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

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

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

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

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)

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

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

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

Thickness of step wedge (cm) RESULTS Thickness of step wedge (cm) Siemens Multix Fusion (SNR) y X= (y - 1.187)/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 0.2 - 3 Siemens Philips 1 Philips 2 GE 0.57 0.95 2.02 2 12.37 2 yrs 10 yrs 7 yrs algorithm

RESULTS – Contrast to Noise ratio 1 2 CNR = Pixel value(step wedge) - Pixel value(Perspex) Noise

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

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