DR system:
How to find the best hardware and software to
achieve minimum dose and optimal image quality
Radiographer, M.Sc. Health: Helle Precht & PhD: Oke Gerke
Denmark
RC 1414 - Paediatric imaging
Agenda
Canons indirect DR system:

DR hardware

Project focusing on scintilator sensitivity in paediatrics

Possibilities in software processing (Multi Frequency Processing)

Optimization

Project focusing on possibilities to grade dose and image quality in
relation to the type of the paediatric examination
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Cooperation between:
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DR hardware
DR Hardware

Connection to generator

Computer capacity

Connection to RIS/PACS systems

Touch screen

Diagnostic monitors

Detector
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DR hardware
DR detector

Stationary/portable ~ Wireless

DQE and MTF

Pixel size and fill factor


Fig.: DR detector (Bushong, 2004)
Fill factor = Light sensitive area/detector area
Scintilator – line spread function
GOS
CsI
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Scintilator project
Project focusing on
scintilator and scoliosis
Background
•
•
•
•
•
•
Scoliosis pathology
Human radiation response in relation to patient age
Tissue weighting factor and contact shield
ALARA
Technique: high kV, airgap and stitching
GOS and CsI scintilator
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Scintilator project
Hypothesis
A Canon detector with CSI scintilator will
produce acceptable image quality at a
scoliosis examination at lower dose than a
Canon detector with GOS scintilator
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Scintilator project
Method
 Theory supported by published articles, books and
information by Canon
 Quantitative experimental design
 Canons CXDI 50G and 50C detector
 Human phantom (audit)
 Dosimeter (DAP and ESD - Unfors)
 Monte Carlo dose calculations
 Statistics
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Scintilator project
Results
200
350
180
300
160
250
120
200
REX
DAP mGy cm^2
140
100
150
80
60
100
40
50
20
0
0,5
0,6
0,8
1
1,2
0
1,6
DAP 1 GOS
2
0,5
2,5
3,2
0,6
0,8
mAs
DAP 2 GOS
4
1
DAP 1 CsI
6,3
1,2
10
1,6
12,5
2
16
2,5
20
3,2
25
4
6,3
10
12,5
16
20
25
mAs
DAP 2 CsI
REX 1 GOS
REX 2 GOS
REX 1 CsI
REX 2 CsI
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Scintilator project
Analyzing image quality
Participants: three radiologists and three reporting radiographers
Every image was scored according to:
No.
Def.:
1
2
3
Too low SNR
Acceptable SNR
High SNR
Reduced spatial
Acceptable spatial
High spatial resolution
resolution
resolution
Image criteria not
Image criteria barely
Image criteria above
met
met
requirements
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Scintilator project
Results
3
Radiographers
2
1
CsI
0
20
16
CsI
GOS
25
1
12,5
2
10
3
CsI
CsI
mAs
GOS
0,5
0,6
0,8
1
1,2
1,6
2
2,5
3,2
4
6,3
10
12,5
16
20
25
0
0,5
0,6
0,8
1
Number of score
1,2
1,6
2
2,5
3,2
4
6,3
Number of score
Radiologists
mAs
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Scintilator project
Technical measurements of
sensitivity at different kV levels
CsI, CXDI 50C
GOS, CXDI 50G
Fig.: CsI and GOS scintilators signal reinforcement at different kV values.
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Scintilator project
Conclusion
The REX value can be used as an objective indicator of image quality based on the
indication for the examination. Dose and scintilator amplification degree affect REX
value.
Based on the experiments the hypothesis is confirmed:
The CsI detector can at 2 mAs produce an acceptable image quality, where GOS does
not produce comparable image quality until 6,3 mAs. This confirms the theory about
the CsI detectors DQE and higher REX value compared to the GOS detector at all mAs
levels.
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Scintilator project
Perspectives
 Other DR products?
 Technical phantom for more objective results
 Use of other modalities - CT, MRI or UL?
 Software optimization
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Software processing
Pre - and post processing
Fig.: Process in production of image data (Canon Inc., 2008a).
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Software processing
Automatic histogram adaptation
Fig.: Exposure recognition (Canon Inc., 2008b)
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Software processing
LUT curves in Canons DR system
Bone#1
Bone#2
Chest
Standard
Inv Linear
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Software processing
Index value: REX - ROI
ROI
Fig.: Basis for REX calculation (Canon Inc., 2001).
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Software processing
Example using a human phantom
REX: 257
REX: 641
REX: 655
REX: 4747
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Software processing
Guide for software optimization
Turn off all the functions of MLT(S)
Set LUT
Adjust ROI Contrast
Max. density region is dark
Min. density region is bright
Graininess needs to be reduced
Sharpness is not enough
Adjust Dynamic Range – Dark Region
Adjust Dynamic Range – Bright Region
Adjust Effect of Noise Reduction
Adjust Frequency Band
Adjust Effect of Edge Enhancement
Fig.: MLT(S) flow chart (Canon Inc., 2008a; Canon Inc., 2008b)
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Software processing
Dynamic range
Bright region
λ
Dark region
λ
Fig.: Technical illustration of MLT(S) compression (Canon Inc., 2008a).
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Software processing
Example using a human phantom
Dynamic range, Dark region
1
20
Dynamic range, Bright region
1
20
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Software processing
Contrast – local and global
Fig.: Local and global contrast (Canon Inc., 2008a).
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Software processing
Example using a CD Rad phantom
1
30
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Software processing
Example using a human phantom
1
30
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Software processing
Frequencies
Fig.: The building and function of the laplacian pyramid (Vuylsteke, Schoeters, 1999).
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Software processing
Edge enhancement
Fig.: Unsharp masking (Gonzales, Woods, 2008).
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Software processing
Example using a CD Rad phantom
Effect: 1
Frequency: 1
Effect: 20
Frequency: 7
Frequency: 4
Effect: 20
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Software processing
Example using a human phantom
Frequency: 1
Frequency: 7
Effect: 20
Effect: 1
Effect: 20
Frequency: 4
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Software processing
Noise reduction
Fig.: The principle behind low pass filtration (Gonzales, Woods, 2008)
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Software processing
Example using a CD Rad phantom
1
10
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Software processing
Example using a human phantom
1
10
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Software project
Project focusing on
software optimization
Background

