temporal-spectral imaging of functional states randall l. barbour nirx medical technologies llc suny...

Temporal-Spectral Imaging of Functional States

Temporal-Spectral Imaging of Functional States

Randall L. BarbourNIRx Medical Technologies LLC

SUNY Downstate Medical Center

4th NIH Optical Imaging Workshop 2004

9/22/2004 R.L. Barbour / NIH Optical Imaging Workshop

Motivation

• Dynamic Optical Tomography– Natural vascular rhythms

• Arteries ~1 Hz

• Veins ~ 0.3 Hz

• Microvessels ~0.02 – 0.15 Hz

– Varying metabolic demand influences tissue vascular coupling• Response to provocation

• Influence of disease

• Effects of drugs

• Time series of images – Multiple features

– High intrinsic contrast

– No need for injection


Motivation

• Dynamic Optical Tomography– Tumor detection / monitoring response to therapy

– Diabetes (PVD)

– Functional brain imaging

– Small animal/pharmacological agents

• Value of intrinsic signals

• Instrumentation

• Analysis tools


Vascular Dynamic States

• Non-propagating– Time varying local change (e.g., arterial HbO2)

– Pulsating behavior

• Propagating– Mayer Waves

– Maneuver induced blood volume changes


1999 2000 2001 2002 2003 2004

1st NIH Workshop OI: Dynamic OT1st NIH Workshop OI: Dynamic OT

si

di+c

di+j

si+j+c11si+j+c11

mc

mj

Symmetry based calibrationSymmetry based calibration

Simultaneous dual-breast scanSimultaneous dual-breast scan

DYNOT at Photonic West

DYNOT at Photonic West 1st commercial DYNOT1st commercial DYNOT

DYNOTcompactDYNOTcompact

Evolving Technology – Milestones

1988/89: First description of Diffuse Optical Tomography1988/89: First description of Diffuse Optical Tomography

1995: First description of Diffuse Fluorescence Tomography1995: First description of Diffuse Fluorescence Tomography

NDM Robust Solutions

0

0R

I II x

I

W


Integrated Imaging Platform

System setup check

1 2

Real time display of raw readings


Simultaneous Dynamic Dual Breast Measurements

• Healthy Breast

– Coherent response to provocation

• Tumor Breast

– Sluggish perfusion– Reduced response to

natural effectors– Elevated Hb levels


Dual Breast Measurement Head


Simultaneous Dual Breast Measurement

1 2


Dual Breast Imaging Result

-4.0E-05

-2.0E-05

0.0E+00

2.0E-05

4.0E-05

6.0E-05

8.0E-05

1.0E-04

1.2E-04

1.4E-04

2500 2550 2600 2650 2700 2750 2800 2850 2900 2950 3000

Imaging Frames

Estim

ate

d

D Hb

red

[m

ol/l]

1 2 3 4 5 6 7

1.5e-8

0

-9.3e-9

2.1e-8

0

-1.2e-8

Left

(tum

or)

1 2 3 4 5 6 7Rig

ht (

heal

thy)

D H

bred

[m

ol/l]

Imaging frames


Valsalva Maneuver

• Spatially averaged HbO2 response

Seconds0 1500

Left Breast

Right Breast

1-

0-

1-

0-


Temporal Features for Valsalva

PeakHeight

MaximalNegative Slope

MaximalPositive Slope

Begin End


Scatter Plot Analysis – Case 1

Right breast (healthy)Left breast (healthy)

HbR HbO2



Right breast (cyst)Left breast (healthy)

HbR HbO2



Right breast (healthy)Left breast (cystectomy)

HbR HbO2



HbR HbO2

Right breast (tumor)Left breast (healthy)


Information Content of Dynamic Behavior

• Amplitude changes

• Time delay, rate of change, …

• Directional features– Propagating behaviors, responses

• Mayer waves


Tensor Imaging

Time Frame n

(small subregion of image area)

Spatial distribution of imaged property has moved between times n and n+1

Estimate magnitude of displacement displacement velocity

Find distances Δx and Δy for which f2(x+ Δx,y+ Δy) and f1(x,y) are most similar

Time Frame n+1

Net displacement vector

f1(x,y) f2(x,y)


