1
6. SUMMARY & CONCLUSIONS 6. SUMMARY & CONCLUSIONS A correlation-based optical flow (OF) method was evaluated on RT3D ultrasound for tracking of the LV endocardial surface defined at ED via manual tracing or segmentation with the QLAB© software from Philips Medical System. Quantitative evaluation showed promising ability of OF tracking to follow the endocardial surface on open-chest and clinical data sets. Combined with epicardium tracking and radial basis function interpolation, dense-field myocardial motion was recovered, and analyzed for dynamic cardiac information. This study provided encouraging results regarding OF performance to accurately track LV surfaces, yield dynamic information from RT3D ultrasound, and provide automated dynamic interpolation of segmented endocardial surfaces. As a semi-automated method, OF based surface tracking can minimize tedious human intervention for ventricular motion analysis. 5. COMPUTATIONAL TIME 5. COMPUTATIONAL TIME 1 Columbia University, New York, NY, U.S.A. 2 Ecole Nationale Supérieure des Télécommunications, Département Traitement du Signal et des Images (TSI), Paris, France 3 Philips France, Suresnes, France Tracking of LV Endocardial Surface on Real Tracking of LV Endocardial Surface on Real - - Time Three Time Three - - Dimensional Dimensional Ultrasound with Optical Flow Ultrasound with Optical Flow 1 Qi Duan, 2 Elsa Angelini, 1 Susan Herz, 3 Olivier Gerard, 3 Pascal Allain, 1 Christopher Ingrassia, 1 Kevin Costa, 1 Jeffrey Holmes, 1 Shunichi Homma, 1 Andrew Laine 1. INTRODUCTION 1. INTRODUCTION Real-time three-dimensional (RT3D) ultrasound with matrix-phased array transducers [1] enable fast, non-invasive visualization of cardiac ventricles. Segmentation of ventricular volumes on RT3D ultrasound is typically performed at end-diastole (ED) and end-systole (ES). Automation of the segmentation process and propagation of segmentation in time is challenging. In this context, a correlation-based 3D+Time optical flow algorithm was applied for tracking of myocardial surfaces. Initialization was performed with either manual tracing or QLAB© software (Philips Medical Systems). 4. DYNAMIC CARDIAC INFORMATION 4. DYNAMIC CARDIAC INFORMATION 4.1 Epicardium Tracking Result Epicardium Epicardium OF tracking initialized with manual tracing was tested on the clinical data. Error measurements were much lower than for the endocardial surface. At ES, a Hammer map error analysis showed that 97% of the epicardial surface tracked with OF resulted in less than 10% differences from the manually traced surface. 2. METHODOLOGY 2. METHODOLOGY 2.1 Correlation-Based Optical Flow A region-based optical flow method using correlation measures [2] was applied to RT3D ultrasound data. Correlation measures are robust to noisy conditions and can adapt to fast motion. A displacement vector for two consecutive time frames was computed via maximization of the cross-correlation coefficient: Two threshold parameters were introduced: A variance threshold to limit the “aperture problem”. An average threshold to limit the tracking to the myocardium (“bright” regions). Regularization of the displacement field was performed via local averaging on a 6-connected neighborhood. 