AAPM 2014 Challenges and opportunities in assessment of …amos3.aapm.org/abstracts/pdf/90-25303-339462-103243.pdf · 2014-07-24 · Challenges and opportunities in assessment of

7/21/2014

1

AAPM 2014

Challenges and

opportunities in

assessment of

image quality Ehsan Samei

Department of Radiology

Duke University Medical Center

Disclosures

• Research grant: NIH R01 EB001838

• Research grant: General Electric

• Research grant: Siemens Medical

• Research grant: Carestream Health

7/21/2014

2

Credits

Jeff Siewerdsen, Johns Hopkins

Grace Gang, Johns Hopkins

Guanghong Chen, UW

Ke Li, UW

4 Clinical Imaging Physics Group

Duke CIPG

Imaging quality and safety

• Quality: Imaging provides a clinical benefit

– Diagnostic information

– Image quality: Universally-appreciated

– Enhancement dependent on illusive criteria

• Safety: Imaging involves a level of “cost”

– Monitory cost

– Information excess

– Radiation cost

Optimization involves 4 steps:

1. Reasonable measures of risk

2. Reasonable measures of image quality

3. Justified balance between the two by targeted adjustment of system parameters

4. Consistent implementation

We cannot optimize imaging without quantification

7/21/2014

3

Optimization framework

Safety indices (organ dose,

eff. dose, …)

Quality indices (d’, Az, …)

Scan factors (mAs, kVp, pitch, recon,

kernel, …)

Benefit re

gim

e

Risk re

gim

e

Different patients

and indications

Optimization regime

Outline

1. Quality Characterization

2. Quality assessment

3. Quality implementation

7/21/2014

4

Model-based recons +s

• Decades in the making

• Enabled by high-powered computers

• Significant potential for dose reduction

• Potential for improved image quality

Model-based recons -s

• Speed

• Increased vendor-dependence

• Unconventional image appearance

• Limited utility of prior quality metrics

• Need for nuanced implementation for effective improvement in patient care

ASiR (GE)

Veo (GE)

IRIS (Siemens)

SAFIRE (Siemens)

AIDR (Toshiba)

iDose (Philips)

…

Noise texture?

Low Dose CT @ 114 DLP, 1.9 mSv Images courtesy of Dr de Mey and Dr Nieboer, UZ Brussel, Belgium

FBP Reconstruction

Iterative Reconstruction

FBP IR

7/21/2014

5

courtesy of University of Erlangen, Germany

Iterative

Reconstruction FBP

Reconstruction

courtesy of Mayo Clinic Rochester, MN

FBP

Reconstruction

Iterative

Reconstruction

Low Dose CT @ 338 DLP, 1.8 mSv Images courtesy of Dr de Mey and Dr Nieboer, UZ Brussel, Belgium

FBP

Reconstruction Iterative

Reconstruction

7/21/2014

6

Qualimetrics 1.0: CNR

• 1st order approximation of image quality

• Related to detectability for constant resolution and noise texture (Rose, 1948)

• Task-generic

CNR =QL -QB

s B

Parameters that affect IQ

1. Contrast

2. Lesion size

3. Lesion shape

4. Lesion edge profile

5. Resolution

6. Viewing distance

7. Display

8. Noise magnitude

9. Noise texture

10. Operator noise

Feature of

interest

Image details

Distractors

Parameters that are measured

by CNR

1. Contrast

2. Lesion size

3. Lesion shape

4. Lesion edge profile

5. Resolution

6. Viewing distance

7. Display

8. Noise magnitude

9. Noise texture

10. Operator noise

Feature of

interest

Image details

Distractors

1. Contrast

8. Noise magnitude

?

