image quality – quantifying quality? · 2015-10-06 · image quality –quantifyingquality?...

Image Quality – Quantifying Quality?

Stephan Scheidegger,2015

Image Quality – Quantifying Quality?

Contents

Motivation Mathematical representation

of image and imaging process From quality to quantity? Low contrast detectability:

Contrast, SNR, CNR, Noise characteristics: NPS High contrast detectability:

MTF CDC, DDC Model observers – observer

models Phantoms

RO

ENTG

ENTE

CH

NIK

STR

AHLE

NBI

OLO

GIE

GR

UN

DLA

GEN

RA

DIO

LOG

IE

STR

AH

LEN

PH

YSI

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Why are we trying to measure Image Quality?

Different reasons – different tasks:

Performance: system suitable for clinical rasks?Clinical relevant quality (Model Observer)

Optimisation: system working at optimal point (ALARA)?Comparison of quality with applied dose (e.g. DDC with CTDI)

Quality control: Change in systems performance?Comparison of a measure representig systems performancewith the base line (e.g. DQE, NPS, MTF etc.)

Mathematical Image Representation

Different concepts (models):

2Dim.‐Functions (continuous models) Matrices (discrete representation – pixel‐model) Vector‐representation (Dim.=k x l x 1 resp. k x l x 3 for RGB)

( , ) ( , , )

...( , ) ( , )( , )

I x y t O x y z t

I x y F P x yS u v FFT F FFT P

A ( , , )

...

( , ) ( , )

H

kl

kl k lA

I O x y z

I P x y W x x y y dx dy

I S N

M

From Quality to Quantity?

technicalclinicalObserverimpression

numbers

MTF, NNPSDQE, SNRCNR

CDC / DDC

AlternativeForce Choice AFC

Visual GradingAnalysis VGA

Receiver OperatingCharacteristics ROC

Line pair TO

Image Quality – Image Characteristics

Qualities to quantities

Scharpness (spatial resolution, esp. High‐contrast resolution)

Gray level (distribution), dynamic range

Contrast Noise … others like uniformity, lag &

ghosting

Image Signal – Gray Levels, Blackening & Co.

Image signal representation

Blackening in film‐screen systems: optical density

Gray level displayed on a monitor (8 or 10 bit resolution, 256 – 1024 levels)

Gray level stored in a imagefile (12 bit – 4096 levels)

CT numbers, HU (12 bit, extended 12 bit)

Image Signal – Gray Levels, Blackening & Co.

Gray level – optical density

By definition adapted to visualprocess (and range!)

II

D refopt log

0.5

1.0

2.0

3.0

Image Signal – Visual Contrast Detection

Visual range & «contrastresolution»

Visual range approx. 10 bit(env. 900 gray scales)

Thresholds!

(1) (2)

max min

max min

(Michelson)

opt opt

nm kl

m

C D D

C I II ICI I

Image Signal – Visual Contrast Detection

LC detectabilityContrast C

Contrast & Dose: Gradation Curve

11000

optDDGyS


usablerange

Gradation Curve

☼ flat☼ steep


Image range=usablerange?

Gray Scale to Dose: CR System

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

0.0018

0.002

2000 2500 3000 3500 4000

x (12bit)

c / m

Gy/

x(12

bit)

cf (meas)cf (calc)

Good contrast?

Image Quality and Optimisation

1

1

, ,...( ; ,...)( ; )

PMMA PVCHU HUSNR HU kVCNR HU kV

1 1( ), ( )c kV w kV

Parameter Set 11kV Parameter Set 2

2kV

2

2

, ,...( ; ,...)( ; )

PMMA PVCHU HUSNR HU kVCNR HU kV

1

1

( ; ,...)( ; )

SNR I kVCNR I kV

2

2

( ; ,...)( ; )

SNR I kVCNR I kV

2

2

( ),( )

c kVw kV

Low‐Contrast‐Detectability: Signal – to – Noise Ratio SNR

Sources of noise:

Quantum noise Detector noise More clinical: «decision

noise!» Often used: additive noise

model: IN(x,y) = I(x,y) + N(x,y)

1 1

1 ( , )

( ) ( )

N M

n mn m

nm nm

HU x y HUN MSNRs HU s HU

Noise level 20%, r = 2 pixel



Dose-dependence of noise(CR-system)

1 1

1 ( , )N M

N M n mn m

R I x ys N M

0

20

40

60

80

100

120

0 0.5 1 1.5 2 2.5

Dose / mGy

R(10

1x10

1)

73 kV90 / 125 kV

(125 kV)

CT: Tube current and noise

Low‐Contrast‐Detectability: Contrast – to – Noise Ratio CNR

Simple approach: Difference of SNR in two compared ROI’s

Usefull for relative signaldetection with threshold?

