Attenuation-Corrected

vs.

Nonattenuation-Corrected

-LSB-

-RSB-

fluoro-d-glucose-Positron

Emission

Tomography

in

Oncology

A

Systematic

Review

Purpose

To

perform

a

systematic

review

and

meta-analysis

to

determine

the

diagnostic

accuracy

of

attenuation-corrected

-LRB-

AC

-RRB-

vs.

nonattenuation-corrected

-LRB-

NAC

-RRB-

-LSB-

-RSB-

fluoro-d-glucose-positron

emission

tomography

-LRB-

FDG-PET

-RRB-

in

oncological

patients

Introduction

The

attenuation

of

photons

originating

from

the

subject

before

they

are

detected

by

the

camera

is

a

generic

limitation

of

nuclear

medicine

imaging

This

attenuation

can

lead

to

image

distortion

and

impairs

adequate

quantification

Attenuation

correction

has

been

commonly

employed

in

-LSB-

-RSB-

fluoro-d-glucose

-LRB-

FDG

-RRB-

imaging

in

an

attempt

to

correct

for

these

effects

With

positron

emission

tomography

-LRB-

PET

-RRB-

scanners

this

is

accomplished

by

transmission

scanning

using

a

radionuclide

source

such

as

or

and

with

PET/computed

tomography

-LRB-

CT

-RRB-

using

CT.

With

respect

to

visual

interpretation

of

the

images

the

added

value

of

attenuation

correction

has

been

controversial

Whereas

attenuation

correction

provides

a

more

realistic

image

of

FDG

distribution

its

application

significantly

increases

acquisition

times

on

standard

full-ring

-LRB-

FR

-RRB-

PET

scanners

In

addition

the

performance

of

attenuation

correction

can

introduce

noise

and

even

artifact

Paradoxically

even

if

the

nuclear

medicine

community

sees

attenuation

correction

or

the

lack

of

it

as

a

potential

effect-modifier

of

test

accuracy

its

impact

is

rarely

accounted

for

in

systematic

reviews

on

the

diagnostic

accuracy

of

PET

As

a

result

the

impact

of

attenuation

correction

on

lesion

detectability

and

interpretation

of

PET

for

oncological

purposes

is

not

well

established

With

PET/CT

scanning

it

is

customary

to

evaluate

either

modality

-LRB-

primarily

to

account

for

artifacts

-RRB-

but

one

needs

to

know

how

to

deal

with

discrepancies

The

objective

of

this

systematic

review

and

meta-analysis

was

to

determine

the

diagnostic

accuracy

of

nonattenuation-corrected

-LRB-

NAC

-RRB-

and

attenuation-corrected

-LRB-

AC

-RRB-

FDG-PET

in

oncological

patients

We

studied

the

effects

of

attenuation

correction

for

both

FR-PET

and

dual-head

coincidence

PET

-LRB-

DH-PET

-RRB-

and

as

a

function

of

different

body

locations

-LRB-

head/neck

chest

abdomen/pelvis

-RRB-

Materials

and

Methods

Literature

Search

A

computer-aided

literature

search

was

performed

in

both

Medline

and

Embase

databases

without

time

range

or

language

restrictions

applying

controlled

vocabulary

-LRB-

MeSH

and

EMTREE

keywords

respectively

-RRB-

as

well

as

free

text

words

The

search

date

was

February

The

search

strategy

-LRB-

Appendix

-RRB-

included

terms

for

PET

with

FDG

modified

from

Mijnhout

et

al.

-LSB-

-RSB-

as

well

as

search

terms

identifying

both

radionuclide

and

X-ray

transmission

emission

attenuation

correction

and

oncological

studies

in

humans

In

addition

the

reference

lists

of

the

eligible

articles

were

reviewed

to

ensure

that

relevant

articles

had

not

been

missed

Study

Selection

From

the

list

of

retrieved

articles

articles

were

initially

evaluated

for

eligibility

on

the

basis

of

title

and

abstract

by

two

independent

reviewers

-LRB-

UJ

PR

-RRB-

If

there

was

uncertainty

as

to

whether

an

article

was

eligible

for

inclusion

the

entire

article

was

reviewed

Inclusion

criteria

were

-LRB-

-RRB-

clinical

studies

evaluating

FDG

imaging

with

and

without

attenuation

correction

in

oncology

patients

-LRB-

-RRB-

study

population

of

at

least

ten

patients

-LRB-

-RRB-

sufficient

detail

to

reconstruct

a

contingency

table

expressing

FDG

imaging

results

by

disease

status

or

sufficient

detail

to

reconstruct

relative

lesion

detection

measurement

of

AC

vs.

NAC

imaging

and

-LRB-

-RRB-

studies

utilizing

FR-PET

and/or

DH-PET

We

excluded

abstracts

editorials

and

reviews

although

the

latter

two

categories

were

used

for

cross-referencing

Methodological

Quality

Assessment

The

methodological

quality

of

each

article

was

independently

assessed

by

each

reviewer

in

terms

of

internal

and

external

validity

-LRB-

Table

-RRB-

based

on

the

Cochrane

Methods

Group

in

Screening

and

Diagnostic

Tests

modified

for

our

area

of

interest

-LSB-

-RSB-

Internal

validity

items

focus

on

whether

a

valid

reference

test

was

used

and

whether

this

reference

test

was

uniformly

and

independently

applied

and

interpreted

as

well

the

type

of

study

design

The

external

validity

items

evaluate

the

applicability

of

the

results

in

terms

of

the

type

of

patient

population

and

spectrum

demographics

the

inclusion/exclusion

criteria

the

knowledge

of

previous

test/clinical

information

that

could

influence

interpretation

and

the

index

test

characteristics

Items

were

scored

as

positive

negative

or

unclear

Table

assessment

of

individual

diagnostic

studies

criteriaTestCriteriaA

Internal

study

validity

Al.

Valid

reference

testHistology

AC

FR

or

DH

coincidence

PET

Blind

measurement

of

reference

test

-LRB-

s

-RRB-

without

knowledge

of

index

testAssessment

of

reference

test

independent

of

index

test

-LRB-

s

-RRB-

results

Avoidance

of

verification

biasChoice

of

patients

assessed

by

reference

test

independent

of

index

test

result

Index

test

-LRB-

s

-RRB-

interpreted

independently

of

all

clinical

informationMentioned

in

publication

Prospective

studyMentioned

in

publicationB

External

study

validity

Spectrum

of

diseasesLocalization

of

disease

described

-LRB-

selected

or

general

-RRB-

Demographic

informationAge

and

sex

given

Inclusion

criteria

describedMentioned

in

publication

Exclusion

criteria

describedMentioned

in

publication

Avoidance

of

selection

biasConsecutive

series

of

patients

Standardized

execution

of

index

test

-LRB-

s

-RRB-

Described

technical

aspects

of

index

test

-LRB-

s

-RRB-

C.