2.2 Real-Time Three-Dimensional Ultrasound Data Sets Tracking of the myocardial surfaces was tested on 3 data sets acquired with a SONOS 7500 3D ultrasound machine (Philips Medical Systems, Best, The Netherlands): Two data sets on an anesthetized open chest dog before (baseline) and 2 minutes after induction of ischemia via occlusion of the proximal left anterior descending (LAD) coronary artery. One transthoracic clinical data set from a heart-transplanted patient. Data acquisition was triggered with EKG and initialized at ED. Data set sizes: [176 160 144] with 16 frames for 1 cardiac cycle (dog- baseline), [176 160 144] with 16 frames for 1 cardiac cycle (dog-post ischemia), [224 208 201] with 16 frames for 1 cardiac cycle (patient). ( ) 2 2 (,) ( , ) (,) ( , ) I t I t t r I t I t t ∈Ω ∈Ω ∈Ω + + = + + x x x x x x x x x Dog Clinical 2.3 Segmentation Manual Tracing: An expert performed manual tracing of all time frames in the data sets, on rotating B-scan views and translating C-scan views. QLAB© Segmentation: The QLAB© software (Philips Medical Systems) was used to segment the endocardial surface. Initialization was performed by a human expert and a parametric deformable model was fit to the data at each time frame. Segmented surfaces were reviewed by expert and manually adjusted for final correction. 2. METHODOLOGY (cont.) 2. METHODOLOGY (cont.) 2.4 Surface Tracking with Optical Flow All data sets were pre-smoothed with anisotropic diffusion. An initial set of endocardial surface points was defined (from manual tracing for dog & clinical data; also from QLAB© for the clinical data). Optical flow (OF) algorithm was used to track the surface in time through the whole cardiac cycle. 2.5 Quantitative Comparison of Surfaces Surface reconstruction is based on finite element model (FEM) [3]. Tracked surfaces were registered with anatomical landmarks and fitted into 64-element meshes in prolate spheroidal coordinates (λ,θ,φ) with bicubic Hermite interpolation [4]. Quantitative measurements: Root mean squared errors (RMSE) of the difference of the radial coordinates λ at node points for OF and manual tracing surfaces. Ventricular volumes. Relative errors were used to generate local relative error maps plotted as Hammer maps. 0 0 0 .1 0 0 0 - 0.1 0 0 -0.1 septum anterior lateral posterior septum apex -1.5 -1 -0.5 0 0.5 4.2 Myocardium Motion Field and Dynamic Cardiac Information Computation of a displacement vector field between ED and ES on the endocardial and epicardial surfaces by OF tracking. Interpolation of myocardial motion with radial basis functions. Extraction of dynamic cardiac information [5]: radial displacement, thickening and twist. Motion Field One Slice 3D Rendering Radial Displacement Thickening Twist ACKNOWLEDGEMENTS ACKNOWLEDGEMENTS This work was funded by National Science Foundation grant BES-02-01617, American Heart Association #0151250T, Philips Medical Systems, New York State NYSTAR/CAT Technology Program, and the Louis Morin Fellowship program. Dr. Andrew McCulloch at the University of California San Diego provided the finite element software “Continuity” through the National