Assessment of image quality in x-ray fluoroscopy based on Model observers as an objective measure for quality control and image optimization

BACKGROUND: Although the Image Quality (IQ) indices calculated by objective Model observers contains more favourable characteristics compared to Figure Of Merits (FOM) derived from the more common subjective evaluations of modern digital diagnostic fluoroscopy units, like CDRAD or the Leeds test-objects, practical issues in form of limited access to unprocessed raw data and intricate laboratory measurements have made the conventional computational methods too inefficient and laborious. One approach of the Statistical Decision Variables (CDV) analysis, made available in the FluoroQuality software, overcome these limitations by calculating the SNR2rate from information entirely based on image frames directly obtained from the imaging system, operating in its usual clinical mode. AIM: The overall aim of the project has been to make the proposed Model observer methodology readily available and verified for use in common IQ tests that takes place in a hospital based on simple measuring procedures with the default image enhancement techniques turned on. This includes conversion of FluoroQuality to MATLAB, assessment of its applicability on a modern digital unit by means of comparisons of measured SNR2rate with the expected linear response predicted by the classical Rose model, assessment of the methods limiting and optimized imaging conditions (with regard to both equipment and software parameters) and dose-efficiency measurements of the SNR2rate/Doserate Dose-to-information (DI) index including both routine quality control of the detector and equipment parameter analyses. MATERIALS AND METHODS: A Siemens Axiom Artis Zee MP diagnostic fluoroscopy unit, a Diamentor transmission ionisation chamber and a small T20 solid state detector have been used for acquisition of image data and measurements of Air Kerma-area product rate (KAP-rate) and Entrance Surface Air Kerma rate (ESAK-rate without backscatter). Two sets of separate non-attached test-details, of aluminium and tissue equivalent materials respectively, and a Leeds test object were used as contrasting signals. Dose-efficiency measurements consisted of variation of 4 different parameters: Source-Object-Distance, Phantom PMMA thickness, Field size and Dose rate setting. In addition to these, dimensions of the test details as well as computational parameters of the software, like ROI size and number of frames, were included in the theoretical analyses. RESULTS: FluoroQuality has successfully been converted to MATLAB and the method has been verified with SNR2rate in accordance with the Rose model with only small deviations observed in contrast analyses, most likely reflecting the methods sensitivity in observing non-linear effects. Useful guidelines for measurement procedures with regard to accuracy and precision have been derived from the studies. Results from measurements of the (squared) DI-indices indicates comparable precision (≤ 8%) with the highest performing visual evaluations but with higher accuracy and reproducibility. What still remains for the method to compete with subjective routine QC tests is to integrate the SNR2rate measurements in an efficient enough QA program