Reproducibility

describedMentioned

in

publication

Data

Extraction

and

Quantitative

Analysis

In

addition

to

methodological

quality

assessment

data

related

to

the

type

of

camera

the

FDG

dose

the

time

interval

between

injection

and

imaging

the

transmission

and

emission

acquisition

protocols

the

reconstruction

protocol

and

the

interpretation

protocol

were

independently

extracted

from

each

study

by

each

reviewer

For

studies

where

it

was

possible

a

contingency

table

was

constructed

Disagreements

were

solved

by

consensus

For

studies

using

an

independent

gold

standard

-LRB-

histopathology

-RRB-

we

determined

the

sensitivity

and

specificity

of

the

index

tests

using

the

number

of

true

positive

false

positive

true

negative

and

false

negative

results

from

the

contingency

table

Furthermore

we

calculated

the

relative

lesion

detection

defined

as

the

percentage

of

lesions

scoring

equally

positive

or

negative

with

NAC

vs.

AC

images

We

performed

a

subgroup

analysis

for

different

locations

of

lesions

and

analyzed

sensitivity

specificity

or

relative

lesion

detection

of

NAC

vs.

AC

for

lesion

location

in

the

head

and

neck

region

the

chest

and

the

abdominopelvic

region

In

cases

of

discrepancy

of

relative

lesion

detection

between

NAC

and

AC

we

extracted

data

to

analyze

whether

this

was

related

to

lesion

size

and/or

intensity

The

statistical

diagnostic

heterogeneity

of

the

sensitivity

and

specificity

per

index

test

across

studies

was

tested

by

the

chi-square

test

In

case

of

statistical

heterogeneity

of

DH

or

FR-FDG-PET

imaging

a

random

effect

model

for

pooling

was

used

whereas

in

case

of

statistical

homogeneity

a

fixed-effect

model

was

used

Sensitivity

specificity

and

relative

lesion

detection

were

pooled

independently

all

pooled

estimates

are

presented

with

confidence

intervals

-LRB-

CI

-RRB-

The

logit

transformed

sensitivity

specificity

relative

lesion

detection

and

corresponding

CI

of

the

index

tests

were

compared

using

z-test

statistics

A

p

value

of

less

than

was

considered

significant

All

statistical

analyses

were

performed

with

the

SPSS

program

for

Windows

-LRB-

version

SPSS

Chicago

IL

USA

-RRB-

Results

The

search

strategy

yielded

references

in

Medline

and

in

Embase

on

February

Of

the

Embase

references

were

also

included

in

Medline

leaving

a

total

of

unduplicated

references

On

the

basis

of

title

and

abstract

alone

references

were

excluded

After

review

of

the

full

text

of

the

remaining

articles

an

additional

studies

-LSB-

-RSB-

proved

to

be

ineligible

because

they

did

not

perform

a

direct

comparison

of

the

yield

of

NAC

vs.

AC

images

in

oncological

patients

One

study

-LSB-

-RSB-

was

excluded

because

it

was

published

in

abstract

form

only

Another

study

-LSB-

-RSB-

was

excluded

because

it

was

published

in

Japanese

and

was

not

readily

translatable

Finally

the

study

of

Hustinx

et

al.

-LSB-

-RSB-

who

evaluated

the

effect

of

attenuation

correction

in

abdominal

tumors

for

a

sodium

iodide

crystal

-LRB-

NaI

-RRB-

PET

scanner

was

excluded

because

no

contingency

tables

could

be

constructed

Eventually

we

included

studies

for

review

-LSB-

-RSB-

A

summary

of

the

methodological

quality

assessment

results

can

be

found

in

Table

Methodological

quality

was

scored

as

negative

when

quality

items

were

unclear

or

absent

in

the

original

article

In

a

minority

-LRB-

-RRB-

of

studies

histology

served

as

the

reference

test

However

nine

of

the

studies

provided

a

direct

comparison

of

AC

and

NAC

PET

In

three

studies

blind

measurement

of

reference

test

was

performed

without

knowledge

of

index

test

All

but

one

study

avoided

verification

bias

In

four

studies

the

index

test

-LRB-

s

-RRB-

was

evaluated

independently

of

all

clinical

information

All

studies

provided

information

about

the

spectrum

of

diseases

being

evaluated

and

standardized

the

execution

of

the

index

test

-LRB-

s

-RRB-

Almost

all

studies

-LRB-

-RRB-

described

the

demographics

of

the

study

population

and

inclusion

criteria

However

only

one

study

mentioned

specific

exclusion

criteria

Six

studies

were

prospective

Only

two

of

the

studies

specifically

mentioned

including

consecutive

patients

and

only

three

studies

specifically

described

the

reproducibility

of

their

results

Table

assessment

of

included

Meta-Analysis

Three

FR-PET

studies

were

eligible

for

pooling

of

sensitivity

on

a

patient

basis

-LSB-

-RSB-

The

pooled

sensitivities

for

AC

and

NAC

FR-PET

were

-LRB-

CI

-RRB-

and

-LRB-

CI

-RRB-

respectively

-LRB-

n

patients

-RRB-

Two

FR-PET

studies

provided

data

that

allowed

pooled

analysis

of

specificity

-LSB-

-RSB-

Weber

et

al.

-LSB-

-RSB-

could

not

be

included

as

there

were

no

patients

without

disease

The

pooled

specificities

for

AC

and

NAC

FR-PET

were

-LRB-

CI

-RRB-

and

-LRB-

CI

-RRB-

respectively

-LRB-

n

patients

-RRB-

For

DH-PET

only

one

study

provided

data

on

sensitivity

and

specificity

-LSB-

-RSB-

Relative

lesion

detection

for

NAC

vs.

AC

PET

was

pooled

for

studies

which

are

demonstrated

in

Fig.

Lesion

detection

of

NAC

FR-PET

vs.

AC

FR-PET

was

-LRB-

CI

-RRB-

for

n

lesions

-LRB-

pooling

of

n

studies

-RRB-

of

which

were

classified

FDG

positive

at

AC

FR-PET

Lesion

detection

of

NAC

DH-PET

vs.

AC

DH-PET

was

-LRB-

CI

-RRB-

for

n

lesions

-LRB-

pooling

of

n

studies

-RRB-

of

which

were

classified

as

FDG-positive

at

AC

DH-PET

Fig.

lesion

detection

of

NAC

vs.

AC

images

for

FR-PET

and

DH-PET

In

addition

we

evaluated

the

relative

lesion

detection

depending

on

body

location

-LRB-

head/neck

chest

abdomen/pelvis

-RRB-

in

the

four

FR-PET

-LSB-

-RSB-

and

in

the

three

DH-PET

studies

that

provided

sufficiently

detailed

information

-LSB-

-RSB-

The

relative

sensitivity

and

specificity

based

on

body

location

could

not

be

calculated

due

to

an

insufficient

number

of

studies

For

FR-PET

we

found

similar

relative

lesion

detection

for

the

three

body

locations

for

head/neck

-LRB-

CI

n

lesions

-RRB-

for

the

chest

-LRB-

CI

n

lesions

-RRB-

and

for

the

abdomen/pelvis

-LRB-

CI

n

lesions

-RRB-

For

DH-PET

relative

detection

rates

for

NAC

were

not

significantly

different

for

the

various

body

sites

in

the

abdomen/pelvis

-LRB-

CI

lesions

-RRB-

in

the

chest

-LRB-

CI

lesions

-RRB-

and

in

the

head/neck

area

-LRB-

CI

lesions

-RRB-

However

in

chest

-LRB-

p

-RRB-

and

abdomen/pelvis

-LRB-

p

-RRB-

the

relative

detection

rates

with

NAC

-LRB-

vs.