7/21/2014

7

Why CNR is not enough: Noise texture

FBP IR

Solomon, AAPM 2012

Resolution and noise, eg 1

Comparable resolution

Lower noise but different texture

0 0.2 0.4 0.6 0.8 0

0.2

0.4

0.6

0.8

1

Spatial frequency

MT

F

0 0.2 0.4 0.6 0.8 0

0.5

1

1.5

2

2.5

x 10 -6

Spatial frequency

NP

S

-FBP

-IRIS

-SAFIRE

-FBP

-IRIS

-SAFIRE

Higher resolution Lower noise but different texture

-MBIR

-ASIR

-FBP

0 0.2 0.4 0.6 0.8 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Spatial frequency

MT

F

0 0.2 0.4 0.6 0.8 0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Spatial frequency

NP

S

x 10-5

Resolution and noise, eg 2

-MBIR

-ASIR

-FBP

7/21/2014

8

NPS vs dose

0 0.2 0.4 0.6 0.80

100

200

300

400

500

600

700

800

fxy

(mm-1)

NP

S (

HU

2m

m2)

NPS (Veo)

5 mAs

12.5 mAs

25 mAs

50 mAs

100 mAs

200 mAs

300 mAs

Li, Tang, and Chen, “Statistical Model Based Iterative Reconstruction (MBIR) in clinical CT

systems: Experimental assessment of noise performance,” Med. Phys. (2014)

0 0.2 0.4 0.6 0.80

1

2

3

4

5

6

fxy

(mm-1)

Norm

aliz

ed N

PS

(A

.U.)

Normalized NPS (Veo)

5 mAs

12.5 mAs

25 mAs

50 mAs

100 mAs

200 mAs

300 mAs

Nonlinearity

NPS peak frequency vs dose

0.5 1.25 2.5 5 10 20 300.05

0.1

0.2

0.4

0.8

mAs

Peak fre

quency (

mm

-1)

FBP

FBP (fitting)

Veo

Veo (fitting)

fpeak

(mAs)0.16

Li, Tang, and Chen, “Statistical Model Based Iterative Reconstruction (MBIR) in clinical CT

systems: Experimental assessment of noise performance,” Med. Phys. (2014)

16 33 62 99 224 346 814 1710 25% 50%

75% 100%

0.2

0.4

0.6

0.8

1

DoseContrast (HU)

PS

F w

idth

(m

m)

0.4

0.5

0.6

0.7

0.8

Veo

FBP

Resolution’s joint dependence on contrast and dose

7/21/2014

9

Detectability index

Resolution and

contrast transfer

Attributes of image

feature of interest

Image noise magnitude

and texture

×

dNPWE'( )

2

=MTF 2(u,v)WTask

2 (u,v)òò E 2(u,v)dudv[ ]2

MTF 2(u,v)WTask

2 (u,v)òò NPS(u,v)E 4 (u,v) + MTF 2(u,v)WTask

2 (u,v)Ni dudv

Richard, and E. Samei, Quantitative breast tomosynthesis: from detectability to estimability. Med Phys, 37(12), 6157-65 (2010).

Chen et al., Relevance of MTF and NPS in quantitative CT: towards developing a predictable model of quantitative... SPIE2012

Task-based quality index

Fisher-Hotelling observer (FH)

Non-prewhitening observer (NPW)

NPW observer with eye filter (NPWE)

dFH'( )

2

=MTF 2(u,v)WTask

2 (u,v)

NPS(u,v)òò dudv

dNPW'( )

2

=MTF 2(u,v)WTask

2 (u,v)òò dudv[ ]2

MTF 2(u,v)WTask

2 (u,v)òò NPS(u,v)dudv

dNPWE'( )

2

=MTF 2(u,v)WTask

2 (u,v)òò E 2(u,v)dudv[ ]2

MTF 2(u,v)WTask

2 (u,v)òò NPS(u,v)E 4 (u,v) + MTF 2(u,v)WTask

2 (u,v)Ni dudv

F

Iodin

e c

once

ntr

ation

Size

Task function: Task characteristics for detection and estimation

C (r ) =Cpeak(1-

r

R

æ

èçç

ö

ø÷÷

2

)n

Chen, SPIE 2013

7/21/2014

10

GE MBIR Dose Reduction Potential

Large feature

detection task

FBP

0 1 2 3 4 5 6 7 0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Eff dose (mSv)

