Transcript Multiscale Hierarchical Segmentation

12
2
BM/i
Visualization of Diffusion Tensor Imaging
Guus Berenschot
May 2003
Supervisor: Bart ter Haar Romeny
Daily Supervisor: Anna Vilanova i Bartroli
Other committee members: Carola van Pul,
Klaas Nicolay and Peter Hilbers
/mhj
12
2
BM/i
Contents
• Introduction Diffusion Tensor Imaging
(DTI)
• Visualization Tool for DTI
• Demonstration
• Conclusion and Future Work
7/17/2015
2
/mhj
12
2
BM/i
Introduction Diffusion Tensor Imaging
• Diffusion is the random motion of molecules, and is
characterized by a diffusion coefficient D.
• In tissue this diffusion hindered by physical barriers.
• The diffusion coefficient is called Apparent Diffusion
Coefficient (ADC)
7/17/2015
3
/mhj
12
2
BM/i
Introduction Diffusion Tensor Imaging
• Diffusion Tensor Imaging is a Magnetic Resonance
Imaging (MRI) technique.
• DTI measures the ADC in 6 directions and computes a
symmetric diffusion tensor (D) of this:
 Dxx Dxy Dxz 


D   Dyx Dyy Dyz 


D D D 
 zx zy zz 
• This diffusion tensor is defined for each voxel in the
3D dataset
7/17/2015
4
/mhj
12
2
BM/i
Diagonalization Diffusion Tensor
• Diagonalization of this
tensor provides three
eigenvectors (ev1, ev2 and
ev3) with three
corresponding eigenvalues
(λ1, λ2 and λ3)
7/17/2015
5
ev1λ1
ev3λ3
ev2λ2
/mhj
12
2
BM/i
Anisotropy Indices
• Linear case:
1  2
Cl 
1  2  3
• Planar case:
2  3
Cp 
1  2  3
• Isotropic case:
33
Cs 
1  2  3
7/17/2015
6
/mhj
12
2
BM/i
Problem Definition
• How to extract meaningful information of a 3D DTI
dataset???
– Neonatal brain (Maxima Medical Center,
Veldhoven)
– Muscles (Magnetic Resonance Laboratory)
7/17/2015
7
/mhj
12
2
BM/i
Visualization Tool For DTI
•
•
•
•
7/17/2015
Anatomical reference
Displaying local tensor information
Displaying global tensor information
Improvements to existing techniques
8
/mhj
12
2
BM/i
Multi Planar Reconstruction Planes
• Anatomical reference
• Displaying local tensor information in 2D slice
7/17/2015
9
/mhj
12
2
BM/i
Anisotropy Indices
• Colorcoding anisotropy indices
7/17/2015
10
/mhj
12
2
BM/i
Colorcoding Main Diffusion Direction
• Colorcoding directions
• Intensity color scaled with anisotropy index
A
H
R
L
F
P
/mhj
Pajevic et al. 1997
7/17/2015
11
12
2
BM/i
Glyphing
• Glyphs are icons that represent the local tensor
information
• Two types of glyphs can be displayed:
– Ellipsoids
– Cuboids (Worth et al., 1998)
7/17/2015
12
/mhj
12
2
BM/i
Cuboids
7/17/2015
13
/mhj
12
2
BM/i
Fiber Tracking: Introduction
• Fiber tracking simplifies the tensor field to the
vector field of the main eigenvector
• This vector field is made continuous by
interpolation
• Consider this vector field as a velocity field and
drop a free particle on it
• This particle will follow a trajectory
• The found trajectory can be seen as a bundle of
fibers
Xue et al. 1999,
Conturo et al. 1999,
Mori et al. 1999
7/17/2015
14
/mhj
12
2
BM/i
Tracking
• The tracking can be seen as solving the
following integral:


x (t )   e1 (t )dt
t
• To solve this integral we use a second order
Runge Kutta integration
7/17/2015
15
/mhj
12
2
BM/i
Seed Points
• Manual definition of a seed point or seed region
• Start tracking in all voxels and keep the
trajectories that pass a certain region
Stopping Criteria
• Linear Anisotropy (Cl) falls below a certain
threshold
• Angle in a fiber is too big
7/17/2015
16
/mhj
12
2
BM/i
Fiber Tracking In Healthy Volunteer
Optical tract
7/17/2015
17
Corpus Callosum
/mhj
12
2
BM/i
Patient with a tumor
Neonatal brain
Mouse muscle
7/17/2015
18
/mhj
12
2
BM/i
Surface Building
• Fiber tracking gives problems in regions
with planar anisotropy; the main
eigenvector is not reliable
• Planar anisotropy can be due to kissing
crossing or branching fibers
7/17/2015
19
/mhj
12
2
BM/i
Surface Building
• If we enter a region with planar anisotropy: follow all
directions defined by local plane and display a surface
here
• If anisotropy is linear again: do the common fiber
tracking.
7/17/2015
20
/mhj
12
2
BM/i
Demonstration
7/17/2015
21
/mhj
12
2
BM/i
Conclusion
• The visualization tool is considered as very useful
by the MMC and the MRL
• Results of fiber tracking in neonatal brain is
promising
Future Work
• Seeding is biased
• Noisy data-> smoothing
• Quantitative information of fibers
7/17/2015
22
/mhj
12
2
BM/i
Thanks
•
•
•
•
•
Anna Vilanova i Bartroli (daily supervisor)
Bart ter Haar Romeny (supervisor)
Gustav Strijkers and Anneriet Heemskerk (MRL)
Carola van Pul and Maurice Jansen (MMC)
George Roos and Jan Buijs (radiologist and
neonatologist MMC)
• Klaas Nicolay and Peter Hilbers (committee
members)
7/17/2015
23
/mhj