AC

-RRB-

for

DH-PET

were

significantly

lower

than

those

obtained

with

FR-PET

A

comprehensive

analysis

of

the

potential

association

of

relative

detection

and

lesion

size

and/or

intensity

for

lesions

with

discrepant

AC

and

NAC

results

was

not

possible

due

to

a

lack

of

detailed

information

We

summarized

the

results

in

Table

findings

of

Bleckmann

et

al.

and

Reinhardt

et

al.

-LSB-

-RSB-

suggest

that

AC

and

NAC

discrepancies

may

relate

to

-LRB-

intrapulmonary

-RRB-

lesion

size

with

more

discrepancies

occurring

with

smaller

lesions

at

the

subcentimeter

level

-LRB-

an

average

of

of

lesions

were

correctly

detected

with

NAC

and

not

with

AC

-RRB-

The

single

discrepant

lesion

in

the

study

of

Weber

et

al.

concerned

a

lesion

in

the

mediastinum

-LSB-

-RSB-

However

the

discrepant

lesions

in

the

studies

of

Nakamoto

et

al.

Schauwecker

et

al.

and

Delbeke

et

al.

included

both

small

and

moderate-sized

lesions

-LRB-

in

relation

to

lesions

included

each

study

-RRB-

-LSB-

-RSB-

In

the

study

of

Schauwecker

et

al.

the

discrepant

lesions

demonstrated

SUVmax

values

ranging

from

to

whereas

in

the

study

of

Delbeke

et

al.

the

two

discrepant

lesions

demonstrated

only

mildly

enhanced

uptake

on

AC

images

and

equivocal

uptake

on

NAC

images

Table

of

discrepant

lesions

between

AC

and

NAC

images

with

respect

to

lesion

size

and

intensityStudyCamera

typeNumber

of

discrepant

lesionsSize

rangeIntensity

-LRB-

semiquantitative

or

qualitative

-RRB-

Bleckmann

et

cmNot

givenNakamoto

et

cmNot

givenReinhardt

et

lesions

demonstrated

discrepancy

in

qualitative

lesion

intensity

lesions

demonstrating

higher

intensity

-LRB-

i.e.

better

visibility

-RRB-

on

NAC

imagesSchauwecker

et

-LRB-

SUVmax

-RRB-

Weber

et

cmNot

givenDelbeke

et

cm

Mild

uptake

at

AC

equivocal

uptake

at

NACaHistology

used

as

gold

standard

and

detailed

information

given

only

for

true

positive

lesions

Discussion

The

cumulated

evidence

summarized

in

this

systematic

review

of

oncological

FDG

imaging

studies

suggests

that

the

accuracy

of

attenuation

and

nonattenuation

corrected

FR-PET

are

similar

However

with

DH

coincidence

imaging

NAC

images

detect

less

lesions

than

AC

images

without

prominent

differences

between

body

areas

Although

in

the

nuclear

medicine

field

attenuation

correction

is

generally

seen

as

an

important

issue

it

is

surprising

to

find

that

several

large

systematic

reviews

did

not

thoroughly

consider

this

as

a

potential

effect-modifier

Gould

et

al.

performed

systematic

reviews

on

FDG

PET

in

pulmonary

lesions

-LSB-

-RSB-

and

mediastinal

lymph

node

staging

in

non-small

cell

lung

cancer

-LSB-

-RSB-

in

the

former

review

the

item

was

not

mentioned

and

in

the

latter

attenuation

correction

was

an

item

of

study

quality

but

no

analysis

of

potential

impact

was

performed

The

choice

of

the

reference

test

is

obviously

relevant

in

studies

on

test

accuracy

In

oncology

histopathology

is

the

typical

endpoint

Of

the

eligible

studies

four

used

histology

as

an

independent

gold

standard

Meta-analysis

of

sensitivity

and

specificity

was

only

possible

for

FR-PET

and

we

found

no

significant

difference

for

either

measure

We

chose

to

use

the

AC

detection

rate

as

an

alternative

reference

test

which

defines

the

relative

lesion

detection

of

NAC

vs.

AC

images

This

choice

theoretically

biases

in

favor

of

AC

Bleckmann

et

al.

and

Reinhardt

et

al.

-LSB-

-RSB-

reported

an

average

of

more

true

positive

lesions

with

NAC

FR-PET

However

we

expect

that

the

resulting

error

is

small

because

in

the

comparison

with

histopathology

false

positive

rates

were

quite

low

for

either

modality

Despite

this

theoretical

negative

bias

towards

NAC

similar

lesion

detection

rates

were

observed

with

both

AC

and

NAC

for

FR-PET

Hence

attenuation

correction

may

not

contribute

to

the

detection

of

malignancy

using

FR-PET

Conversely

with

DH-PET

AC

images

demonstrated

a

significantly

higher

detection

rate

as

opposed

to

NAC

images

which

is

surprising

given

that

AC

images

are

usually

significantly

noisier

than

NAC

images

We

postulate

that

this

may

be

secondary

to

differences

in

reconstruction/filtering

algorithms

In

addition

there

are

limitations

associated

with

performing

a

meta-analysis

and

data

pooling

such

as

the

homogeneity

of

the

data

and

the

quality

of

the

published

studies

Homogeneous

data

have

higher

statistical

strength

than

heterogeneous

data

The

data

in

our

study

were

heterogeneous

so

that

we

used

a

random

effect

model

for

pooling

In

addition

the

statistical

strength

of

the

meta-analysis

is

limited

by

the

quality

of

the

published

studies

included

in

it

As

mentioned

earlier

and

summarized

in

Table

the

studies

had

several

quality

limitations

Finally

meta-analyses

are

limited

by

publication

bias

which

biases

towards

the

publication

of

favorable

results

or

popular

subjects

We

were

surprised

by

the

limited

number

of

good

comparative

studies

evaluating

the

value

of

attenuation

correction

It

appears

that

attenuation

correction

has

been

accepted

as

the

standard

of

practice

without

sound

scientific

evidence

to

support

it

The

introduction

of

PET/CT

machines

has

made

the

time

constraints

associated

with

transmission

scanning

less

of

an

issue

However

PET/CT

is

not

a

panacea

X-ray

transmission

scanning

has

its

own

problems

and

numerous

PET/CT

publications

have

demonstrated

artifact

that

can

be

introduced

with

X-ray

transmission

scanning

-LSB-

-RSB-

Furthermore

in

the

study

of

Reinhardt

et

al.

-LSB-

-RSB-

a

significantly

improved

visibility

was

demonstrated

for

of

lung

metastases

with

NAC

images

as

opposed

to

CT-AC

images

This

higher

visibility

for

NAC

images

was

even

more

pronounced

for

lesions

smaller

than

cm

These

findings

underline

that

even

as

PET/CT

use

becomes

more

widespread

evaluation

of

both

NAC

and

AC

images

should

remain

an

integral

part

of

image

interpretation

and

not

just

to

recognize

image

artifacts

At

the

same

time

NAC

vs.