AU

C (

NP

WE

)

0.2

mSv

3.7

mSv

ACR Acrylic insert MBIR

78%

20%

Richard, Li, Samei, SPIE 2011

FBP

0 1 2 3 4 5 6 7 0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Eff dose (mSv)

AU

C (

NP

WE

)

Small feature

detection task

0.2

mSv

3.7

mSv

ACR 0.5 lp/mm bar pattern

64%

29%

MBIR

Richard, Li, Samei, SPIE 2011

GE MBIR Dose Reduction Potential

Duke-UMD-NIST study

Parameter GE Discovery CT 750 HD

Siemens Flash Philips iCT

kVp 120 120 120

Rotation time 0.5 0.5 0.5

SFOV 50 50 50

DFOV 25 25 25

Recon Algorithm FBP Standard / ASIR50

B31f / I30f Safire 3

B / iDose5

Recon Mode Helical Helical Helical

Collimation 0.625 0.6 0.625

Pitch 0.984 1 0.93

Slice Thickness 1.25 1 1

Dose levels: 20, 12, 7.2, 4.3, 1.6, 0.9 mGy

For anonymity will be referred to as vendor A, B, and C

Christianson, AAPM 2012

7/21/2014

11

Observer study design

Observer study design 3 scanner models

3 dose levels

2 reconstruction algorithms

10 slices

x 5 repeated exams

Total of 900 images

12 expert observers from two institutions

How much can IR reduce dose?

23% dose reduction

Default clinical protocol at 12 mGy

7/21/2014

12


6% dose reduction



33% dose reduction


CNR vs observer performance

7/21/2014

13

d’ vs observer performance

30 mm

15 mm

20 mm30 mm

33 mm

48.75 mm

64.5 mm

165 mm

6 mm4 mm

2 mm

8% 13%18%

25%

36%36%

25%

18%

TangoPlus

Our contrast detail phantom

0

10

20

30

40

50

60

70

80

90

100

HU

Tan

go B

lack

+

DM 853

0

DM 852

5

DM 852

0

DM 851

5

DM 851

0

DM 850

5

Vero W

hite

DM 843

0

DM 842

5

DM 979

5

DM 978

5

DM 977

0

DM 976

0

DM97

50

DM 974

0

Tan

go +

Vero Black

Vero Blue

Vero Grey

Vero Clear

Tan

go G

rey

RGD 516

0

FC 720

Durus

White

CT images of the low-contrast phantom

B31s

I31s-5

7/21/2014

14

• Siemens SOMATOM Force

• 4 doses (0.7, 1.4, 2.9, 5.8 mGy)

• 2 slice thicknesses (0.6, 5 mm)

• 4 Recons (FBP, ADMIRE 3-5)

Acquired CT data

FBP, STD = 36 HU

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

Spatial Frequency (1/mm)

NP

S (

mm

2H

U2)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.1

0.5

1


TT

F

ADMIRE−3, STD = 24 HU

ADMIRE−4, STD = 20 HU ADMIRE−5, STD = 15 HU

FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

(a)

(b)

(c)

TTF and NPS across recons

FBP, STD = 36 HU

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500


NP

S (

mm

2H

U2)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.1

0.5

1


TT

F

ADMIRE−3, STD = 24 HU

ADMIRE−4, STD = 20 HU ADMIRE−5, STD = 15 HU

FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

(a)

(b)

(c)

CT images across dose and recon Dose

IR S

trength

7/21/2014

15

• Visible groups across:

– Dose (0.74, 1.4, 2.9, 5.8 mGy)

– Slice Thickness (0.6, 1.8, 5 mm)

– Recon (FBP, ADMIRE 3-5)