Biomedical Computation Resource (NIH P41RR08605). The authors also would like to thank Todd Pulerwitz, M.D. (Department of Medicine, Columbia University) for his efforts in acquiring the ultrasound data sets. 10 25 16 1 Optical Flow 160 160 70 10 Human Expert Human Involvement (minutes) Total time for full cycle (minutes) Total time for ejection phase (minutes) Single frame (minutes) Testing Environment: “C” code on a 2.4GHz AMD 64-bit server, running Redhat Linux 3. RESULTS 3. RESULTS 3.1 Open-Chest Dog Data - 0 . 1 0 . 1 0.1 0. 1 0 0.2 0.2 0.1 0 - 0 . 2 0 0 0.2 -0.2 - 0 . 2 0.1 septum anterior lateral posterior septum apex -1.5 -1 -0.5 0 0.5 -2 - 2 0 .1 0 0 -0 . 5 -0.5 -0.2 0 0.2 0.1 - 0 .2 - 0. 2 0. 1 0 . 2 0 .2 0 . 1 0 -0.5 0 . 2 septum anterior lateral posterior septum apex -1.5 -1 -0.5 0 0.5 Lat. Ant. Lat. Ant. RMSE Max. error = Baseline: 0.19 (frame 11) Ischemia:0.08 (frame 12) (λ=0.7±0.3 at ED λ=0.6±0.2 at ES) Max. error = 5ml LV Volume (post ischemia) LV Volume (baseline) Max error = 7ml 0 5 10 15 0 20 40 60 80 Frame Number Volume (ml) Manual OF 0 5 10 15 0 0.1 0.2 0.3 0.4 0.5 Frame Number RMSE Baseline Ischemia 0 5 10 15 0 20 40 60 80 Frame Number Volume (ml) Manual OF 3.2 Clinical Data Experiments: Forward and backward tracking and re-initialization using manual tracing and QLAB© segmentation. 0 2 4 6 0 0.1 0.2 0.3 0.4 0.5 Frame Number RMSE Forward Backward RMSE 1 2 3 4 5 0 0.1 0.2 0.3 0.4 0.5 Frame Number RMSE No re-initialization Re-initialization every 4 frames Re-initialization every other frame Combining forward and backward tracking RMSE Using Manual tracing Using QLAB© Max. error = Manual: 0.09 (frame 09) QLAB©:0.08 (frame 10) (λ=0.6±0.3 at ED λ=0.5±0.2 at ES) Max. error = 10ml 0 5 10 15 0 0.1 0.2 0.3 0.4 0.5 Frame Number RMSE Manual QLAB 0 5 10 15 0 20 40 60 80 Frame Number Volume (ml) Manual OF 0 - 0 . 1 0. 1 0 0 0 .1 0 . 2 0. 1 0.2 0 - 0 . 2 - 0 . 5 0 . 2 0 .1 septum anterior lateral posterior septum apex -1 0 0. 1 0. 1 0 0 -0.5 -1 - 0 . 2 - 0 . 5 - 0 . 2 0 0 . 1 0. 2 - 0.2 - 0.1 0 septum anterior lateral posterior septum apex -1.5 -1 -0.5 0 0.5 λ diff. Lat. Ant. Lat. Ant. RMSE & Volume Hammer maps RMSE & Volume Hammer maps 0 5 10 15 0 20 40 60 80 Frame Number Volume (ml) RMSE & Volume λ diff. OF vs. manual tracing OF vs. QLAB© segmentation ES ES λ diff. λ diff. OF vs. manual tracing at baseline OF vs. manual tracing at post ischemia ES ES Max. error = 13ml QLAB OF REFERENCES REFERENCES [1] O. T. von Ramm, et al, “Real Time Volumetric Ultrasound Imaging Systems,” Journal of Digital Imaging, vol. 3, pp. 261-266, 1990. [2] J. L. Barron, et al, “Performance of Optical Flow Techniques,” Int. J of Computer Vision, vol. 12, pp.43-77, 1994. [3] S. Herz, et al, “Novel Technique for Quantitative Wall Motion Analysis Using Real-Time Three-Dimensional Echocardiography,” Proc. of ASE, 2004. [4] C. M. Ingrassia, et al, “Impact of Ischemic Region Size on Regional Wall Motion,” Proc. Of BMES, 2003. [5] M. Suhling, et al, “Myocardial Motion Analysis From B-Mode Echocardiograms,” IEEE Trans. Image Processing, vol.14, pp.525-536, 2005. -10 -8 -6 -4 -2 0 2 4 6 8 10 -3 -2 -1 0 1 2 3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