AC

discrepancies

at

PET/CT

offer

an

obvious

opportunity

for

further

investigation

Conclusions

In

this

meta-analysis

we

found

no

significant

difference

in

sensitivity

specificity

or

relative

lesion

detectability

between

AC

and

NAC

FR

FDG

PET

However

attenuation

correction

improved

lesion

detection

for

DH

coincidence

imaging

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O

attenuation correction
positron emission tomography

deoxyglucose (mesh)
tomography, x-ray computed (mesh)
tomography, emission-computed (mesh)
neoplasms (mesh)
human (mesh)
systematic review (mesh)


Tricholoma matsutake 1-Ocen-3-ol and methyl cinnamate repel mycophagous Proisotoma minuta -LRB- Collembola : Insecta -RRB- Two major volatiles produced by the mycelia and fruiting bodies of Tricholoma matsutake -LRB- 1-octen-3-ol and methyl cinnamate -RRB- repel a mycophagous collembolan , Proisotoma minuta . Aggregation of the collembolans on their diet was significantly inhibited by exposure to 1 ppm methyl cinnamate or 10 to 100 ppm 1-octen-3-ol . The aggregation activity decreased dose-dependently upon exposure to 1-octen-3-ol at concentrations higher than 0.01 ppm . Aggregation in the presence of methyl cinnamate exhibited three phases : no significant effect at concentrations ranging from 0.001 to 0.1 ppm , significant inhibition from 1 to 100 ppm , and strong inhibition at 1,000 ppm . These results may explain why certain collembolan species do not prefer T. matsutake fruiting bodies . Introduction Fungal fruiting bodies are more nutritious than their mycelia -LRB- Stark 1972 ; Cromack et al. 1975 ; Vogt and Edmonds 1980 -RRB- and are exploited by various arthropods , including dipteran larvae , collembolans , and acarine and oribatid mites -LRB- Hammond and Lawrence 1989 -RRB- . Collembolans are the most abundant group of insects found on agaric fruiting bodies -LRB- Yamashita and Hijii 2003 -RRB- , and numbers often reach hundreds to thousands on a suitable fruiting body -LRB- Sawahata et al. 2000 , 2001 , 2002 ; Nakamori and Suzuki 2005a -RRB- . Collembolans attack the hymenial area , often consuming 50 % or more of it in suitable fruiting bodies -LRB- Sawahata 2006 -RRB- . Additionally , collembolans are able to break the hyaline and thin-walled basidiospores of some fungi -LRB- Ponge and Charpentie 1981 ; Sawahata et al. 2001 ; Nakamori and Suzuki 2005b -RRB- . Therefore , collembolans are likely to adversely affect spore production by fungal species with these types of spores . The fruiting body of the ectomycorrhizal fungus Tricholoma matsutake -LRB- S. Ito and Imai -RRB- Sing. , which has the common name matsutake , is one of the favorite and most valuable food items produced by forests in Japan because of its characteristic odor and texture , but artificial cultivation of T. matsutake has not yet succeeded . Sawahata and Narimatsu -LRB- 2006 -RRB- reported that collembolan populations on the fruiting bodies of T. matsutake are smaller than those on other mushrooms growing in the same forest . The gut contents of collembolans collected from the fruiting bodies suggested that they fed on the fungal tissue on the gill surface -LRB- Sawahata and Narimatsu 2006 -RRB- . In our preliminary studies , Folsomia candida Willem and Proisotoma minuta Tullberg fed on sliced samples of the fruiting body of T. matsutake in a laboratory experiment -LRB- data not shown -RRB- . These findings suggested that the mushroom is not poisonous to collembolans and that it is simply avoided as a source of food when alternatives are available . Collembolans use fungal volatiles to recognize potential sources of food -LRB- Bengtsson et al. 1988 ; Hedlund et al. 1995 -RRB- . Bengtsson et al. -LRB- 1991 -RRB- suggested that the volatile compounds 1-octen-3-ol and 1-hepten can aid the collembolans in their search for a palatable fungus . The major volatiles produced by T. matsutake are 1-octen-3-ol and methyl cinnamate -LRB- Ohta 1983 ; Terashita et al. 1991 -RRB- . The former is a primary volatile produced by many species of fungi -LRB- Kaminski et al. 1974 ; Pyysalo 1976 -RRB- , and the latter determines the characteristic odor of this fungus -LRB- Terashita et al. 1991 -RRB- . Hence , methyl cinnamate may prevent aggregation and feeding on the fruiting body by some collembolans . Further , high concentrations of 1-octen-3-ol can repel fungivores -LRB- Pfeil and Mumma 1993 ; Wood et al. 2001 -RRB- . The fruiting body of T. matsutake contains 1-octen-3-ol at concentrations ranging from 2 to 186 ppm on a fresh-weight basis -LRB- Ohta 1983 ; Terashita et al. 1991 -RRB- , but it is unknown whether 1-octen-3-ol repels collembolans at high concentrations . The present study aimed at investigating the effects of the major volatile components of T. matsutake -LRB- 1-octen-3-ol and methyl cinnamate -RRB- at different concentrations on the aggregation of mycophagous collembolans by biological assay -LRB- feeding test -RRB- . Our goal was to provide an explanation for why collembolans aggregate in relatively small numbers on T. matsutake fruiting bodies . Materials and methods Proisotoma minuta was extracted from forest soils near the Matsunoyama Museum of Natural Science in Niigata Prefecture -LRB- 307 m above sea level , 37 ° 05 ′ N , 138 ° 36 ′ E -RRB- , in central Japan . This species was used because it fed on various ectomycorrhizal fungi in a choice experiment -LRB- Schultz 1991 ; Hiol Hiol et al. 1994 -RRB- , and it is amenable to laboratory culture . The collembolans were reared on dry yeast at 20 °C in a cylindrical chamber -LRB- 11 cm in diameter , 7 cm in height -RRB- whose bottom -LRB- 1.0 to 1.5 cm in thickness -RRB- was covered by a mixture of plaster of Paris and charcoal -LRB- 10:1 v/v -RRB- . We followed the methods used in previous food choice experiments -LRB- e. g. , Schultz 1991 ; Kaneda and Kaneko 2004 -RRB- . Each assay -LRB- with six replications -RRB- used 70 individuals -LRB- 1 month after eclosion from eggs , 0.8 to 1.1 mm in body size -RRB- at a time in the same type of chamber . The major volatile compounds of T. matsutake have already been determined to be 1-octen-3-ol and methyl cinnamate by several researchers -LRB- e.g. , Ohta 1983 ; Terashita et al. 1991 -RRB- . We substituted laboratory-grade versions of these chemicals for the natural volatile compounds . 