Insert Counting Experiment

Total number of discernable objects

0.74 1.4 2.9 5.8

0.0

0.0

0.4

1.4

0.0

0.8

1.9

3.2

0.0

0.8

3.0 3.8

0.2

2.0

3.8

5.3

CTDIvol

(mGy)

VIG

sco

re

Slice Thickness: 0.6 mm

0.74 1.4 2.9 5.8

0.0

0.6

3.1

3.6

0.2

2.0

4.6 5

.4

0.1

2.2

5.2

6.0

1.1

3.2

6.0

7.0

CTDIvol

(mGy)

VIG

sco

re


0.74 1.4 2.9 5.8

1.6

4.4

6.2

8.8

2.4

5.2

6.6

9.4

2.6

5.6 6

.6

9.8

2.9

6.1 6

.9

10

.4

CTDIvol

(mGy)

VIG

sco

re

Slice Thickness: 5 mm

FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

0.74 1.4 2.9 5.8

0.0

0.0

0.4

1.4

0.0

0.8

1.9

3.2

0.0

0.8

3.0 3.8

0.2

2.0

3.8

5.3

CTDIvol

(mGy)

VIG

sco

re


0.74 1.4 2.9 5.8

0.0

0.6

3.1

3.6

0.2

2.0

4.6 5

.4

0.1

2.2

5.2

6.0

1.1

3.2

6.0

7.0

CTDIvol

(mGy)

VIG

sco

re


0.74 1.4 2.9 5.8

1.6

4.4

6.2

8.8

2.4

5.2

6.6

9.4

2.6

5.6 6

.6

9.8

2.9

6.1 6

.9

10

.4

CTDIvol

(mGy)

VIG

sco

re


FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

0.74 1.4 2.9 5.8

0.0

0.0

0.4

1.4

0.0

0.8

1.9

3.2

0.0

0.8

3.0 3.8

0.2

2.0

3.8

5.3

CTDIvol

(mGy)

VIG

sco

re


0.74 1.4 2.9 5.8

0.0

0.6

3.1

3.6

0.2

2.0

4.6 5

.4

0.1

2.2

5.2

6.0

1.1

3.2

6.0

7.0

CTDIvol

(mGy)

VIG

sco

re


0.74 1.4 2.9 5.8

1.6

4.4

6.2

8.8

2.4

5.2

6.6

9.4

2.6

5.6 6

.6

9.8

2.9

6.1 6

.9

10

.4

CTDIvol

(mGy)

VIG

sco

re


FBP

ADMIRE−3

ADMIRE−4

ADMIRE−5

0.74 1.4 2.9 5.8

0.0

0.0

0.4

1.4

0.0

0.8

1.9

3.2

0.0

0.8

3.0

3.8

0.2

2.0

3.8

5.3

CTDIvol (mGy)

VIG score Slice Thickness: 0.6 mm

0.74 1.4 2.9 5.8

0.0

0.6

3.1

3.6

0.2

2.0

4.6

5.4

0.1

2.2

5.2

6.0

1.1

3.2

6.0

7.0

CTDIvol (mGy)

VIG score Slice Thickness: 1.8 mm

0.74 1.4 2.9 5.8

1.6

4.4

6.2

8.8

2.4

5.2

6.6

9.4

2.6

5.6

6.6

9.8

2.9

6.1

6.9

10.4

CTDIvol (mGy)