Object Size

time


Feature Propagation

time


Velocity Vector Fields


Healthy Subject

2

4

6

30

210

60

240

90

270

120

300

150

330

180 0

Compass function

0.5

1

1.5

30

210

60

240

90

270

120

300

150

330

180 0

Compass function

0.5

1

1.5

2

30

210

60

240

90

270

120

300

150

330

180 0

Compass function

10

20

30

40

30

210

60

240

90

270

120

300

150

330

180 0

Compass function


Diabetic Subject

2

4

6

8

30

210

60

240

90

270

120

300

150

330

180 0

Compass function

5

10

15

20

30

210

60

240

90

270

120

300

150

330

180 0

Compass function

5

10

15

20

30

210

60

240

90

270

120

300

150

330

180 0

Compass function

1

2

3

4

5

30

210

60

240

90

270

120

300

150

330

180 0

Compass function 5

10

15

30

210

60

240

90

270

120

300

150

330

180 0

Compass function


Things of Interest

• Temporal organization of vector fields

• Trajectories

• Duration

• Response to provocation, etc…


Quantitative Performance of 1st Order NDM

● NDM – 1th Order Solution:

FEM Model

Spatial Correlation: 0.2456

Temporal Correlation: 0.9946

Reconstructed Images

Targets

X-Y Plane X-Z Plane Y-Z Plane


Image Enhancement by Linear Deconvolution

ImageMedium

Reconstruction Filter

Medium Image

Reconstruction

(a) (b)

1,

m

a D 2

,m

a D

,m

a ND

1,

r

a D 2

,r

a D

,r

a ND

1,

m

a D 2

,m

a D

,m

a ND

1,

r

a D 2

,r

a D

,r

a ND


T( ) ( ) ( ) ( )0 01 02 0, , , 1, 2, , i i i i

nx x x i NX r

0.0 0.1 0.2 0.3 0.4 0.5

t

-1.0

-0.5

0.0

0.5

1.0

2[ a

(t) -

a(

0)]/[

(max

- m

in)(

a)] ·

· ·

·

·

·

· ·

·

· ·

·

·

· ·

·

·

·

·

·

· ·

·

· ·

· ·

·

· ·

· · ·

· ·

·

·

·

·

·

12

34

5, ...

Step1: Assign N independently known optical coefficient distributions

where n is the number of mesh nodes.

Temporal Encoding of Spatial Information

Rel

ativ

e A

mpl

itude


Generation of Deconvolution Filter (2)

T( ) ( ) ( ) ( )1 2, , , 1, 2, , i i i i

r r r rnx x x i NX r

(1) (2) ( ) (1) (2) ( )11 12 101 01 01 1 1 1

(1) (2) ( ) (1) (2) ( )21 22 202 02 02 2 2 2

(1) (2) ( ) (1) (2) ( )1 20 0 0

N Nn r r r

N Nn r r r

N Nn n nnn n n rn rn rn

f f fx x x x x x

f f fx x x x x x

f f fx x x x x x

Step2: Simulate the detector readings and reconstruct the optical coefficient distributions using NDM:

Step3: Compute deconvolution filter :ijF f

Known Distribution Reconstructed Distribution


Temporal-Spectral Imaging

Temporal Function Target X-Y Plane X-Z Plane Y-Z Plane

1st Order NDM Without Deconvol.

1st Order NDM With Deconvol.


Image enhancement with noise

t=0 t=T/4 t=T/2 t=3T/4 t=T

X-Y Plane

X-Z Plane

X-Y Plane

X-Z Plane

X-Y Plane

X-Z Plane

(a) Before deconvolution and low-pass filter

(b) After deconvolution and before low-pass filter

(c) After deconvolution and low-pass filter

Nosie level 2: 1%--10%

Sinusoidal time series:

T=10 s

● Images with noise:


Summary

• Integrated, scalable technology• Comprehensive software tools

– System control, calibration– Data integrity– Image recovery, analysis, display

• Simultaneous multi-site monitoring• Fast, stable reconstructions• Exploration of varying forms of dynamic behavior

– Amplitude, rate imaging– Tensor Imaging


Finding … ?

…..Nemo!

temporal-spectral imaging of functional states randall l. barbour nirx medical technologies llc suny...

Documents

right breast cyst

rate of change

local change

f2x x

times n

responsesmayer wavesr