1-Octen-3-ol -LRB- Wako Pure Chemical Industries , Osaka , Japan -RRB- was diluted with distilled water to prepare solutions of 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , and 1,000 ppm . Methyl cinnamate -LRB- Wako -RRB- was diluted similarly but with dichloromethane . Controls were created using distilled water and dichloromethane , respectively . Into each diluted solution , three pieces of filter paper -LRB- 3 cm in diameter -RRB- were dipped for long enough that the paper became completely saturated . The wet 1-octen-3-ol filter papers were placed 1 cm apart on the bottom of the assay chamber to form a triangle -LRB- Fig. 1 -RRB- . The wet methyl cinnamate filter papers were kept in air for 15 min to evaporate the solvent and then moistened with distilled water before bioassay by the same procedure . Fig. 1Bioassay method used to assess the ability of 1-octen-3-ol and methyl cinnamate to repel the collembolan Proisotoma minuta -LRB- n = 70 -RRB- . Plugs of potato-dextrose agar -LRB- PDA -RRB- were placed at the center of each sample-treated filter paper on a mixture of plaster of Paris and charcoal -LRB- 10:1 v/v -RRB- at the bottom of the cylindrical chamber On each sample of filter paper , including the control , a plug -LRB- 1 cm in diameter -RRB- of 3 % potato dextrose agar medium -LRB- a PDA plug -RRB- was provided as food -LRB- Fig. 1 -RRB- . Seventy insects were introduced into the chamber and held for 90 min in the dark at 20 °C ; then , the individuals on each PDA plug were counted without disturbance . The results represent means of 18 samples -LRB- six replicates of three PDA plugs per chamber -RRB- among the dilution series -LRB- eight levels , including the control -RRB- for the two test compounds . The mean numbers of collembolans in each treatment were compared by F test , one-way analysis of variance , and a multiple-range test -LRB- Fisher 's PLSD -RRB- . Statistical analyses were performed with Stat View 5.0 J software -LRB- SAS Institute , Cary , NC , USA -RRB- . Results Both compounds at 1,000 ppm almost completely inhibited the aggregation of P. minuta on the PDA plugs -LRB- Figs. 2 and 3 -RRB- , leaving means of fewer than 1.0 and 0.5 individuals per PDA plug on methyl cinnamate and 1-octen-3-ol , respectively . Statistically significant reductions in aggregation were also observed at several lower concentrations . Fig. 2Influence of the concentration of methyl cinnamate on the number of Proisotoma minuta aggregated to feed on PDA plugs . Gray bars indicate the means of 18 samples -LRB- six replicates of three PDA plugs per dish -RRB- , and the error bars indicate the standard error -LRB- SE -RRB- of the mean . Means labeled with different letters differ significantly -LRB- Fisher 's PLSD test , P < 0.05 -RRB- Fig. 3Influence of the concentration of 1-Octen-3-ol on the number of Proisotoma minuta aggregated to feed on PDA plugs . Gray bars indicate the means of 18 samples -LRB- six replicates of three PDA plugs per dish -RRB- , and the error bars indicate the standard error -LRB- SE -RRB- of the mean . Means labeled with different letters differ significantly -LRB- Fisher 's PLSD test , P < 0.05 -RRB- Discussion Methyl cinnamate exhibited three ranges of concentrations with different abilities to inhibit the aggregation of P. minuta -LRB- Fig. 2 -RRB- : no significant effect -LRB- a mean of 13.3 to 16.1 individuals per plug -RRB- from 0.001 to 0.1 ppm , incomplete but significant inhibition -LRB- a mean of 1.2 to 8.2 individuals per plug -RRB- from 100 to 1,000 ppm , and strong inhibition at 1,000 ppm . Thus , methyl cinnamate repels collembolans at high concentrations . Methyl cinnamate has previously been shown to be a principal volatile component of the fruiting body of T. matsutake -LRB- Terashita et al. 1991 -RRB- , and its content increases during fungal development to reach 22 to 154 ppm on a fresh weight basis by the time the veil breaks -LRB- Ohta 1983 -RRB- . High levels of the compound -LRB- 150 to 360 ppm , with a mean of 236 ppm -RRB- are localized in the lamellae and spores -LRB- Ohta 1983 -RRB- , where collembolans are frequently observed feeding on the spores and hyphae of the hymenia -LRB- Sawahata 2006 -RRB- . The high content of methyl cinnamate in the lamellae may thus prevent aggregation and grazing on gill surfaces by collembolans . 1-Octen-3-ol has previously been shown to be another principal volatile component of the fruiting body of T. matsutake , at concentrations in the fruiting bodies ranging from 2 to 186 ppm on a fresh weight basis -LRB- Ohta 1983 ; Terashita et al. 1991 -RRB- . This volatile component exhibited a clear dose -- response relationship -LRB- Fig. 3 -RRB- : As the concentration of 1-octen-3-ol increased from 0.001 to 1000 ppm , aggregation of the insects decreased continuously , and the difference from the control became significant at concentrations of 10 ppm and higher . At concentrations of 100 and 1,000 ppm , 1-octen-3-ol significantly reduced aggregation of P. minuta -LRB- to an average of 1.2 individuals per PDA plug -RRB- . These results suggest that Collembola show a different response pattern at different concentrations , with a slight and nonsignificant increase in aggregation compared with the control at concentrations ranging up to 0.01 ppm ; at higher concentrations , aggregation decreased significantly -LRB- to less than 50 % of the control -RRB- , and at 1000 ppm , aggregation was almost completely eliminated . Bengtsson et al. -LRB- 1991 -RRB- also found a weak but nonsignificant attractive effect of 1-octen-3-ol on collembolans -LRB- individuals of Onychiurus armatus Tullb . -RRB- in soil fungi . Further studies with new methods are needed to explain the attractive effect of low levels of 1-octen-3-ol among a range of collembolans . The present study provides an explanation of why certain collembolans avoid aggregation on T. matsutake fruiting bodies in the field . Both major components of the volatiles present in the fruiting bodies of this species -LRB- methyl cinnamate and 1-octen-3-ol -RRB- defend against collembolan attack at high concentrations . Wood et al. -LRB- 2001 -RRB- reported that 1-octen-3-ol prevents feeding by banana slugs at a concentration of 28 ppm . Pfeil and Mumma -LRB- 1993 -RRB- reported that 1-octen-3-ol and 3-octanone seemed to deter gravid females of Megaselia halterata Wood -LRB- Phoridae -RRB- at high concentrations . Therefore , some fungal species may produce high concentrations of 1-octen-3-ol to defend against mycophagous insects .

