Optimisation of Computed Radiography chest imaging utilising a digitally

reconstructed radiograph simulation technique

Craig MooreRadiation Physics Service

Hull & East Yorkshire Hospitals NHS Trust

www.hullrad.org.uk

Introduction - Literature• Lots of publications have shown that patient

anatomy is the limiting factor in the reading normal structures, and detection of lesions (lung nodules) in chest images– Bochud et al 1999– Samei et al 1999, 2000– Burgess et al 2001– Huda et al 2004– Keelan et al 2004– Sund et al 2004– Tingberg et al 2004

• European wide RADIUS chest trial (2005)

Introduction - Literature• Chest radiography is

now generally considered to be limited by the projected anatomy

• Patient anatomy = anatomical noise

So if we want to optimize digital system for chest imaging, vital that anatomical noise is present in the images!!!

Introduction

• However, the radiation dose/image quality relationship must not be ignored

• Doses must be kelp ALARP– ICRP 2007– IR(ME)R2000 – (required legally in the UK)

• We would therefore want system (quantum) noise present in an image for dose reduction studies

Digitally Reconstructed Radiograph (DRR)

• Hypothesis:– Can use CT data of humans to provide realistic

anatomy (anatomical noise)• Clinically realistic computerized ‘phantom’

– Simulate the transport of x-rays through the ‘phantom’ and produce a digitally reconstructed radiograph (DRR – a simulation of a conventional 2D x-ray image created from CT data)

– Add frequency dependent system noise post DRR calculation

– Add radiation scatter post DRR calculation– Validate– Use for optimization studies

DRR Algorithm: Virtual Patient• Virtual patient derived from chest

portion of real CT datasets• Voxel resolution = 0.34 x 0.34 x 0.8

mm• CT number converted to linear

attenuation coefficient (LAC) using tissue equivalent inserts

– Measure mean CT No. in each insert

– We know elemental composition of each so can derive LAC

– Can derive relationship between CT No. and LAC

PA slice 1

PA slice NCT axial batch 1 (i.e. CT slices 1

to 20)

CT axial batch 2 (i.e. CT slices 21

to 40)

Final CT axial batch (i.e. slices

681 to 700)

CT dataset re-orientated in

the ‘PA’ direction

X-ray spectra derived from

IPEM 78

X-ray attenuated exponentially through CT

dataset using a ray casting

method of DRR calculation

dExEEIEAE

en

max

00 })(exp1){(

Energy absorbed in CR phosphor

Intensity of X-rays exiting is calculated

Scatter Addition• Must add to DRR generated image

– DRR algorithm does not calculate scatter

• Measured scatter in CR chest images using lead pellet array

• Use chest portion of RANDO phantom

Noise Addition

Uniform noise image Corrected noise image

lung

spine

diaphragm

Based on slightly modified work by Bath et

al, 2005

Lung Nodule Simulation• Added artificial nodules to the CT data prior to X-

ray projection• Baed on work by Li et al 2009

Human DRR v Human CR

DRR CR

DRRs: 50 kVp v 150 kVp

Scatter Rejection• The DRR algorithm can produce images with scatter rejection

(b) (c)(a)

DRR DRR CR

DRR No Rejection DRR Grid CR Grid

Obese Patients

• DRR algorithm can also produce images of large/obese patients

(a) (b)

DRR CR

Validation

• Decided to validate with RANDO and real patient images:– Histogram of pixel values– Signal to noise ratios (SNR)

• Important because signal and noise affects the visualisation of pathology

– Tissue to rib ratios (TRR)• Pixel value ratio of soft tissue to that of rib• Important as rib can distract the Radiologist from

detecting pathology

Phantom HistogramsCR - 60 kVp 10 mAs

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20000

Pixel Value

Freq

uenc

y

DRR - 60 kVp 10 mAs

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Pixel Value

Freq

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y

a

b

CR - 150 kVp 0.5 mAs

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DRR - 150 kVp 0.5 mAs

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c

d

CR - 90 kVp 1 mAs

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DRR - 90 kVp 1 mAs

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CR - 90 kVp 4 mAs

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DRR - 90 kVp 4 mAs

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Phantom Histograms

PATIENT - HistogramsDRR Histogram

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1744 1995 2246 2497 2748Pixel Value