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Page 1: Tracking of LV Endocardial Surface on Real

6. S

UM

MA

RY

& C

ON

CLU

SIO

NS

6. S

UM

MA

RY

& C

ON

CLU

SIO

NS

A c

orre

latio

n-ba

sed

optic

al fl

ow (O

F) m

etho

d w

as e

valu

ated

on

RT3

D

ultra

soun

d fo

r tra

ckin

g of

the

LV e

ndoc

ardi

al s

urfa

ce d

efin

ed a

tED

via

m

anua

l tra

cing

or s

egm

enta

tion

with

the

QLA

softw

are

from

Phi

lips

Med

ical

Sys

tem

.

Qua

ntita

tive

eval

uatio

n sh

owed

pro

mis

ing

abili

ty o

f OF

track

ing

to fo

llow

th

e en

doca

rdia

l sur

face

on

open

-che

st a

nd c

linic

al d

ata

sets

.

Com

bine

d w

ith e

pica

rdiu

m tr

acki

ng a

nd ra

dial

bas

is fu

nctio

n in

terp

olat

ion,

den

se-fi

eld

myo

card

ial m

otio

n w

as re

cove

red,

and

an

alyz

ed fo

r dyn

amic

car

diac

info

rmat

ion.

This

stu

dy p

rovi

ded

enco

urag

ing

resu

lts re

gard

ing

OF

perfo

rman

ceto

ac

cura

tely

trac

k LV

sur

face

s, y

ield

dyn

amic

info

rmat

ion

from

RT3

D

ultra

soun

d, a

nd p

rovi

de a

utom

ated

dyn

amic

inte

rpol

atio

n of

seg

men

ted

endo

card

ial s

urfa

ces.

As

a se

mi-a

utom

ated

met

hod,

OF

base

d su

rface

trac

king

can

min

imiz

e te

diou

s hu

man

inte

rven

tion

for v

entri

cula

r mot

ion

anal

ysis

.

5. C

OM

PUTA

TIO

NA

L TI

ME

5. C

OM

PUTA

TIO

NA

L TI

ME

1 C

olum

bia

Uni

vers

ity, N

ew Y

ork,

NY

, U.S

.A.

2 E

cole

Nat

iona

le S

upér

ieur

e de

s Té

léco

mm

unic

atio

ns, D

épar

tem

ent T

raite

men

t du

Sig

nal e

t des

Imag

es (T

SI),

Par

is, F

ranc

e3 P

hilip

s Fr

ance

, Sur

esne

s, F

ranc

e

Trac

king

of L

V En

doca

rdia

l Sur

face

on

Rea

lTr

acki

ng o

f LV

Endo

card

ial S

urfa

ce o

n R

eal -- T

ime

Thre

eTi

me

Thre

e --D

imen

sion

al

Dim

ensi

onal

U

ltras

ound

with

Opt

ical

Flo

wU

ltras

ound

with

Opt

ical

Flo

w1 Q

i Dua

n, 2 E

lsa

Ang

elin

i, 1 S

usan

Her

z, 3 O

livie

r Ger

ard,

3 Pas

cal A

llain

, 1C

hris

toph

er In

gras

sia,

1 K

evin

Cos

ta,1

Jeffr

ey H

olm

es,1

Shun

ichi

Hom

ma,

1 And

rew

Lai

ne

1. IN

TRO

DU

CTI

ON

1. IN

TRO

DU

CTI

ON

Rea

l-tim

e th

ree-

dim

ensi

onal

(RT3

D) u

ltras

ound

with

mat

rix-p

hase

d ar

ray

trans

duce

rs [1

] ena

ble

fast

, non

-inva

sive

vis

ualiz

atio

n of

car

diac

ven

tricl

es.

Seg

men

tatio

n of

ven

tricu

lar v

olum

es o

n R

T3D

ultr

asou

nd is

typi

cally

per

form

ed a

t en

d-di

asto

le (E

D) a

nd e

nd-s

ysto

le (E

S).

Aut

omat

ion

of th

e se

gmen

tatio

n pr

oces

s an

d pr

opag

atio

n of

seg

men

tatio

n in

tim

e is

cha

lleng

ing.

In th

is c

onte

xt, a

cor

rela

tion-

base

d 3D

+Tim

e op

tical

flow

alg

orith

m w

as a

pplie

d fo

r tra

ckin

g of

myo

card

ial s

urfa

ces.

Initi

aliz

atio

n w

as p

erfo

rmed

with

eith

er m

anua

l tra

cing

or Q

LAB

©so

ftwar

e (P

hilip

s M

edic

al S

yste

ms)

.

4. D

YNA

MIC

CA

RD

IAC

INFO

RM

ATI

ON

4. D

YNA

MIC

CA

RD

IAC

INFO

RM

ATI

ON

4.1

Epic

ardi

um T

rack

ing

Res

ult

Epi

card

ium

Epi

card

ium

OF

track

ing

initi

aliz

ed w

ith

man

ual t

raci

ng w

as te

sted

on

the

clin

ical

dat

a.

Err

or m

easu

rem

ents

wer

e m

uch

low

er

than

for t

he e

ndoc

ardi

al s

urfa

ce.

At E

S, a

Ham

mer

map

err

or a

naly

sis

show

ed th

at 9

7% o

f the

epi

card

ial

surfa

ce tr

acke

d w

ith O

F re

sulte

d in

le

ss th

an 1

0% d

iffer

ence

s fro

m th

e m

anua

lly tr

aced

sur

face

.2.

MET

HO

DO

LOG

Y2.

MET

HO

DO

LOG

Y2.