O B O O B I O O O O O B O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Decomposition cross-correlation for analysis of collagen matrix deformation by single smooth muscle cells Microvascular remodeling is known to depend on cellular interactions with matrix tissue . However , it is difficult to study the role of specific cells or matrix elements in an in vivo setting . The aim of this study is to develop an automated technique that can be employed to obtain and analyze local collagen matrix remodeling by single smooth muscle cells . We combined a motorized microscopic setup and time-lapse video microscopy with a new cross-correlation based image analysis algorithm to enable automated recording of cell-induced matrix reorganization . This method rendered 60 -- 90 single cell studies per experiment , for which collagen deformation over time could be automatically derived . Thus , the current setup offers a tool to systematically study different components active in matrix remodeling . Introduction The extracellular matrix -LRB- ECM -RRB- provides a biophysical and biochemical environment for cell mechanical behavior . In turn , cellular interactions with the ECM resulting from adhesive , proteolytic and migratory activity govern continuous matrix reorganization . One such example occurs in eutrophic inward remodeling of small arteries -LSB- 34 -RSB- . Here , the existing collagen matrix is rearranged around a smaller diameter . Such inward remodeling is a hallmark of many hypertensive disorders and has been shown to have predictive value for cardiovascular events -LSB- 9 -RSB- . In vitro setups of cell-seeded matrix scaffolds allow studying the specific components that are active in vascular remodeling -LSB- 22 -RSB- . Especially , matrix reorganization can be monitored in the presence of specific cells , matrix elements , and blockers or markers of remodeling enzymes such as the matrix metalloproteases and transglutaminases -LSB- 1 , 2 -RSB- . Several approaches using multi-cellular preparations have been developed . Thus , free-floating collagen gels can be seeded with contractile cells , and the remodeling is monitored from the dimensions of these gels -LSB- 8 , 13 , 16 , 20 -RSB- . In the so-called culture force monitor , collagen gels mixed with cells are allowed to polymerize between a fixed plate and a force transducer . Contractile force development is measured during subsequent culture of this preparation -LSB- 12 , 14 , 22 , 36 -RSB- . However , interpretation of data derived from these multi-cellular approaches is troubled by variability in cellular mechanical activity , lack of information on cell density , and synergistic contractile effects -LSB- 30 -RSB- . Therefore , microscopic observations on matrix reorganization by single cells may provide more detailed and fundamental information on the mechanisms of remodeling . Several groups have studied the effect of single cell activity on local deformation of matrices -LSB- 5 , 28 -RSB- . The most frequently used substrates are silicone rubber -LSB- 3 , 6 , 18 , 21 -RSB- and polyacrylamide sheets . Local deformation is then monitored by tracing the texture , sometimes facilitated by using fluorescent beads or imprinted micropatterns . Although these inert scaffolds provide a geometrical framework to study processes like cell locomotion , they do not resemble the physiological ECM . This has been solved in some cases by coating the polyacrylamide membrane with a thin layer of collagen -LSB- 11 , 17 , 26 , 27 , 37 -RSB- . However , remaining issues are the orientation of the collagen fibrils in the coating as compared to native collagen , and the inability of the cells to chemically modify -LRB- e.g. by cross-linking -RRB- the artificial sheets . A possible way to overcome these issues is the use of collagen-based matrices . Cell-induced deformation patterns of these matrices and the degree of reversibility of such deformation provide information on physical remodeling and the underlying biochemical processes . Deformations have been assessed by manually tracing beads or landmarks in consecutive images -LSB- 6 , 28 , 30 -RSB- . However , so far , only a few algorithms were developed for automatic detection of substrate deformations . In particular , very few of such algorithms allow tracing in the absence of embedded beads -LSB- 23 , 33 , 35 -RSB- , and more stable algorithms are required . While single cell observations provide a more fundamental insight into matrix remodeling than macroscopic studies , a concern is efficiency . Time-lapsed video microscopy of single cells forms a labor-intensive and time-consuming experiment . Meaningful experiments require the comparison of large data sets , including different cell types , various matrix compositions , or the use of biochemical or molecular interventions . A fully automated , motorized microscopy setup is required that scans series of individual cells and their surrounding matrix . In addition , each individual cell should automatically be kept in focus during remodeling experiments , which take up 24 h or more . Finally , stable algorithms are needed for the automated analysis of geometrical reorganization with minimal user input . The aim of this study is to develop an automated technique that can be employed to obtain and analyze local matrix remodeling by individual cells . The system that we present allows for monitoring of ∼ 75 cells in parallel , using time-lapse video microscopy and computer-controlled stage positioning . In addition , we present and evaluate a new algorithm for automated detection of collagen matrix deformation around these cells . Methods Cell culturing and collagen matrix preparation Smooth muscle cells , obtained from mesenteric small arteries , were cultured in Leibovitz medium with 10 % -LRB- v/v -RRB- heat-inactivated fetal calf serum . Cells from passages three to nine were used in experiments . Matrix constructs were produced from calf skin collagen -LRB- MP Biomedicals -RRB- at a concentration of 1 mg/ml ; pH was buffered by HEPES , and a mix of antibiotics -LRB- PSF and ciproxin -RRB- was added . Immediately after preparation at 4 °C , the collagen mixture was poured into a 3.8 cm2 culture well and a 1.5-h polymerization period at 37 °C was allowed . Then , SMCs were seeded at a concentration of about 1 cell per mm2 in the presence of 1 ml serum-free Leibovitz medium . The cells were maintained in an incubation chamber that was set to a temperature of 37 °C throughout the experimental procedure . After a stabilization period of about 1 h , cell -- matrix interactions were monitored by microscopic imaging for a period of 24 h of spontaneous cell contraction . Automated microscopic imaging In order to enable simultaneous monitoring of cell-induced matrix remodeling at multiple locations , microscopy was combined with a motorized stage . Individual cells and their surrounding matrix were studied by phase-contrast microscopy -LRB- Olympus IMT-2 with 10 × objective and 2.5 × projection lens -RRB- . Images were captured by a Qimaging Retiga SRV camera . The calibration factor for these images -LRB- 1,392 × 1,040 pixels -RRB- was 0.88 μm/pixel . The microscopic field of view was set by a motorized stage , controlled by custom written software -LRB- Matlab 7.0 with Image Acquisition Toolbox 2.0 -RRB- . After manually determining and storing a set of x , y , z-coordinates for about 60 -- 90 appropriate cells , these positions were tracked through time at a 15-min interval and images were captured by an automated procedure . During the time-lapsed image acquisition , samples were kept in focus by means of implementation into the acquisition software of one of the general auto-focus algorithms . Image contrast is optimal when a histogram of intensity values shows a broad distribution over all bins . This characteristic feature can be approximated by the standard deviation of pixel intensity values . For each image , contrast was enhanced by histogram equalization , and standard deviation was calculated . A normalized focus index -LRB- FI -RRB- was defined by dividing the standard deviation of the original image -LRB- SDoriginal -RRB- by the standard deviation of the contrast enhanced image -LRB- SDenhanced -RRB- : For each cell , a series of images was captured at five different heights . The image was then defined to be in focus at the vertical level of maximal normalized focus . This height -LRB- z -RRB- was used as the central level when capturing the z-series in the next time step , thus allowing gradual vertical shift during the time-lapsed image acquisition . Finally , a stack of time-lapsed images was constructed for each x , y-position and analyzed off-line . Gel dynamics analysis Cell -- matrix interactions were quantified offline using in-house designed , automated image analysis software -LRB- Matlab 7.0 with Image Processing Toolbox 4.2 -RRB- . Matrix reorganization was assessed by calculation of the displacement field around a cell . This was achieved by performing a cross-correlation between each two successive images in an image stack ; resolution of the displacement field was refined by correlation of subimages of decreasing size . This