Freq

uenc

y

Patient CR Histogram

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60000

1733 2012 2291 2570 2849Pixel Value

Freq

uenc

y

Typical histogram of average patient DRR

Typical histogram of average patient CR image

SNRs• Good agreement in lung, spine and diaphragm

areas of chest• Maximum deviation approx 15%

– Mean deviation = 7%• Addition of frequency dependent noise not

perfect:– CR system noise is ergodic (changes with time)– Noise added here is a snapshot (and so not ergodic)– However, quantum noise dominates over ‘ergodic

noise’ so not such an issue– DQE is NOT constant with dose variation in image

Validation - TRRs

• Good agreement– Within 2%

• As tube potential increases TRR decreases– Due to rib attenuating higher percentage in

incident photons at lower potentials than soft tissue, thus forcing up TRR

Validation - Radiologists• Have told me DRR

images contain sufficient clinical data to allow diagnosis and subsequent optimisation

• They have scored the images out of 10– ‘are the images sufficiently

like real CR images?– Average score of 7.8

Conclusions – DRR Algorithm• DRR computer program has been produced that adequately

simulates chest radiographs of average and obese patients– Anatomical noise simulated by real human CT data– System noise and scatter successfully added post DRR generation that

provides:• SNRs• TRRs • Histograms

– in good agreement with those measured in real CR images• Provides us with a tool that can be used by Radiologists to grade

image quality with images derived with different x-ray system parameters

• WITHOUT THE NEED TO PERFORM REPEAT EXPOSURE ON PATIENTS

• ACCEPTED FOR PUBLICATION IN THE BJR – AUGUST 2011

Optimisation of CR Chest Radiography using DRR Generated Images

• In Hull chest exposure factors were not standardised (historical reasons!!!!)

• Three main hospital sites:– 60 kVp & 10 mAs– 70 kVp & 5 mAs– 80 kVp & 5 mAs

• In the last 6 months, four expert image evaluators have scored DRR reconstructed images – Two Consultant Radiologists– Two Reporting Radiographers

• Scoring criteria based on European guidelines (CEC)

What to Optimise?

• Optimum tube potential for Average Patients (70 kg ± 10 kg)– Without scatter rejection (as per Hull protocol)– With scatter rejection (grid and air gap)

• Optimum tube potential for Obese patients– Without scatter rejection– With scatter rejection

• Is Scatter rejection indicated?• Dose reduction?

Scoring Criteria• Images scored on a dual PACS monitor system • Image on right hand screen held at a constant

kVp • Images on left hand screen displayed from 50 to

150 kVp in steps of 10kVp (approx)– Image 1 = 50 kV– Image 2 = 60 kV– Image 10 = 150 kV

• Test images scored against reference image• All images matched effective dose

GRADING VISIBILITY OF STRUCTURE

-3 Definitely inferior to the reference image

-2 Reasonably inferior to the reference image

-1 Slightly inferior to the reference image

0 Equal to the reference image

+1 Slightly better than the reference image

+2 Reasonably better than the reference image

+3 Definitely better than the reference image

[1] European guidelines on quality criteria for diagnostic radiographic images. CEC European Commission EUR 16260 EN (Luxembourg 1996)

STUCTURE – NORMAL ANATOMY GRADING

IMAGE 1 IMAGE 2 IMAGE 3 IMAGE 4

Vessels seen approx approx 3 cm from the pleural margin

Thoracic vertebrae behind the heart

Retrocardiac vessels

Pleural margin

Vessels seen en face in the central area

Hilar region

STUCTURE – LUNG NODULES GRADING

Nodule in lateral pulmonary region

Nodule in hilar region

Are the ribs a distraction? Y/N

Tube Potential Optimisation

Results – Average Sized Patients, no scatter rejection

VGAS = average of Radiologists results

0

0.1

0.2

0.3

0.4

0.5

0.6

40 60 80 100 120 140 160

Tube Potential kVp

VGA

S

`

Results – Average Patients with scatter rejection - grid

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0 20 40 60 80 100 120 140 160

Tube Potential (kVp)