VIG score


IQ metrics vs human performance

0.5 0.55 0.6 0.65 0.7 0.750.4

0.5

0.6

0.7

0.8

0.9

1

AUCCNR

Ob

serv

er

Pe

rfo

rman

ce

FBP

ADMIRE3

y=0.938*x+0.101, R2 = 0.116

0.65 0.7 0.75 0.8 0.85 0.9 0.95 10.4

0.5

0.6

0.7

0.8

0.9

1

AUCSNR

Ob

serv

er

Pe

rfo

rman

ce

FBP

ADMIRE3

y=1.06*x+−0.182, R2 = 0.706

0.55 0.6 0.65 0.7 0.75 0.80.4

0.5

0.6

0.7

0.8

0.9

1

AUCNPW

Ob

se

rve

r P

erf

orm

an

ce

FBP

ADMIRE3

y=1.84*x+−0.494, R2 = 0.725

0.54 0.56 0.58 0.6 0.62 0.64 0.66 0.680.4

0.5

0.6

0.7

0.8

0.9

1

AUCNPWE

Ob

se

rve

r P

erf

orm

an

ce

FBP

ADMIRE−3

y=2.91*x+−1.05, R2 = 0.77

CNR NPWd '2

NPWEd '2

0.5 0.55 0.6 0.65 0.7 0.750.4

0.5

0.6

0.7

0.8

0.9

1

AUCCNR

Ob

serv

er

Pe

rfo

rman

ce

FBP

ADMIRE3

y=0.938*x+0.101, R2 = 0.116

0.65 0.7 0.75 0.8 0.85 0.9 0.95 10.4

0.5

0.6

0.7

0.8

0.9

1

AUCSNR

Ob

serv

er

Pe

rfo

rman

ce

FBP

ADMIRE3

y=1.06*x+−0.182, R2 = 0.706

0.55 0.6 0.65 0.7 0.75 0.80.4

0.5

0.6

0.7

0.8

0.9

1

AUCNPW

Ob

se

rve

r P

erf

orm

an

ce

FBP

ADMIRE3

y=1.84*x+−0.494, R2 = 0.725

0.54 0.56 0.58 0.6 0.62 0.64 0.66 0.680.4

0.5

0.6

0.7

0.8

0.9

1

AUCNPWE

Ob

se

rve

r P

erf

orm

an

ce

FBP

ADMIRE−3

y=2.91*x+−1.05, R2 = 0.77

7/21/2014

16

Task-Based Detectability Extended to quadratic PL image reconstruction

G. Gang et al. Med Phys 41 (2014)

Detectability Map d’(x,y)

0

0.4

0.6

0.2

Line Pair Detection Task

G. Gang et al. Med Phys 41 (2014)

Cylinde

r

Ellipse Thorax

Similar levels of d’ computed via Fourier

and spatial domain approach (~5-15%

agreement)

Applicable to various objects and imaging

tasks.

Local Fourier metrology applicable to

quadratic PL reconstruction, gives a useful

framework for system design and

optimization.

% difference between Fourier

and spatial domain calculation of d’

Ellipse phantom

Justification of Assumptions:

Local Stationarity and Linearity

FBP

PL

Resolution Tumor characteristics

3D Task function (Wtask)

Size, shape, and contrast of

the nodule being quantified

Noise

3D Noise power spectrum

(NPS)

Texture and magnitude of the

noise

3D Task transfer

function (TTF)

System resolution

with respect to the

contrast of the

object and image

noise

222

2

2

( , , ; , ) ( , , )'

[ ( , , ; ) ( , , )] ( , , )

task

i task

TTF u v w C N W u v w dudvdwe

NPS u v w N N u v w W u v w dudvdw

Estimability index (e’): prediction of the

quantification precision capturing the interaction

between the imaging system, the segmentation

software, and the lesion

Internal noise (Ni)

Inconsistency of the

quantification software due

to placement of random

seeds

7/21/2014

17

LUNGMAN,

Kyoto Kagaku, Japan

Empirical precision measurement

LungVCAR

GE Healthcare

Predicted

precision matches

empirical

precision! 2

_2

1 1

1.96 2 /

2.77 /ˆ

( )2.77 /

( 1)

j Wj true

Wj true

ijk ijn Ki k true

PRC V

V

V VV

n K

Outline




7/21/2014

18

Task-based assessment metrology Mercury Phantom 3.0

• Diameters matching population cohorts

• Depths consistent with cone angles

• Straight-tapered design enabling evaluation of AEC response to discrete and continuous size transitions