procedure is explained below and illustrated in Fig. 1 . Fig. 1Graphical representation of parameters used in matrix deformation analysis . The image shows a single SMC in the center , surrounded with a collagen matrix of relatively smooth texture . The white box -LRB- 768 × 768 pixels -RRB- indicates the area for the most coarse correlation analysis . This correlation between two successive images was applied over an area as indicated by the yellow box -LRB- 960 × 960 pixels -RRB- . The green boxes show the more refined cross-correlation windows -LRB- stages 2 -- 5 -RRB- . Table 1 indicates the area expansion used for these correlation analyses First , gross displacement was defined at the point of maximal correlation between two parent images I 1 -LRB- at t = t 0 -RRB- and I 2 -LRB- at t = t 0 + Δt -RRB- , using a correlation threshold of 0.5 and a maximum tested displacement of 11 pixels . Then , I 1 was decomposed into four equal-sized square subimages , for which another cross-correlation was performed . The position of the correlation subwindow of I 2 was refined using the displacement as calculated for the parent image , with a safety measure to prevent I 2 crossing the borders of I 1 . This procedure of accurately positioning a correlation window not only reduced the possibility of an accidental cross-correlation match between a subimage of I 1 and any random subimage of I 2 , but also drastically reduced calculation time . Resolution of the displacement field was refined to the fifth decomposition stage in our experiments -LRB- see Fig. 1 -RRB- , using maximum tested displacements -LRB- n cc -RRB- as indicated in Table 1 . When insufficient correlation -LRB- r < 0.5 -RRB- was found in any small area , displacement field from the next larger images were used . The calculated displacements were then assigned to the corresponding subimage centers . Subsequently , a continuous displacement field was obtained by bicubic interpolation . Finally , matrix compaction for each stack was quantified by tracking circular areas centered around the cell -LRB- see Fig. 5 -RRB- . Table 1Settings for matrix compaction analysis by decomposition cross-correlationDecomposition stageCross-correlation windowSearch area expansion -LRB- n cc -RRB- 1768 × 768962384 × 384483192 × 19224496 × 9624 5 48 × 48 24 624 × 2412712 × 126Dimensions of the cross-correlation window I 1 were reduced by 50 % for each decomposition stage . The search area of I 2 for correlation with I 1 was obtained by increasing the cross-correlation window with n cc pixels on all image sides . The settings which were used for gel dynamics analysis and validation are given in bold Validation The method described above -LRB- decomposition CC -RRB- was validated on several test series against a straightforward cross-correlation analysis -LRB- direct CC -RRB- , with settings according to decomposition stage 1 . The first test case consisted of an image of a collagen-embedded cell , which was artificially resized by 3 % , thereby simulating matrix compaction . Secondly , increasing amounts of white noise were added to the resized image in order to test the stability of both correlation methods . Relative dispersion -LRB- RD -RRB- , which is defined as standard deviation divided by mean , was used as a noise level index . Finally , image resizing was followed by a horizontal translation of 60 pixels for a low and high noise example -LRB- see Table 2 -RRB- . Table 2Characteristics of validation images : CC was tested by addition of Gaussian white noise with mean 0.0 and increasing variance levels , in several cases the image was resized or translatedIndexScaling -LRB- % -RRB- Gaussian white noise varianceRD imageRD increase -LRB- % -RRB- RD noiseHorizontal shift -LRB- pixels -RRB- a10 .00000.2500 b0 .970.00000.2510.50 c0 .970.00050.2604.10.1040 d0 .970.00100.2697.60.1460 e0 .970.00200.28514.10.2070 f0 .970.00300.30120.20.2530 g0 .970.00400.31626.30.2930 h0 .970.00500.32931.70.3270 i0 .970.00050.2594.10.10860 j0 .970.00300.30320.20.26560 Relative dispersion -LRB- RD -RRB- in an image is defined as standard deviation divided by mean , RD increase is expressed as RD value compared to case `` a '' , RD noise is calculated as ratio of noise variance and image mean Results Parallel recording of matrix compaction movies We were able to record on average 66 movies on collagen compaction by single cells in parallel at a time resolution of 15 min -LRB- n = 10 experiments -RRB- . Critical issues that limited this number were the need to avoid rapid acceleration and deceleration of the microscope stage , and the time-consuming autofocus algorithm . If cells were properly focused in the initialization stage , only a small fraction -LRB- < 5 % -RRB- ran out of focus during 24-h compaction experiments . Under the given incubation conditions , the majority of the cells adhered firmly to the collagen matrix and demonstrated little migratory activity . Typically , cell centroids remained within a 150 μm radius of their original xy position . z position decreased slowly in many cases , reflecting compaction of the matrix in the vertical direction -LRB- data not shown -RRB- . Validation of the collagen compaction analysis Analysis based on cross-correlation of images at a series of decomposition stages was compared with straightforward cross-correlation . This was performed on images simulating matrix compaction , subsequently followed by a challenge of increasing amounts of image noise and artificial translation . Decomposition CC was more time-consuming than direct CC : respectively , 84 and 54 s per image pair . This was due to a larger number of cross-correlations that have to be performed , and more complex data storage and lookup operations . For low noise levels -LRB- Table 2 , case c , d -RRB- , both analysis methods render 100 % correct displacement vectors -LRB- see Fig. 2 -RRB- . When noise increased , the number of vectors in the smallest decomposition stage that had insufficient cross-correlation -LRB- r < 0.5 -RRB- rose . However , this effect was smaller in the decomposition CC versus direct CC . As an example , the percentage correct displacement vectors at 20.2 % noise -LRB- Table 2 , case f -RRB- was 77.7 % in direct CC versus 89.5 % in decomposition CC . Concurrently , the average displacement error at this noise level amounted to 2.1 and 0.7 pixels , respectively . Fig. 2Noise sensitivity of the cross-correlation methods . Top from left to right simulated displacement field of 3 % compaction -LRB- Table 2 , case b -RRB- , displacement field as predicted by direct CC , displacement field as predicted by decomposition CC ; both cross-correlations were performed at a RD increase of 20.2 % -LRB- Table 2 , case f -RRB- ; vectors were not scaled to absolute displacements , but to optimal visual illustration ; red asterisks indicate vectors based on cross-correlation values below threshold -LRB- 0.5 -RRB- at the highest resolution . Middle the fraction of displacement vectors in the smallest decomposition stage that is calculated correctly -LRB- error in horizontal and vertical direction ≤ 1 pixel -RRB- . This fraction decreased at higher noise levels -LRB- relative dispersion : Table 2 , case c -- h -RRB- . Bottom the average error between calculated and simulated displacement vectors increased for higher noise levels -LRB- Table 2 , case c -- h -RRB- . The decomposition method resulted in a slightly lower number of incorrect vectors , and a large decrease in displacement error Figure 3 shows estimates for matrix compaction for these simulated deformations . The simulated compaction over a disc with 350 pixels radius was 6 % . For noise levels up to 7.6 % -LRB- Table 2 , case d -RRB- , such compaction was indeed found by both correlation methods . At higher noise levels , direct CC underestimated compaction much more severely as compared to decomposition CC . As an example , after a noise increase of 20.2 % , decomposition CC estimated a compaction value of 5.9 % as compared to only 4.1 % for direct CC . Fig. 3Calculated area at a radial distance of 350 pixels after a simulated 3 % compaction . Increasing displacement errors , caused by higher noise levels , resulted in a mismatch between calculated area and simulated compacted area -LRB- 94 % of original -RRB- . The decomposition method showed stable results at larger relative dispersion values -LRB- Table 2 , case c -- h -RRB- When an additional horizontal shift was imposed , both analysis methods correctly estimated compaction for 4.1 % noise -LRB- Table 2 , case i -RRB- . For higher noise levels , decomposition CC succeeded in a correct area assessment . In contrast , at 20.2 % noise and 60 pixels translation -LRB- Table 2 , case j -RRB- , direct CC predicted 1.2 % rather than 6 % compaction of the 350 pixels disc -LRB- see Fig. 4 -RRB- . This effect was not due to more incorrect displacement vectors , but could be attributed to a rise in average displacement error up to 14.6 pixels . Fig. 4Sensitivity of the correlation methods to translation of the image . A horizontal displacement of 60 pixels was imposed after application of a 3 % compaction and noise addition -LRB- 4.1 , respectively , 20.2 % RD