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Basics of Diffusion Tensor Imaging and DtiStudio

DTI Basics

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DTI reveals White matter anatomy

Gray matter

White matter

DTI uses water diffusion as a probe for white matter anatomy

Isotropic diffusion

Anisotropic diffusion

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3D axonal structures can be reconstructed based on DTI

What is Diffusion Imaging and How It Works

90˚

RF

90˚

RF

G

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Diffusion process: Concentration gradient

How can we measure diffusion without perturbing the system?

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Homogeneous magnetic field

O

H H

O

H

H

O

H H

B0 / 2

e.g. B0 = 9.4 T

= 400 MHz

400

Inhomogeneous magnetic field

O

H H O

H

B0 / 2

e.g. B0 = 9.4 T

= 400 MHz

H

O

H

H

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Direction of Gradient

X

Y

Z

Z-Gradient X-Gradient Y-Gradient

Strength of Gradient

X

Y

Z

Weak

X- Gradient Strong

X- Gradient

Strong

X- Gradient

Opposite polarity

+/-

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length

strength Gx

X

Y Z

A diagram to show the gradient application

how it affects spins

90˚

RF

B0 / 2

Dephasing

Rephasing G

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If spins move

Imperfect refocusing

=Signal loss!

Dephasing Rephasing

Signal loss!

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Parameters that affect the result

G

Small G Less signal loss

Large G More signal loss

D

Equation for the diffusion attenuation

G

b-value

Sig

nal

In

ten

sity

D

ln S S 0 2 G

2

2

3

D = - bD

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Diffusion measurement by imaging

90˚

RF

G

Imaging

b

DWI and ADC

1 G/cm 6 G/cm 10 G/cm 13 G/cm

b-value

Sig

na

l In

ten

sity

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Direction of the diffusion measurment

Only the diffusion along a gradient direction can be

measured

Apparent Diffusion Constant (ADC) Map with Different Measurement Direction

Y X Z

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Anisotropic diffusion

Free diffusion

Restricted diffusion

Isotropic diffusion

Anisotropic diffusion

Determination of diffusion ellipsoid

x

y

z

Measure diffusion along various directions (> 6)

Calculate shape of the ellipsoid

l1

l2

l3

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Raw data of DTI

Sx S0 Sz Sy

Sx+z Sx+y Sy+z

DtiStudio Interface and DTI Data Processing

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Initial data I/O window

Viewing screen

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Tensor calculation results

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DTI-derived contrasts Sx S0 Sz Sy Sx+z Sx+y Sy+z

FA map Orientation map

Relationships of DTI-derived maps

Average STD

Axial diffusivity Radial diffusivity Mean diffusivity Fractional anisotropy

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Simplifications and assumptions in DTI

Diffusion ellipsoid

Non-tensor analysis

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Artifact Detection and Correction

Subject motion

32 1 10 15 22

0

0.8

1.6

2.4

3.2

4

equi

vale

nt d

egre

e ar

ound

x a

xis

5 10 15 20 25 30

0

0.02

0.04

0.06

0.08

0.1

73088: rotationmetric

rota

tionm

etric

# of DWI

0

2

4

6

8

tran

sla

tion

sco

re

5 10 15 20 25 300

1

2

3

4

5

6

773088: translationmetric

tran

sla

tion

me

tric

: m

m

# of DWI

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Uncorrectable motion problem

6 15 28 11

Eddy current distortion

-0.1 -0.05 0 0.05 0.1-1

-0.5

0

0.5

1

ex vs Gx, R = 0.9993

ex

norm

aliz

ed

Gx

-0.1 -0.05 0 0.05 0.1-1

-0.5

0

0.5

1

ey vs Gy, R = 0.99532

ey

norm

aliz

ed

Gy

-0.1 -0.05 0 0.05 0.1-1

-0.5

0

0.5

1

ez vs Gz, R = -0.99576

ez

norm

aliz

ed

Gz

R = 0.9993 R = 0.9953 R =-0.9957

b c d

PE(

Y)

RO(X)

a

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Data corruption

50 100 150

50

100

150

0

1

2

3

50 100 150

50

100

150

0

1

2

3

50 100 150

50

100

150

0

0.5

1

1.5

2

a b c d

e f g h

i j k l

QC Reporting 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2

fitting error score

Histogram of normalized absolute fitting errors before registration

0 1 2 3 40

200

400

600

800

1000

1200

noise

a b

Scanner#1 Scanner#2 Scanner#3 Scanner#40

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

0.02

histogram eddyzmetrics

0 0.02 0.04 0.06 0.080

100

200

300

400

500

600

Rotation Metric

0 0.4 0.8 1.2 1.6 2 2.4 2.8

equivalent degree around x axis

Histogram rotationmetrics

50 100 150

50

100

150

0

0.5

1

1.5

2

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Fiber Tracking

Can axonal projections be reconstructed?

At each voxel, average fiber

orientation can be estimated

Axonal projection reconstruction

may be possible

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Examples of the tracking

Example of reconstruction

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Fiber selection process

#3

#3

#2

#1

#2

#1

Automated ROI definition and tracking

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Tensor vs Non-tensor

Deterministic vs Probabilistic

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Quantitative Data Analysis and MriStudio (DiffeoMap and RoiEditor)

Scalarization is needed for image analysis

Path

olo

gy/

Fun

ctio

ns/

Conversion to a number e.g.: FA, T2, size

size

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Location is the most difficult parameter to quantify

Without identification of corresponding locations, quantification is meaningless

Coverage and granularity of image quantification

Co

vera

ge

Granularity

1 1.5M

100%

Whole-brain analysis

ROI-based analysis

Atlas-based analysis

Voxel-based analysis

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Normalization of pathological brains original Normalized

LDDMM: Michael I. Miller

Voxel-based analysis

Normalized

FA, T

2

Control Patient

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Rationale of isotropic filtering

Rationale of anatomical filtering: Atlas-based analysis

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3D electronic brain atlas

Automated segmentation of pathological brains

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Automated and quantitative image analysis for population data

A B

A: Preproseccing

B: Linear transformation

C: LDDMM D: Transformation

E: Landmark LDDMM

DiffeoMap Interface

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Age-dependent

Multi-definition

0 M 12 M 24 M

Connectivity

Adult

Vascular territory Structural

segmentation Probabilistic

Choice of atlases

A: ROI management

B: Atlas Selection

C: ROI drawing assist

D: ROI list

RoiEditor Interface