1 C

orre

latio

n-B

ased

Opt

ical

Flo

w

A re

gion

-bas

ed o

ptic

al fl

ow m

etho

d us

ing

corr

elat

ion

mea

sure

s [2

] w

as a

pplie

d to

RT3

D u

ltras

ound

dat

a. C

orre

latio

n m

easu

res

are

robu

st to

noi

sy c

ondi

tions

and

can

ada

pt to

fast

mot

ion.

A d

ispl

acem

ent v

ecto

r for

two

cons

ecut

ive

time

fram

es w

as

com

pute

d vi

a m

axim

izat

ion

of th

e cr

oss-

corr

elat

ion

coef

ficie

nt:

Two

thre

shol

d pa

ram

eter

s w

ere

intro

duce

d:

A v

aria

nce

thre

shol

d to

lim

it th

e “a

pertu

re p

robl

em”.

An

aver

age

thre

shol

d to

lim

it th

e tra

ckin

g to

the

myo

card

ium

(“

brig

ht”r

egio

ns).

Reg

ular

izat

ion

of th

e di

spla

cem

ent f

ield

was

per

form

ed v

ia lo

cal

aver

agin

g on

a 6

-con

nect

ed n

eigh

borh

ood.

2.2

Rea

l-Tim

e Th

ree-

Dim

ensi

onal

Ultr

asou

nd D

ata

Sets

Trac

king

of t

he m

yoca

rdia

l sur

face

s w

as te

sted

on

3 da

ta s

ets

acqu

ired

with

a S

ON

OS

750

0 3D

ultr

asou

nd m

achi

ne (P

hilip

s M

edic

al S

yste

ms,

Bes

t, Th

e N

ethe

rland

s):

Two

data

set

s on

an

anes

thet

ized

ope

n ch

est d

ogbe

fore

(b

asel

ine)

and

2 m

inut

es a

fter i

nduc

tion

of is

chem

ia v

ia o

cclu

sion

of

the

prox

imal

left

ante

rior d

esce

ndin

g (L

AD

) cor

onar

y ar

tery

.O

ne tr

anst

hora

cic

clin

ical

dat

a se

t fro

m a

hea

rt-tr

ansp

lant

ed

patie

nt.

Dat

a ac

quis

ition

was

trig

gere

d w

ith E

KG

and

initi

aliz

ed a

t ED

.

Dat

a se

t siz

es: [

176

160

144]

with

16

fram

es fo

r 1 c

ardi

ac c

ycle

(dog

-ba

selin

e), [

176

160

144]

with

16

fram

es fo

r 1 c

ardi

ac c

ycle

(dog

-pos

t is

chem

ia),

[224

208

201

] with

16

fram

es fo

r 1 c

ardi

ac c

ycle

(pat

ient

).

()

22

(,)(

,)

(,)

(,

)

ItI

tt

rI

tI

tt

∈Ω ∈Ω∈Ω⋅+∆

+∆

=+∆

+∆

∑ ∑∑

x xx

xx

x

xx

x

Dog

Clin

ical

2.3

Segm

enta

tion

Man

ual T

raci

ng: A

n ex

pert

perfo

rmed

man

ual t

raci

ng o

f all

time

fram

es in

the

data

set

s, o

n ro

tatin

g B

-sca

n vi

ews

and

trans

latin

g

C

-sca

n vi

ews.

QLA

Segm

enta

tion:

The

QLA

softw

are

(Phi

lips

Med

ical

S

yste

ms)

was

use

d to

seg

men

t the

end

ocar

dial

sur

face

. Ini

tializ

atio

n w

as p

erfo

rmed

by

a hu

man

exp

ert a

nd a

par

amet

ric d

efor

mab

le

mod

el w

as fi

t to

the

data

at e

ach

time

fram

e. S

egm

ente

d su

rface

sw

ere

revi

ewed

by

expe

rt an

d m

anua

lly a

djus

ted

for f

inal

cor

rect

ion.

2. M

ETH

OD

OLO

GY

(con

t.)2.

MET

HO

DO

LOG

Y (c

ont.)

