FRI0629 APPLICATION OF AN ADVANCED NOISE REDUCTION ALGORITHM FOR IMAGING OF HANDS IN RHEUMATIC DISEASES – EVALUATION OF IMAGE QUALITY COMPARED TO STANDARD DOSE IMAGES. (June 2019) - Ziegeler, Katharina; Siepmann, Stefan; Beck, Alexander; Lembcke, Alexander; Hamm, Bernd; Hermann, Kay Geert A.
Abstract : Background: X-ray is the fundamental imaging technique in diagnosis and follow up of rheumatic diseases. As patients often require sequential X-rays, dose reduction is of great importance. New advanced noise reduction algorithms allow for a dose reduction of up to 50%. Objectives: The aim of this study was to evaluate, whether the application of an advanced noise reduction algorithms is feasible in the context of imaging of rheumatic diseases. Methods: A total of 298 patients were enrolled prospectively into three tiers: 80%, 64% and 50% dose reduction groups. All patients received imaging of one hand (laterality randomly assigned) with low-dose technique and of the contralateral side with standard protocol. All images were evaluated by two blinded independent readers who scored (on a scale of 1 to 5) the visualisation of bony cortex, trabeculae and joint spaces of fingers and wrist separately as well as soft tissue and overall contrast. Score values were analysed using T-tests for paired samples. Results: Overall image quality (expressed by mean sum scores out of 40) of the 50% low-dose images was 31.52 (SD 1.94) vs. 31.66 (SD 1.82) for standard images (p=0.217). Bony contours as well as trabeculae was equally well visualized in both image sets. An image example is given in Fig. 1 (Left hand: 50%-dose image; right hand: standard-dose image). Soft tissue visualization was slightly lower for low-dose compared to standard images (mean score of 3.81 vs. 3.88; p=0.001). Conclusion: Overall image quality of low dose images was not inferior to standard dose images. Therefore, application of low-dose technology based on advanced noise estimation algorithms in the context of imaging of rheumatic diseases is feasible. Disclosure of Interests: Katharina Ziegeler: None declared, Stefan Siepmann: None declared, Alexander Beck: None declared, Alexander Lembcke: None declared, Bernd Hamm Grant/research support from: Siemens, GE, Bayer, Samsung, Canon, Guerbet, Kay Geert A. Hermann Speakers bureau: AbbVie, MSD, Pfizer, UCB, Samsung