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Application of an advanced noise reduction algorithm for imaging of hands in rheumatic diseases: evaluation of image quality compared to standard-dose images

Abstract

Objectives

X-ray is the fundamental imaging technique in both diagnosis and follow-up of rheumatic diseases. As patients often require sequential X-rays over many years, dose reduction is of great importance. New advanced noise reduction algorithms allow for a dose reduction of up to 50%. The aim of this study was to evaluate whether quality of low-dose images is non-inferior to standard-dose images and, therefore, application of this technique is possible in the context of imaging of rheumatic diseases.

Methods

A total of 298 patients with known or suspected rheumatic disease were enrolled prospectively into this study, separated into three consecutive groups: 80%, 64% and 50% tube charge reduction. All patients received imaging of one hand (laterality randomly assigned) with low-dose technique and imaging of the contralateral hand with standard-dose protocol. Images were evaluated by two independent readers who scored (on a scale of 1–5) the visualization of bony cortex, trabeculae and joint spaces of fingers and wrist separately. Additionally, soft tissue and overall contrast were evaluated on the same scale.

Results

Overall image quality (expressed by mean sum score 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 = 0.068). Bony contours as well as trabeculae were equally well visualized in both image sets. Median scores for soft tissue visualization was slightly lower for low dose compared to standard images [4 (IQR 3.5–4) vs. 4 (IQR 3.88–4); p = 0.001].

Conclusions

Overall image quality of low-dose images was not inferior to standard-dose images. Therefore, the application of low-dose technology based on advanced noise estimation algorithms in the context of imaging of rheumatic diseases is possible.

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Acknowledgements

The Department of Radiology of Charité-Universitätsmedizin Berlin has a master research agreement with Samsung Health Medical Corporation for the further development of radiography technologies. Bernd Hamm receives honoraria from Canon Medical Systems. Kay-Geert Hermann receives funding from the Berlin Institute of Health (Clinical Fellow Programme). All other authors have no funding to report.

Funding

Part of the data included in this manuscript here was presented at the Congress of the European League Against Rheumatism 2019 in Madrid as K. Ziegeler, S. Siepmann, A. Beck, A. Lembcke, B. Hamm, K.G.A. Hermann, FRI0629. Application of an advanced noise reduction algorithm for imaging of hands in rheumatic diseases—evaluation of image quality compared to standard-dose images, Annals of the Rheumatic Diseases 78(Suppl 2) (2019) 1012–1012.

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Katharina Ziegeler, Stefan Siepmann and Kay-Geert Hermann. The first draft of the manuscript was written by Katharina Ziegeler and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Katharina Ziegeler.

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Conflict of interest

The authors’ department has a master research agreement with Samsung Health Medical Corporation for the further development of radiography technologies. All authors declare to have no conflict of interest.

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Supplementary Figure. Further

imaging examples. 1a: standard dose image. 1b: 64% low-dose image of the same patient as 1a. 2a: 80% low-dose image. 2b: standard dose image of the same patient as 2a (TIF 10062 kb)

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Cite this article

Ziegeler, K., Siepmann, S., Hermann, S. et al. Application of an advanced noise reduction algorithm for imaging of hands in rheumatic diseases: evaluation of image quality compared to standard-dose images. Rheumatol Int 40, 893–899 (2020). https://doi.org/10.1007/s00296-020-04560-1

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Keywords

  • X-ray
  • Dose reduction
  • Imaging
  • Rheumatic diseases