55

Design: Base material

• Polyethylene

– 80 HU @ 120 KVp

– Near patient equivalent

– Affordable

– Easy to machine

56

Design: Size Pediatric representation percentages

MP 3.0 section

Water size equivalent

Abdomen Chest Head

Age Percentile Age Percentile Age Percentile

120 mm 112 mm 0 12 0 50

0 5 7 5 5.5 5

185 mm 177 mm 6 50 10 50 3 95

15 5 16 5 12 50

230 mm 220 mm

3 95 8 95

- - 12 50 16 50 21 5

300 mm 290 mm 12 95

19 95 - - 21 50

370 mm 355 mm 20 95 - - - - 57

7/21/2014

19

Design: Size Adult representation percentages

58

MP 3.0 section

Water size equivalent

Abdomen Chest Head

M F M F M F

120 mm 112 mm - - - - - -

185 mm 177 mm - - - - 25 75

230 mm 220 mm 0.4 9 0.06 1.4 - -

300 mm 290 mm 27.1 61 14 48 - -

370 mm 355 mm 80 90.3 60 87 - -

59

• Representation of abnormality-relevant HUs

• Sizes large enough for resolution sampling

• Maximum margin for individual assessment

• Iso-radius resolution properties

• Matching uniform section for noise assessment

Design: Resolution, HU, noise

0

1

Non-Stationary Noise and Resolution

MTF

0

2.5 x10-6

NPS The Local NPS and MTF

Noise and resolution model for Penalized Likelihood (PL) model-based reconstruction.* Predictive framework for NPS, MTF, and detectability index (d’) enables task-based design and optimization of new systems using iterative reconstruction.

*Fessler et al. IEEE-TIP (1996) G. Gang et al. Med Phys 41 (2014)

7/21/2014

20

0°

72°

144°

216°

288°

0°

72°

144°

216°

288°

1.15”

1.02” Water

0 HU

Polystyrene

-40 HU

Bone

950 HU

Iodine

8.5 mgI/cc

225 HU

Air

-1000 HU

0.56” 0.53”

Sections 2-5 Smallest Section 1

Water

0 HU

Polystyrene

-40 HU

Bone

950 HU

Iodine

8.5 mgI/cc

225 HU

Air

-1000 HU

Design: Resolution, HU, noise

1.0”

imQuest

HU, Contrast, Noise, CNR, MTF, NPS, and d’ per patient size, mA modulation profile

(image quality evaluation software)

Noise texture effect

0%

10%

20%

30%

40%

50%

60%

70%

80%

10 20 30 40 50 100 150

% N

ois

e R

ed

uc

tio

n F

rom

IR

mAs

Water Water + Sponge Water + Acrylic IR noise reduction:

65% in uniform bkgd

20% in textured bkgd

Solomon, SPIE 2012

7/21/2014

21

Mercury phantom 3.0 Added contrast detail and texture

Generation 1Generation 2Generation 3Generation 4Generation 5Generation 6

Textured lung phantom

Textured soft-tissue phantom: clustered lumpy background*

*Bochud, F., Abbey, C., & Eckstein, M. (1999). Statistical texture synthesis of mammographic images with super-blob

lumpy backgrounds. Optics express, 4(1), 33–42. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19396254

7/21/2014

22

*Bochud, F., Abbey, C., & Eckstein, M. (1999). Statistical texture synthesis of mammographic images with super-blob

lumpy backgrounds. Optics express, 4(1), 33–42. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19396254

Textured soft-tissue phantom: clustered lumpy background*

Anatomically informed heterogeneity phantoms

30 mm

165 mm

30 mm

165 mm

68

Lung texture phantom

30 mm

165 mm

69

7/21/2014

23

Soft-tissue texture phantom

30 mm

165 mm

70

How was the data analyzed?

= +

= STD

Noise in the uniform phantom

0 30 STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

FBP

IR

7/21/2014

24

Noise in the soft-tissue phantom

0 30 STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

FBP

IR

Noise in the lung phantom

0 30 STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

FBP

IR

Comparing noise across textures

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

0 10 20 30

STD (HU)

FBP

IR

7/21/2014

25

What about noise texture?

