The influence of a novel edge enhancement software on image quality of DR hand images of patients with rheumatoid arthritis. Issue 3 (August 2021)

• Record Type: Journal Article
• Authors: Outzen, C.B.; Maron, D.; Nissen, J.; Munk, J.; Grau, L.M.; Juhl, D.; Precht, H.
• Is Part Of: Radiography. Volume 27:Issue 3(2021)
• Page Start: 877
• Page End: 882
• Publication Date: 2021-08
• Subjects: New software -- Enhancement -- Image quality -- Digital radiography -- Rheumatoid arthritis -- Visual grading analysis; Diagnostic imaging -- Periodicals; Radiotherapy -- Periodicals; Cancer -- Radiotherapy -- Periodicals; Diagnostic Imaging -- Periodicals; Neoplasms -- Periodicals; Radiotherapy -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Radiothérapie -- Périodiques; Cancer -- Radiothérapie -- Périodiques; Electronic journals; 616.0757
• DOI: 10.1016/j.radi.2021.02.006
• Languages: English
• ISSNs: 1078-8174

Abstract

Abstract: Introduction: This study aimed to evaluate the effects of a newly developed Advanced Edge Enhancement software (AEE) (Canon Europe, Amsterdam, NL) on image quality (IQ) of Digital Radiography (DR) hand images focusing on rheumatoid arthritis (RA). Methods and materials: Fifty posterior-anterior hand images with or suspected for RA were collected. For each of the 50 images, six copies were made with each their AEE algorithm settings. A total of 330 images (30 images iterated) were evaluated using relative Visual Grading Analysis (VGA) by three observers and combined into a VGA Score (VGAS). Second, 50 images of a technical Contrast Detail Radiography Phantom (CDRAD) was produced with three different AEE software settings, each at level 1, 5 and without the AEE software yielding 350 CDRAD images. All images was analysed by the CDRAD Analyser and included for an objective analysis of the AEE software. Results: The VGA study showed a significant difference in image quality between a standard image and images with AEE software applied. The average VGA score of the AEE software was better than the standard images (interval between 0.2 and 0.9). The AEE algorithms at level 5 scored significantly lower for noise but significantly higher for spatial resolution, sharpness and contrast in the VGA. The CDRAD images showed that all AEE algorithms had a statistically significant improvement for level 1 and deterioration for level 5 compared to the standard image. Conclusion: Overall the AEE algorithm: small structure level 1 showed an improvement of all IQ criteria in the VGA and a better technical IQ. Implications for practice: The AEE software ought to be considered as a useful addition to the current software, possibly enabling visualisation of structures currently visible.