Survey of optimization level with Canon’s European Application Group

New MLT(S) software – new possibilities within Radiography?

Lack of research in Paediatric Radiography within the software optimization

Are we in accordance with national and international standards?

Do we use the full potential of DR systems?

Radiographer’s job description
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Software project
Survey (Questionaire)
Implementation - dose
6
5
4
3
2
1
0
Cause of missing optimization
Number of answers
6
5
4
3
2
1
0
Number of answers
Number of answers
Implementation - image quality
8
7
6
5
4
3
2
1
0
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Software project
Hypothesis
Hyp.1:
With Canon's new MLT(S) software one can maintain
optimal image quality at lower mAs in paediatric
examinations of the femur.
Hyp.2:
If the pathological focus at a femur examination is
changed from primary to follow-up examination of a
fracture, it is possible to reduce mAs more than the
achieved mAs value from hyp. 1 using MLT(S).
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Software project
Optimization
Current practice
Adjust practice
and formulate
possible new
criteria for
good practice
Compare practice
with criteria for
good practice
Point out deviation
Fig.: Quality development as a dynamic process (Kjærgaard, 2001)
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Software project
Optimization in Radiography
1.
Set of standards, always based on an
anatomical background
2.
Make sure that these live up to image quality
(diagnosis) and dose demand (reference dose)
3.
Optimize low performing practices
4.
Set/develop new standards
5.
Repeat
(European Commission, 1996a; Båth et al., 2005)
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Software project
Which examination to start optimizing?
The following examinations should be given priority in
the optimization of paediatric examinations:
Acquisition leading to repeated radiation
 Acquisition that gives high radiation dose
 Acquisition involving radiation sensitive area
(ICRP, 2006; ICRP - annals of the ICRP, 2004).
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Software project
As Low As Reasonable Achievable
(ALARA)
diagnostic
information
Dose saturation
dose
Threshold value for diagnostic information
Fig.: Dose draft on diagnostic information (Norrman, 2007)
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Software project
Method
 Theory supported by published articles,
books and information by Canon
 Quantitative experimental design
 Technical phantom (CD Rad)
 Human phantom (VGA analysis)
 mAs and software settings are variable
 Monte Carlo dose calculations
 Statistics
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Software project
Phantoms
Technical CD Rad phantom with water absorption
Human lamb phantom
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Software project
Technical parameters used
in the experiments
Fixed parameters:
 60 kV
 Total filtration: 4,2 mm Al
 SID: 100 cm
 LUT: Bone#1
 Collimation: 42x42 cm and 26x13 cm
Variable parameters:
 16-0,5 mAs
 Software parameters
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Software project
Software settings
Software processing
Contrast
Dynamic range, Dark Region
Settings
16
10
23
13
29
16
20
Noise reduction
5
7
10
Edge enhangement, frequency band
1
3
5
Edge enhancement, effect
1
4
7
10
Table: Applied MLT(S) parameters in CD Rad tests
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Software project
Analysis of CD Rad images
Fig.: CD Rad analyser (Artinis, 2006)
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Software project
Analysis of human phantom images:
1st hypothesis
1
VGA
Visualization of age appropriate trochanter, femoral head, medial and
lateral condyle and femur bone
2
Visualization of periarticular soft tissue level
3
Sharpness of the demarcation between cancellous bone and compact
bone
4
Sharpness of trabecular
Table: Image criteria on femur images (Bontrager, 2002; European Commission, 1996b)
-2
Clearly worse than the reference image
-1
A little worse than the reference image
0
Comparable with the reference image
+1
A little better than the reference image
+2
Clearly better than the reference image
Table: Relative VGA scale for scoring image quality (Almén, et al, 2000).
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Software project
Analysis of human phantom images:
2nd hypothesis
1
VGA
Visualization of the fracture bone ends of the
femur and their position
Table: Image criteria for control exposure of femur AP.
1
Not visible
2
Poorly reproduced
3
Well reproduced
4
Very well reproduced
Table: Absolute VGA scale for scoring image quality (Almén, et al, 2000).
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Software project
Results
S-4
S-10
2 mAs
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Software project
Results
Radiologist
Not visible
Poorly
represented
Well
