mr diffusion tensor imaging, tractography richard watts, d.phil. citigroup biomedical imaging center...
TRANSCRIPT
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MR Diffusion Tensor Imaging, Tractography
Richard Watts, D.Phil.Citigroup Biomedical Imaging Center
Weill Medical College of Cornell UniversityBox 234, 1300 York Avenue, New York, NY 10021
Email [email protected], Telephone 212 746-5781
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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.
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Outline• Background
– Diffusion– Restricted Diffusion and Anisotropy
• Methods– Data Acquisition– Display of Diffusion Tensor Data– Fiber Tracking– Problems and Limitations
• Examples
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Diffusion
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Diffusion Equation
Dtr 62
r = Displacement (mm)D = Diffusion constant
(mm2/s)t = Time (mm)
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Distance ScalesQuestion: What distance do protons travel during an EPI readout time?
Assume: Diffusion constant ~ 10-3 mm2/sTime ~ 100 ms = 0.1s
1.0106
63
2
Dtr
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
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Diffusion Imaging of Leukemia
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Diffusion Imaging of Leukemia
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Spin Echo
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Spin Echo
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Spin Echo
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Data Acquisition – Spin Echo
TE
time
90º 180º
RF
Gx
Diffusion Gradients
Echo
bDeSS 0 where 3222 gb
g
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Restricted Diffusion
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Diffusion Ellipsoid in White Matter
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Anisotropy
Isotropic:Having the same properties in all directions
Anisotropic:Not isotropic; having different properties in different directions
Webster’s Dictionary
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Data Acquisition – Spin Echo
TE
time
90º 180º
RF
Gx
Gy
Gz
Linear combination of gradients - measure component of diffusion in any direction
Echo
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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
zzyzxz
yzyyxy
xzxyxx
DDD
DDD
DDD
D
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Calculated Quantities…
T2-Weighted Image “Average” Diffusion*
Diffusion along X Diffusion along Y Diffusion along Z
Degree of Anisotropy*
* Various definitions
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1. (Approximately) Isotropic Diffusion
How a blob of ink would spread out
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2. Anisotropic Diffusion
How a blob of ink would spread out
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Vector PlotIn-plane
Through-plane
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Direction of Greatest Diffusion
+++
=
Color (Hue) = Direction of highest diffusionBrightness = Degree of anisotropy
X-component Y-component Z-component Anisotropy
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Diffusion Tensor – Colour Map
Left-Right
Anterior-Posterior
Superior-Inferior
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DTI – Color Map
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Diffusion Tensor – 3D Colour Map
Left-Right
Anterior-Posterior
Superior-Inferior
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How Many Measurements?
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Which Directions?
Isotropic resolution diffusion tensor imaging with whole brain acquisition in a clinically acceptable timeD.K. Jones, S.C.R. Williams, D. Gasston, M.A. Horsfield, A. Simmons, R. HowardHuman Brain Mapping 15, 216-230 (2002)
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Fiber Tracking – Discrete Case
Direction ofGreatest diffusion
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Fiber Tracking – Discrete Case
Direction ofGreatest diffusion
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Fiber Tracking – Continuous Case
Direction ofGreatest diffusion
Mori et al, 1999
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Fiber Tracking – Where to Start
• Everywhere: Seed points distributed evenly throughout volume
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DTI Tractography
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Fiber Tracking – Where to Start
• Within a plane: All fibers within or crossing a selected plane are tracked
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Fiber Tracking – Corpus Callosum
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Fiber Tracking – Corpus Callosum
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Fiber Tracking – Where to Start
• Within a small volume
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Fiber Tracking - CST
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“Human Neuroanatomy” Carpenter & Sutin 1981
Upper Extremity
Trunk
Lower Extremity
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“Human Neuroanatomy” Carpenter & Sutin 1983
Upper ExtremityTrunk
Lower Extremity
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Fiber Tracking - CST
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Fiber Tracking - CST
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Combining DTI and fMRI
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fMRI – Feet Movement
Feet
-4
-3
-2
-1
0
1
2
3
4
1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106
Acquisition Number
Sig
nal
Ch
ang
e (%
)
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fMRI – Finger Tapping
Fingers
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106
Acquisition Number
Sig
nal
Ch
ang
e (%
)
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fMRI – Tongue Movement
Tongue
0
1
2
3
4
5
6
1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106
Acquisition Number
Sig
nal
Ch
ang
e (%
)
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Results – fMRI – Feet, Fingers, Tongue
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“Images of Mind”, Posner and Raichie, 1999
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Fiber Tracking - CSTSubject 1 Subject 2 Subject 3 Subject 4
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Crossing Fibers
Tongue movementFeet movement
Corticospinal Tract
Longitudinal Fasciculus
Cingulum
CorpusCallosum
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DTI – Tracking below SLF
TongueFeetFingers
Lower
TrunkUpper
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DTI Tractography – Clinical Example
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DTI Tractography – Clinical Example
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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”
Crossing Fibers
Kissing Fibers
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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)
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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
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DTI – Tracking below SLF
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DTI – Tracking below SLF
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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)
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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
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The Diffusion Tensor
gDgb T
eSS 0
0 where 3
220
b
gGx
bDeSS 0 where 3222 gb
Identical if
D
D
D
DDD
DDD
DDD
D
zzyzxz
yzyyxy
xzxyxx
00
00
00
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How Many Measurements?
gDgb T
eSS 0
0
zzyzxz
yzyyxy
xzxyxx
DDD
DDD
DDD
D
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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310
649.0074.0064.0
074.0709.0075.0
064.0075.0638.0
D
Corresponding Tensor
mm2/s
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Eigenvalues and Eigenvectors of the Diffusion Tensor
31 10814.0
509.0
709.0
488.0
1v
32 10603.0
615.0
696.0
371.0
2v
33 10578.0
603.0
111.0
790.0
3v
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310
606.0085.0022.0
085.0620.1775.0
022.0775.0115.1
D
Corresponding Tensor
mm2/s
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Eigenvalues and Eigenvectors of the Diffusion Tensor
31 10185.2
036.0
809.0
586.0
1v
32 10651.0
823.0
309.0
476.0
2v
33 10506.0
566.0
500.0
656.0
3v