VGAS

Results – Average Patients with scatter rejection – air gap

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

40 60 80 100 120 140 160

Tube Potential (kVp)

VGAS

Results – Obese Patients without scatter rejection

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0 20 40 60 80 100 120 140 160

Results – Obese Patients without scatter rejection

• Very weak trend for better image quality with higher kVp• Lower kVps probably ‘worse’ due to combination of:

– Lack of penetration through obese patient– Increased scatter from obese patient (scatter to cassette

changes very little with kVp)• This is not so with average patients

– Less tissue (fat) so more radiation penetration– Less scatter from fat

• It is likely that poorer radiation penetration and increased scatter from obese patient outweighs the inherent benefit of photoelectric contrast obtained from lower kVps

Results – Obese Patients with scatter rejection - grids

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

40 60 80 100 120 140 160

Tube Potential (kVp)

VGAS

Results – Obese Patients with scatter rejection – air gap

-1.8

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

40 60 80 100 120 140 160

Scatter Rejection

Scatter Rejection vs no scatter rejection – average patients

0

0.2

0.4

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0.8

1

1.2

1.4

grid air gap

Scatter Rejection Technique

VGAS

Scatter Rejection vs no scatter rejection – average patients

• Superior image quality with scatter rejection technique

• Grids performed much better than air gap• Statistically significant differences• BUT:

– Image evaluators were asked if increase in dose due to use of grids was justified, even with better image quality

– Answer in 100% of cases was NO– SCATTER REJECTION FOR AVERAGE PATIENTS

IS NOT INDICATED

Exposure time for average patients without scatter rejection and low tube potentials?

• Scatter rejection not indicated• Therefore low kVps should be used (remember the

graph)• European guidance recommends exposure times < 20

ms• Can we achieve this with low kVps??• Modern Philips X-ray generator:

– For lgM = 2 (Agfa CR specific Dose Indicator)– With 630 mA, all kVps are possible – Max exp time = 16 ms

• At the expense of increased tube loading

Scatter Rejection vs no scatter rejection – obese patients

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

grid air gap

Scatter Rejection Technique

VGAS

Scatter Rejection vs no scatter rejection – obese patients

• Superior image quality with scatter rejection technique

• Grids performed much better than air gap• Statistically significant differences• BUT:

– Image evaluators were asked if increase in dose due to use of grids was justified, even with better image quality

– Answer in 100% of cases was YES– ANTI SCATTER GRID USE FOR OBESE PATIENTS

IS INDICATED

Exposure times for obese patients with an anti-scatter grid

• Remember that low tube potentials were superior with an anti-scatter grid!!!!

• Need exposure times < 20ms• Is this possible with low kVps and scatter grid for lgM =

2??• Scatter grid is a focused grid so have to use it in a fixed

range of FDD– Nominal distance = 140 cm FDD– Range allowed = 115cm – 180cm

• At 140 cm FDD lowest exp time = 17.6ms @ 109 kVp• At 115cm FDD lowest exp time = 20 ms @ 90 kVp• So are limited to 90 kVp

Dose Reduction

Dose Reduction?

• Images were also presented at different doses

Dose reduction?

• Results suggest doses can be reduced by around 50% before image quality begins to suffer and become unacceptable

• Therefore could half exposure mAs

Conclusions – Use of DRR algorithm to optimise CR chest

imaging• Average patients:– No scatter rejection is indicated– Therefore, low kVps (< 102 kVp) are indicated – Can have exposure times < 20 ms for all kVps

• Obese patients– Anti-scatter grid is indicated– So low kVps (<102kVp) should be used– For exposure times < 20ms, are limited to 90 kVp

• Doses– As low as 50% reduction possible

• ACCEPTED FOR PUBLICATION IN BJR

Optimisation & Standardisation in Hull?

• Agreed with Consultant Radiologists:– 60 kVp & 10 mAs

• After a ‘settling in’ period:– 60 kVp & 8 mAs– Want to go lower than 8mAs eventually!!!

• Implication on patient dose?– Using PCXMC effective dose calculation software:– 80 kVp/5 mAs = 0.011 mSv– 60 kVp/8 mAs = 0.006 mSv– Approx 45% drop in effective dose