represented
Very well
represented
1
0
56 (46.67%) 56 (46.67%)
2
0
14 (11.67%)
78 (65%)
28 (23.33%)
3
0
1 (0.83%)
33 (27.5%)
86 (71.67%)
8 (6.66%)
Table: Frequency table on the radiologist’s score, number (%), of the image criteria
for each of the four scoring possibilities within the hypothesis.
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Software project
Statistical results
Significant factors by 1th hypothesis:
• mAs
• dynamic range, dark region
• frequency band
Significant factors by 2nd hypothesis:
• mAs
• dynamic range, dark region
• frequency band
• edge enhancement effect
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Software project
Bias
1. During the CD Rad tests the use of two images at each adjustment of
MLT(S) parameters and dose was an absolute minimum, the
recommendation is six identical images.
2. Calculation of applied water phantom as an absorber to the CD Rad
phantom.
3. Use of a lamb phantom; the difference to human anatomy is natural.
4. Size and absorption of the human phantom was larger than femur of a
five-year old child.
5. Manually placing ROI and it’s influence on the REX value.
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Software project
Conclusion
Distinction between optimal and diagnostic image quality.
Based on the experiments both hypothesis is confirmed:
Optimal image quality is obtained at a dose reduction of 70 % from 16 to 5 mAs with
MLT(S) optimized images. Specifically optimized images are approved at 2 mAs, but
the radiologists VGA scores are worse than the reference image (diagnostic image
quality). This reduction consists of 97 %.
In follow up exposures of femur fracture all the radiologists approved optimized
images at 0,5 mAs corresponding to a dose reduction of 92 %.
Because of the factual bias of the project it might generally be possible to reduce dose
even further, as the lamb phantom absorbs more radiation than a five year old child.
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Software project
Perspectives
 New version of the MLT(S) software.
 The complexity of software optimization demonstrates the necessity of more educated
radiographers with a view to handle development and implementation of such practices.
 Future software could incorporate processing combinations designed for representing a given
pathology optimally with the lowest possible dose.
 In the future examine possibilities of the software in several organs, pathologies and patient
groups – a manual on software optimization will be developed as well as a database on applied
radiographic techniques and software settings for all Europe.
 In order to disseminate the achieved knowledge two articles will be written for publication in
Paediatric Radiology.
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References
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Almén, A., Tingberg, A., Mattsson, S. et al. (2000); The influence of different technique factors on
image quality of lumbar spine radiographs as evaluated by established CEC image criteria, The British
Journal of Radiology, vol. 73, pp. 1192-99.
Artinis (2006); Manual – Contrast-Detail Phantom, Artinis CD Rad type 2.0.
Båth, M., Håkansson, M. et al. (2005); A conceptual optimisation strategy for radiography in a digital
environment, Radioation Protection Dosimetry, vol. 114 pp. 230-35.
Bontrager, K.L. (2002); Textbook of Radiographic Positioning and Related Anatomi, 4 th edn, Bontrager
Publising, Phoenix.
Canon Inc.(2001); X-ray Digital Camera CXDI Series, Technical guide – Image Processing, Japan.
Canon Inc. (2008a); CXDI Image Processing Software MLT(S) User’s Manual, Japan.
Canon Inc. (2008b); Multiobjective Frequency Processing Function manual – MLT(S) Edition, Japan.
European Comission (1996a); European guidelines on quality criteria for diagnostic radiographic
images, Luxemburg.
European Commission (1996b); European guidelines on quality criteria for diagnostic radiographic
images in paediatrics, Luxemburg.
Gonzales, R.C. & Woods, R.E. (2008); Digital Image Processing, 3 rd edn, Pearson, Prentice Hall.
ICRP (2006); Recommendations of the International Comission on Radiological Protection.
ICRP – annals of the ICRP (2004); Guest Editiorial – Managing patient dose in digital radiology, vol.
34, pp. 1-73.
Kjærgaard, J. (2001); Kvalitetsudvikling i sundhedsvæsenet, 1 st edn, 3 rd oplag, Munksgaard, DK.
Norrman, E. (2007); Optimisation of radiographic imaging by means of factorial experiments –
Doctoral Dissertation, Ørebro studies in Phisics 3, Ørebro University, Sweden.
Vallgårda, S. & Koch, L. (2007); Forskningsmetoder i folkesundhedsvidenskab, 3 rd edn, Munksgaard,
Copenhagen.
Vuylsteke, P. & Schoeters, E. (1999); Image Processing in Computer Radiography. Vol. 16 pp 87-101.
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Questions?
Denmark
Thanks for your attention
E-mail: [email protected]
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