FBP IR

0 0.2 0.4 0.6 0.8 10

50

100

150

200

250

300

Spatial Frequency (cycles/mm)

NP

S (

HU

2*m

m2)

Lung Texture NPS

FBP: Red Pixels

IR: Red Pixels

IR: Blue Pixels

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

1.2

1.4

Spatial Frequency (cycles/mm)

nN

PS

(m

m2)

Lung Texture NPS

FBP: Red Pixels

IR: Red Pixels

IR: Blue Pixels

Outline




Components of quality assurance

Retrospective performance assessment Quality by outcome

Prospective protocol definition Quality by prescription

System performance assessment Quality by inference

7/21/2014

26

0 2 4 6 8 10 0.5

0.6

0.7

0.8

0.9

1

Eff dose (mSv)

AZ

Feature with no iodine

kV IR optimization

0 2 4 6 8 10 0.5

0.6

0.7

0.8

0.9

1

Eff dose (mSv)

AZ

Feature with iodine

Task Optimal technique % dose reduction (wrt 120 kVp FBP)

No Iodine 80/100 kVp with IRIS 36%

With Iodine 80 kVp with IRIS 40%

ACR phantom

Samei, Richard, Med Phys, in press, 2014

De

tect

abili

ty t

ren

ds

w

ith

do

se/s

ize

80 Smitherman, AAPM, 2014

Protocol optimization

• Setting dose to achieve a targeted task performance for a given size patient

Smitherman, AAPM, 2014

7/21/2014

27

Protocol optimization

• Setting dose to achieve a targeted task performance for a given size patient

Smitherman, AAPM, 2014

PRC: Relative difference between any two repeated

quantifications of a nodule with 95% confidence

Quality-dose dependency Quantitative volumetry via CT

Noise texture vs kernel

GE Siemens

Solomon, Samei, Med Phys, 2012

7/21/2014

28

Texture similarity

Sharpness Solomon, Samei, Med Phys, 2012

CT image quality monitoring

GNI

0

20

40

60

80

100

0 20 40 60 80 100

GN

I

Subtraction Method

y = x R2 = 0.998

Christianson, AAPM, 2014

Noise per slice FBP

ASiR

7/21/2014

29

0

5

10

15

20

25

30

35

40

45

50

20 30 40

GN

L (H

U)

Effective Diameter (cm)

Scanner 1

Scanner 2

Scanner 3

0

5

10

15

20

25

30

35

40

45

50

20 30 40

SSD

E (m

Gy)


Scanner 1

Scanner 2

Scanner 3

Trends in dose and noise

Abdomen Pelvis Exams (n=2358)


0

5

10

15

20

25

30

35

40

45

50

20 30 40

GN

L (H

U)


Scanner 1

Scanner 2

Scanner 3

0

5

10

15

20

25

30

35

40

45

50

20 30 40

SSD

E (m

Gy)


Scanner 1

Scanner 2

Scanner 3

Trends in dose and noise


SSDE varies with size


0

5

10

15

20

25

30

Scanner 1 Scanner 2 Scanner 3

SSD

E (m

Gy)

0

5

10

15

20

25

30

Scanner 1 Scanner 2 Scanner 3

GN

L (H

U)

Variability in dose and noise

Target noise level

ACR DIR

25th -

75th



7/21/2014

30

Conclusions

• New technologies necessitate an upgrade to

performance metrology towards higher degrees of

clinical relevance:

• Physical surrogates of clinical performance

• “Taskful” metrology and dependencies

• Incorporation of texture into image quality

estimation

• Extension of quality metrology to quality

monitoring and quality analytics

Thank you!

Documents

AAPM 2014 Challenges and opportunities in assessment of …amos3.aapm.org/abstracts/pdf/90-25303-339462-103243.pdf · 2014-07-24 · Challenges and opportunities in assessment of