts, D.Phil.

Citigroup Biomedical Imaging Center

Weill Medical College of Cornell University

Box 234, 1300 York Avenue, New York, NY 10021

Email [email protected], Telephone 212 746-5781

acknowledgements
Acknowledgements
  • Weill Medical College of Cornell University
    • Department of Radiology
      • Aziz Ulug, Linda Heier.
    • Citigroup Biomedical Imaging Center
      • Doug Ballon, Jon Dyke, Katherine Kolbert.
    • Sackler Institute
      • BJ Casey, Matt Davidson, Katie Thomas.
outline
Outline
  • Background
    • Diffusion
    • Restricted Diffusion and Anisotropy
  • Methods
    • Data Acquisition
    • Display of Diffusion Tensor Data
    • Fiber Tracking
    • Problems and Limitations
  • Examples
diffusion equation
Diffusion Equation

r = Displacement (mm)

D = Diffusion constant

(mm2/s)

t = Time (mm)

distance scales
Distance Scales

Question: What distance do protons travel during an EPI readout time?

Assume: Diffusion constant ~ 10-3 mm2/s

Time ~ 100 ms = 0.1s

The root mean square (RMS) distance is ~0.02mm = 20μm

Such an experiment is sensitive to changes in diffusion caused by structures on this scale or smaller

data acquisition spin echo
180º

90º

RF

time

TE

Diffusion Gradients

g

Gx

where

Data Acquisition – Spin Echo

Echo

anisotropy
Anisotropy

Isotropic:

Having the same properties in all directions

Anisotropic:

Not isotropic; having different properties in different directions

Webster’s Dictionary

data acquisition spin echo1
180º

90º

RF

time

TE

Gx

Gy

Gz

Data Acquisition – Spin Echo

Echo

Linear combination of gradients - measure component of diffusion in any direction

diffusion tensor imaging
Diffusion Tensor Imaging
  • Tensor is a mathematical model of the directional anisotropy of diffusion
  • Represented by a 3x3 symmetric matrix  6 degrees of freedom
  • Fit experimental data to the tensor model
  • From the tensor, we can calculate
    • Direction of greatest diffusion
    • Degree of anisotropy
    • Diffusion constant in any direction
calculated quantities
* Various definitionsCalculated Quantities…

T2-Weighted Image

“Average” Diffusion*

Degree of Anisotropy*

Diffusion along X

Diffusion along Y

Diffusion along Z

1 approximately isotropic diffusion
1. (Approximately) Isotropic Diffusion

How a blob of ink would spread out

2 anisotropic diffusion
2. Anisotropic Diffusion

How a blob of ink would spread out

vector plot
Vector Plot

In-plane

Through-plane

direction of greatest diffusion
Direction of Greatest Diffusion

+

+

+

X-component

Y-component

Z-component

Anisotropy

Color (Hue) = Direction of highest diffusion

Brightness = Degree of anisotropy

=

diffusion tensor colour map
Diffusion Tensor – Colour Map

Left-Right

Anterior-Posterior

Superior-Inferior

diffusion tensor 3d colour map
Diffusion Tensor – 3D Colour Map

Left-Right

Anterior-Posterior

Superior-Inferior

which directions
Which Directions?

Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time

D.K. Jones, S.C.R. Williams, D. Gasston, M.A. Horsfield, A. Simmons, R. Howard

Human Brain Mapping 15, 216-230 (2002)

fiber tracking discrete case
Fiber Tracking – Discrete Case

Direction of

Greatest diffusion

fiber tracking discrete case1
Fiber Tracking – Discrete Case

Direction of

Greatest diffusion

fiber tracking continuous case
Fiber Tracking – Continuous Case

Direction of

Greatest diffusion

Mori et al, 1999

fiber tracking where to start
Fiber Tracking – Where to Start
  • Everywhere: Seed points distributed evenly throughout volume
fiber tracking where to start1
Fiber Tracking – Where to Start
  • Within a plane: All fibers within or crossing a selected plane are tracked
human neuroanatomy carpenter sutin 1981
“Human Neuroanatomy” Carpenter & Sutin 1981