2 112

, 2 , 1( ) ( )Pos Pos

nm Pos nm Pos

HU HUCNR SNR

s HU s HU

Low‐Contrast‐Detectability: Contrast – to – Noise Ratio CNR

CNR = ‐0.007

CNR = 0.03

Is CNR a usefull quantity?

upper threshold

lower threshold

signal

upper threshold

lower threshold

signal

Increasing noise

+ Noise Stochastik Resonance!

Nosie Characteristics?

Noise Characteristics CT

Miéville et al (2012): Effects of computing parameters and measurement locations…Phys Med

Marshall N: The diagnostic Xray perspective, UZ Leuven

Noise Characteristics: NPS

High‐Contrast‐Resolution

Observer‐based vs. calculated:

Line pairs (lp / mm) Modulation transfer

function MTF

HC resolution(lp / mm)

a) 73kV 32mAs b) 90kV 8mAs

Modulation Transfer Function MTF

Results without Noise• ROI with high contrast edge:

no filter (1); r = 0.5 p (2); r = 1 p (3); r = 2 p (4); r = 4 p (6); r = 12 p (8)

MTF high C

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

lp / pixel

MTF

Reihe1Reihe2Reihe3Reihe4Reihe5Reihe6Reihe7Reihe8

Gaussian filter r = 12 pixel

initial

Gaussian filter r = 4 pixels

-50

0

50

100

150

200

250

300

-8 2 12 22 32

Position / pixel

Gre

y Va

lue


MTF high C

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

lp / pixel

MTF


MTF high C

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

lp / pixel

MTF


MTF(50)

Modulation Transfer Function MTF

Results with Noise• Gaussian filter with r = 0.5 pixel, no

noise (blue), 10% Gaussian noise (yellow), 20% Gaussian noise (pink)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.2 0.4 0.6 0.8 1

Results with Noise• ImageJ: MTF with no noise vs.

MTF with noise, at high contrast level: (a) Gaussian filter with r = 4 pixels, noise level 5%; (b) Gaussian filter with r = 0.5 pixel, noise level 20%; (c) Gaussian filter with r = 0.5 pixel, noise level 10%

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.2 0.4 0.6 0.8 1

00.10.20.30.40.50.60.70.80.9

1

0 0.5 10

0.10.20.30.40.50.60.70.80.9

1

0 0.5 1

Optimisation of CR-Systems

b) 73kV 2mAsa) 73kV 32mAs c) 90kV 8mAs d) 125kV 4mAs

Optimisation of CR Systems

Modulations-Transfer-Funktion

0

0.5

1

1.5

2

2.5

3

3.5

4

0 0.5 1 1.5 2 2.5

Dose / mGy

MTF

/ lp

/mm

MTF50MTF80

1.0

0.5

0.3

0.8

5 10 15 2000.0

Bildfrequenz / (lp/mm)

MTF

2( ) ( ) ixMTF L x e dx

Modulation Transfer Function MTF: Iterative Reconstruction total kk

MTF MTF

MTF 50Abdomen (Siemens Force Abdomen 2.0 Br36 3) 0.227Abdomen (Siemens Force Abdomen 2.0 Br36 4) 0.239Abdomen (Siemens Force Abdomen 2.0 Br36 5) 0.2435

Richard et al. (2012): Towards task‐based assessment of CT performance. Med Phys

Alternative Approach

Images:[1]

Contrast‐detail curve [1, 2, 3]


Images:[1]

Thorax Protocol @ 120 kV B70s

92.5mA/7.62CTDI

69mA/5.83CTDI

58.75mA/4.93CTDI

No Window Window C40 W300

Window C300 W1500

Images using different windows

Pelvis Protocol@

80 kV

100 kV

No Window Window C40 W300 Window C300 W1500

Aproach for obtaining CDC and DDCRegistration

CT ImageTemplate Image

Approach for CDC and DDCPaired point matching using ICP

Affine TransformationT

Model Observers – Observer Models

Aims

Standardized observer To mimic psychophysiological aspects of recognition To cover image quality close to the clinical need

1 0

0 1

0

1

2 2

: H: H( )

; 1 12 2

b

b s

H Htt

H H

HH

t tt SNR

I I NI I I N

T I

Choosing the «Right Phantom» … ?

image quality – quantifying quality? · 2015-10-06 · image quality –quantifyingquality?...

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