1. Quantitative assessment of image quality in different digital radiography systems
Roshan S Livingstone and Benedicta R
Department of Radiology
Christian Medical College, Vellore, S India
Vision statement: The Christian Medical college, Vellore seeks to be a witness to the healing ministry of Christ through excellence in education, service and research

2. Introduction
Images acquired using flat detectors (FD) in Digital radiography (DR) has increased tremendously compared to conventional film-screen radiography due to its varied advantages
Prolonged use of FD and its influence on image quality is not fully understood
Assessment of image quality from radiological images are generally qualitative/subjective in nature
A simple method of testing quality of DR images need to be established for quantitative analysis

3. Factors affecting image quality in flat detectors
Training of DR operator
Are we ready?
Selecting optimal exposure factors?
Flat Detector dependence
Types - CsI/GOS
Temperature
Image resolution – Spatial and contrast
Post processing software algorithms
Radiation exposure

4. Aim
To evaluate the performance of FDs using a simple in-house test tool in 5 different DR units
Siemens Multix Fusion GE
Philips Diagnost
(2 x-ray units)
Siemens Aristos

5. Materials and methods
Perspex block
Aluminium step wedge
Dose Area Product (DAP) meter
Unfors – Solid state dosimeter
Checking output consistency in all DR systems – 60 kV, 20 mAs, 100cm
Similar size region of interest (ROI) on images
Assessment of image quality using Centricity software (GE Centricity, USA)

6. Experimental setup in Siemens Multix fusion
Protocol kV
mAs
AEC
Grid
DAP (µGym2)
Knee 60
2.3
Yes
12.68
1.1 No
5.17 10
60.59
60.55
DAP meter
43 cm
43 cm
Standardisation
Automated selection of exposure factors (AEC) in all machines
Maintaining a constant DAP values – without AEC in all machines
Experimental setup in Siemens Multix fusion

7. Image quality analysis
Calculating Signal to noise ratio (SNR) and Contrast to noise ratio (CNR)
Measuring pixel value
SNR(step wedge) = Pixel value(step wedge)
Noise
Step wedge 1
CNR = Pixel value(step wedge) - Pixel value(Perspex)
Noise 2
Perspex phantom 3
1 – Mean pixel value from step wedge
2 – Mean pixel value from perspex
3 – Mean pixel value for noise
Image quality assessment using Centricity software (GE Centricity, USA)
from picture archival communication system (PACS) workstation

8. Characteristic Curve of a film
RESULTS
Curve/slope similar to film screen
FDs response to a x-ray exposure – determines sensitivity
Dynamic range and wide latitude in FD compared to film
Characteristic Curve of a film

9. Thickness of step wedge (cm)
RESULTS
Thickness of step wedge (cm)
Siemens Multix Fusion
(SNR) y
X= (y )/0.5706
0.5
1.42
0.4
0.8
1.65
1.1
1.84
1.4
2.02
1.5
1.7
2.18 2
2.34
2.0
2.3
2.49
2.6
2.64
2.5
Useful density of the film-screen
Siemens
Philips 1
Philips 2 GE
0.57
0.95
2.02 2
12.37
2 yrs
10 yrs
7 yrs
algorithm

10. RESULTS – Contrast to Noise ratio1 2
CNR = Pixel value(step wedge) - Pixel value(Perspex)
Noise

11. Discussion and Conclusion
Both qualitative and quantitative assessment of image quality – SNR and CNR
All detectors exhibited similar sensitivity corresponding to a film screen combination (0.2 – 2.8 au of SNR)
Contrast variations were observed with the use of grid (reduction of noise) and non grid
The resolution of the image depended on the software algorithms and preset protocol selected
For higher tube potentials, different stepwedge is required

12. Summary
This simple method serves as image quality assessment tool to study the following;
Contrast enhancement ratio of detectors
Signal to noise ratio (SNR)
Contrast to noise ratio (CNR)
Sensitivity of the detector
Compare image quality between different detectors
Study optimal exposure factors required for imaging
Disclosure: No funding involved
Conflicts of interest : NIL