2.4

Surf

ace

Trac

king

with

Opt

ical

Flo

w

All

data

set

s w

ere

pre-

smoo

thed

with

ani

sotro

pic

diffu

sion

.

An

initi

al s

et o

f end

ocar

dial

sur

face

poi

nts

was

def

ined

(fro

m m

anua

l tra

cing

for d

og &

clin

ical

dat

a; a

lso

from

QLA

for t

he c

linic

al d

ata)

.

Opt

ical

flow

(OF)

alg

orith

m w

as u

sed

to tr

ack

the

surfa

ce in

tim

e th

roug

h th

ew

hole

car

diac

cyc

le.

2.5

Qua

ntita

tive

Com

paris

on o

f Sur

face

s

Sur

face

reco

nstru

ctio

n is

bas

ed o

n fin

ite e

lem

ent m

odel

(FE

M) [

3].

Trac

ked

surfa

ces

wer

e re

gist

ered

with

ana

tom

ical

land

mar

ks a

nd

fitte

d in

to 6

4-el

emen

t mes

hes

in p

rola

te s

pher

oida

l coo

rdin

ates

,θ,φ

) with

bic

ubic

Her

mite

inte

rpol

atio

n [4

].

Qua

ntita

tive

mea

sure

men

ts:

Roo

t mea

n sq

uare

d er

rors

(RM

SE

) of t

he d

iffer

ence

of t

he ra

dial

co

ordi

nate

s λ

at n

ode

poin

ts fo

r OF

and

man

ual t

raci

ng s

urfa

ces.

Ven

tricu

lar v

olum

es.

Rel

ativ

e er

rors

wer

e us

ed to

gen

erat

e lo

cal r

elat

ive

erro

r map

s pl

otte

d as

Ham

mer

map

s.

0 0

0.1

0

00

-0.1

0

0 -0.1sept

uman

terio

rla

tera

lpo

ster

ior

sept

um

apex

-1.5

-1-0.5

00.5

4.2

Myo

card

ium

Mot

ion

Fiel

d an

d D

ynam

ic C

ardi

ac In

form

atio

n

Com

puta

tion

of a

dis

plac

emen

t vec

tor f

ield

bet

wee

n E

D a

nd E

S o

n th

e en

doca

rdia

l and

epi

card

ial s

urfa

ces

by

OF

track

ing.

Inte

rpol

atio

n of

myo

card

ial m

otio

n w

ith ra

dial

bas

is fu

nctio

ns.

Ext

ract

ion

of d

ynam

ic c

ardi

ac in

form

atio

n [5

]: ra

dial

dis

plac

emen

t, th

icke

ning

and

twis

t.M

otio

n Fi

eld

One

Slic

e

3D

Ren

derin

g

Rad

ial D

ispl

acem

ent

Thic

keni

ngTw

ist

AC

KN

OW

LED

GEM

ENTS

AC

KN

OW

LED

GEM

ENTS

This

wor

k w

as fu

nded

by

Nat

iona

l Sci

ence

Fou

ndat

ion

gran

t BE

S-0

2-01

617,

Am

eric

an H

eart

Ass

ocia

tion

#015

1250

T, P

hilip

s M

edic

al S

yste

ms,

New

Yo

rk S

tate

NYS

TAR

/CA

T Te

chno

logy

Pro

gram

, and

the

Loui

s M

orin

Fel

low

ship

pro

gram

. Dr.

And

rew

McC

ullo

ch a

t the

Uni

vers

ity o

f Cal

iforn

ia S

an D

iego

pr

ovid

ed th

e fin

ite e

lem

ent s

oftw

are

“Con

tinui

ty”t

hrou

gh th

e N

atio

nal B

iom

edic

al C

ompu

tatio

n R

esou

rce

(NIH

P41

RR

0860

5).

Th

e au

thor

s al

so w

ould

like

to th

ank

Todd

Pul

erw

itz, M

.D. (

Dep

artm

ent o

f Med

icin

e, C

olum

bia

Uni

vers

ity) f

or h

is e

fforts

in a

cqui

ring

the

ultra

soun

d da

ta s

ets.