increase -RRB- . Top left displacement field for high noise example -LRB- RD increase : 20.2 % -RRB- , as calculated by direct CC . Red asterisks indicate displacements where cross-correlation was below threshold -LRB- 0.5 -RRB- ; at these positions , a large mismatch between calculated compaction and simulated compaction occurs . Top right visual representation of matrix deformation at a radial distance of 270 pixels . Bottom calculated area remaining after a simulated compaction . At low noise levels -LRB- Table 2 , case i -RRB- , a horizontal shift after image resizing did not affect the calculated area ; for high noise -LRB- Table 2 , case j -RRB- , failing correlation in the direct method results in a high offset in calculated area Collagen compaction by individual smooth muscle cells Figure 5 shows an example of collagen matrix compaction by an individual smooth muscle cell . In this particular case , gross geometrical reorganization occurred in the first 6 h of the 22-h observation period . Compaction magnitude decreases with distance , and there was a time delay of around 1.5 h before compaction visible at a radius of 97 μm -LRB- 110 pixels -RRB- became apparent at a distance of 308 μm -LRB- 350 pixels -RRB- . Fig. 5Typical example of collagen matrix compaction by individual smooth muscle cell as estimated by decomposition cross-correlation , obtained at a resolution of 1 frame per 15 min . Top left displacement field represented by vectors , refined in five decomposition stages . Vectors originating from a red asterisk were created at a larger correlation scale ; for the smallest window , cross-correlation value at these positions was below threshold -LRB- 0.5 -RRB- . Top right visual representation of matrix compaction at a radial distance of 350 pixels , as estimated from a series of displacement fields , showing an initial -LRB- yellow -RRB- circle and its deformed -LRB- green -RRB- state . Bottom estimation of compacted area after 22 h at distances of 110 , 230 and 350 pixels -LRB- scaling : 0.88 μm/pixel -RRB- Discussion This study aimed at developing an automated technique for obtaining and analyzing matrix remodeling by individual cells . Emphasis was put on construction of an automatic , reliable algorithm for assessment of a detailed matrix displacement field . Especially , refinement of a cross-correlation based image analysis with a decomposition scheme was investigated . While `` classic '' direct CC sufficed for pairs of images with high correlation and low noise , this was no longer the case when substantial matrix remodeling occurred within the time frame between two consecutive images . This resulted in failing of CC at spots of high geometrical reorganization . However , when using decomposition CC , the gross displacements at these positions could be estimated by analysis of parent images with larger dimensions . This way , at a RD increase of 20.2 % the average displacement error was lowered threefold in decomposition CC as compared to direct CC . The method of refining displacement field accuracy with each decomposition step becomes progressively more important with larger displacements . Therefore we investigated the effect of a horizontal shift superimposed on a simulated compaction . Such shift occurred in our in vitro experiments when a group of neighboring cells pulled strongly on the matrix adjacent to a cell of interest . The shift interfered with direct CC because zero deformation was assumed when no proper correlation could be found . The result was an irregular displacement field -LRB- see Fig. 4 -RRB- . When using decomposition CC , on the other hand , the gross displacement was already detected in the first decomposition stage . Displacement values were then adjusted when local compaction was detected in subsequent decomposition stages . Using this strategy , a lack of local correlation at the finest resolution resulted in only a minor displacement error , with an average of 0.9 as compared to 14.6 pixels for direct CC . Both cross-correlation methods differ not only in stability of displacement field estimation , but in efficiency of calculation time as well . Direct CC requires a large search area , i.e. the subwindows of I 2 have to extend considerably beyond the boundaries of their corresponding I 1 subwindow in order to perform a meaningful correlation . On the other hand , with decomposition CC n cc can decrease with each stage -LRB- see Table 1 -RRB- , since each subwindow of I 2 is repositioned according to the preliminary displacement as calculated for its parent window . Due to the increased number of calculations and data manipulations that have to be performed , analysis time was still about 50 % larger for decomposition CC at the settings as stated in Table 1 . We chose a correlation threshold of 0.5 for both techniques , as well as cross-correlation windows as indicated in Table 1 . These values were empirically determined as an optimum in the trade-off between calculation time , false positive displacements and overlooking local deformations . Clearly , these choices depend on the contrast and texture of the images and the nature of the deformation , and will need to be optimized for specific future experiments . Cell traction is frequently assessed by quantification of deformations in a flexible substratum . Both 2D and 3D approaches have been used , resulting in different cellular morphologies -LSB- 19 , 20 , 29 -RSB- . While the latter seems more physiological , interpretation of the 3D experiments is more complex -LSB- 35 -RSB- . 2D substrates offer more straightforward tools for analyzing mechanical behavior of single cells . These materials can be enriched with fluorescent microbeads to increase image contrast . In order to achieve a high resolution in the vicinity of cells , which can change their morphology rapidly , manual tracking of specific landmarks was employed in several studies -LSB- 6 , 28 , 30 -RSB- . However , this labor-intensive method inherently limits the number of cells under study . In several studies particle tracking was performed on individual microbeads -LSB- 11 -RSB- . In this case , resolution depends on particle distribution , which is typically far from homogeneous in collagen substrates -LSB- 23 -RSB- . Several nested cross-correlation methods have been developed -LSB- 35 -RSB- . All of these algorithms are based on empirical determination of square size , distance for pattern search , and normalized CC threshold . In order to spatially limit the search region , a relative translational shift has been derived from image registration -LSB- 23 , 33 -RSB- . Several schemes have been implemented to increase displacement field resolution in a step-by-step manner . In a single refinement cycle , Dembo and colleagues -LSB- 26 -RSB- decreased the subwindow size if significant displacement was observed . On the other hand , window size can be increased if correspondence failure occurs at high resolution -LSB- 23 -RSB- . The algorithm developed by Wang and coworkers -LSB- 33 -RSB- resembled our decomposition CC to a large extent . However , these authors aimed at construction of traction fields with a smooth nature by incorporation of filtering procedures at several stages . Geometrical matrix reorganization provides a qualitative index of the traction forces present in the underlying material . The actual forces can be derived from a displacement field series using material properties of the ECM construct . However , for collagen scaffolds these are highly heterogenous . Frequently , local stiffness is estimated by either microneedles -LSB- 5 , 10 , 21 , 30 -RSB- or optical tweezers -LSB- 4 , 15 , 32 -RSB- . Tractions can then be calculated by application of stress -- strain relationships , including appropriate boundary conditions -LSB- 7 , 24 , 25 , 31 , 37 , 38 -RSB- . However , this translation to quantitative traction forces lies beyond the scope of this article . In conclusion , we presented integral methodology for the study of matrix remodeling . These techniques allow systematic screening of the role of matrix components such as collagen , elastin , fibronectin and laminin . Likewise , the function of stationary cells like smooth muscle cells , fibroblasts , and osteoblasts can be investigated . Our method can be applied under a wide variety of other experimental conditions . The single requirement for the image quality is a sufficiently high contrast in the material under investigation , without the neeed for laborious and potentially interfering micropatterning . Furthermore , our graphical user interface enables a flexible tuning of parameters such as number of decompositions , size of correlation window , search area and cross-correlation threshold . Using our motorized microscopy stage it is possible to patch individual images together in order to create one large field of view for the study of motile cells such as keratocytes . Finally , the method can be extended with fluorescence imaging of specific cell structures , cytokines , hormones and enzymes -LSB- 15 -RSB- .

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

matrix remodeling
collagen
smooth muscle cell
microscopy
image analysis
deformation