Upper Extremity

Trunk

Lower Extremity

slide40
“Human Neuroanatomy” Carpenter & Sutin 1983

Upper Extremity

Trunk

Lower Extremity

fiber tracking cst3
Fiber Tracking - CST

Subject 1

Subject 2

Subject 3

Subject 4

crossing fibers
Cingulum

Feet movement

Tongue movement

Corpus

Callosum

Longitudinal Fasciculus

Corticospinal Tract

Crossing Fibers
dti tracking below slf
DTI – Tracking below SLF

Feet

Tongue

Fingers

Upper

Trunk

Lower

limitations of dti fiber tracking
Crossing Fibers

Kissing Fibers

Limitations of DTI/Fiber Tracking
  • Partial volume
    • A single voxel may contain fibers running in multiple directions – average anisotropy measured
    • Tensor may not be a good representation
    • Need to distinguish “kissing” and “crossing”
more pretty pictures
More Pretty Pictures…
  • Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time
    • D.K. Jones, S.C.R. Williams, D. Gasston, M.A. Horsfield, A. Simmons, R. Howard
    • Human Brain Mapping 15, 216-230 (2002)
conclusions the future
Conclusions, the Future
  • DTI provides the only non-invasive method to study organization white matter fibers. Previous studies have been limited to animal models and stroke patients
  • Current limitations on DTI and Fiber Tracking:
    • Partial volume effects
    • SNR
    • Acquisition time/physiological noise
  • Advances
    • High field, faster gradients, more efficient coils, motion detection/correction, new pulse sequences (eg. 3D, spiral…)
    • Higher SNR can be traded for smaller voxels, reducing partial volume effects
    • Beyond the tensor model… HARD imaging, q-space imaging
    • New tracking algorithms
references
References
  • High-resolution isotropic 3D diffusion tensor imaging of the human brain.
    • X. Golay, H. Jiang, P.C.M. van Zijl, S. Mori
    • Magn. Res. Med. 47, 837-843 (2002)
  • White matter mapping using diffusion tensor MRI
    • C.R. Tench, P.S. Morgan, M. Wilson, L.D. Blumhardt
    • Magn. Res. Med. 47, 967-972 (2002)
  • Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging
    • S. Mori, B.J. Creain, V.P. Chacko, P.C.M. van Zijl
    • Ann. Neurol. 45, 265-269 (1999)
  • Diffusion tensor imaging: Concepts and applications
    • D. Le Bihan et al
    • J. Magn. Res. Imaging 13, 534-546 (2001)
  • In vivo three dimensional reconstruction of rat brain axonal projections by diffusion tensor imaging
    • R. Xue, P.C.M. van Zijl, B.J. Cain, M. Solaiyappan, S.Mori
    • Magn. Res. Med. 42 1123-1127 (1999)
  • A direct demonstration of both structure and function in the visual system: combining diffusion tensor imaging with functional magnetic resonance imaging
    • D.J. Werring, C.A. Clark, G.J.M. Parker, D.H. Miller, A.J. Thompson, G.J. Barker
    • NeuroImage 9, 352-361 (1999)
  • Orientation-independent diffusion imaging without tensor diagonalization: anisotropy definitions based on the physical attributes of the diffusion ellipsoid
    • A.M. Ulug, P.C.M. van Zijl
    • J. Magn. Res. Imaging 9, 804-813 (1999)
references1
References
  • Imaging cortical association tracts in the human brain using diffusion-tensor based axonal tracking
    • S. Mori et al
    • Magn. Res. Med. 47, 215-223 (2002)
  • Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable time
    • D.K. Jones, S.C.R. Williams, D. Gasston, M.A. Horsfield, A. Simmons, R. Howard
    • Human Brain Mapping 15, 216-230 (2002)
  • Diffusion tensor imaging and axonal tracking in the human brainstem
    • B. Stietjes et al
    • NeuroImage 14 723-735 (2001)
  • Tracking neuronal fiber pathways in the living human brain
    • T.E. Conturo et al
    • Proc. Natl. Acad. Sci. 96 10422-10427 (1999)
  • The future for diffusion tensor imaging in neuropsychiatry
    • K.H. Taber et al
    • J. Neuropsychiatry Clin. Neurosci. 14 1-5 (2002)
  • Tensorlines: Advection-diffusion based propogation through diffusion tensor fields
    • D. Weinstein, G. Kindlmann, E. Lundberg
the diffusion tensor

where

g

Gx

The Diffusion Tensor

where

Identical if

how many measurements1
How Many Measurements?

7 degrees of freedom:

S0, Dxx, Dyy, Dzz, Dxy, Dxz, Dyz

Need at least 7 directions – but more is better!

30 slices x 32 directions = 960 images…

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