1025

161

Opt

ical

Flo

w

160

160

7010

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an E

xper

t

Hum

an

Invo

lvem

ent

(min

utes

)

Tota

l tim

efo

r ful

l cyc

le(m

inut

es)

Tota

l tim

efo

r eje

ctio

n ph

ase

(min

utes

)

Sin

gle

fram

e (m

inut

es)

Test

ing

Env

ironm

ent:

“C”c

ode

on a

2.4

GH

z A

MD

64-

bit s

erve

r, ru

nnin

g R

edha

tLin

ux

3. R

ESU

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n-C

hest

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a

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0.1

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0.2

0.2

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sept

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olum

e (p

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mia

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Vol

ume

(bas

elin

e)

Max

err

or =

7ml

05

1015

020406080

Fram

e N

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r

Volume (ml)

Man

ual

OF

05

1015

0

0.1

0.2

0.3

0.4

0.5

Fram

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05

1015

020406080

Fram

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Volume (ml)

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ical

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a

Exp

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ents

: For

war

d an

d ba

ckw

ard

track

ing

and

re-in

itial

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usin

g m

anua

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and

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tion.

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46

0

0.1

0.2

0.3

0.4

0.5

Fram

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0.2

0.3

0.4

0.5

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initi

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mes

Re-

initi

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r fra

me

Com

bini

ng fo

rwar

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dba

ckw

ard

track

ing

RM

SE

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ng M

anua

l tra

cing

Usi

ng Q

LAB

©

Max

. erro

r = Man

ual:

0.09

(fra

me

09)

QLA

:0.0

8 (fr

ame

10)

(λ=0

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at E

D λ

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at E

S)M

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l

05

1015

0

0.1

0.2

0.3

0.4

0.5

Fram

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Man

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05

1015

020406080

Fram

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Volume (ml)

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OF

0-0

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0.1

0

0

0.1

0.2

0.1 0.2

0

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1

sept

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sept

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0 0.1

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0

0.1

0.2 -0

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0

sept

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lpo

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sept

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λ di

ff.

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Ant.

Lat.

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RM

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&

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mer

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aps

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Ham

mer

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aps

05

1015

020406080

Fram

eN

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Volume (ml)

RM

SE

&

Volu

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λ di

ff.

OF

vs. m

anua

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cing

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enta

tion

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ff.λ

diff.

OF

vs. m

anua

l tra

cing

at

bas

elin

e O

F vs

. man

ual t

raci

ng

at p

ost i

sche

mia

ESES

Max

. erro

r=13

ml

QLA

BO

F

REF

EREN

CES

REF

EREN

CES

[1] O

. T. v

on R

amm

, et a

l, “R

eal T

ime

Vol

umet

ric U

ltras

ound

Imag

ing

Sys

tem

s,”J

ourn

al o

f Dig

ital I

mag

ing,

vol.

3, p

p. 2

61-2

66, 1

990.

[2

] J. L

. Bar

ron,

et a

l, “P

erfo

rman

ce o

f Opt

ical

Flo

w T

echn

ique

s,”I

nt. J

of C

ompu

ter V

isio

n, v

ol. 1

2, p

p.43

-77,

199

4.[3

] S. H

erz,

et a

l, “N

ovel

Tec

hniq

ue fo

r Qua

ntita

tive

Wal

l Mot

ion

Ana

lysi

s U

sing

Rea

l-Tim

e Th

ree-

Dim

ensi

onal

E

choc

ardi

ogra

phy,

”Pro

c. o

f AS

E, 2

004.

[4] C

. M. I

ngra

ssia

, et a

l, “Im

pact

of I

sche

mic

Reg

ion

Siz

e on

Reg

iona

l Wal

l Mot

ion,

”Pro

c. O

f BM

ES

, 200

3.[5

] M. S

uhlin

g, e

t al,

“Myo

card

ial M

otio

n A

naly

sis

From

B-M

ode

Echo

card

iogr

ams,

”IE

EE

Tra

ns. I

mag

e P

roce

ssin

g,

vol.1

4, p

p.52

5-53

6, 2

005.

-10

-8-6-4-20246810

-3-2-10123

-0.3

-0.2

-0.1

00.1

0.2

0.3