RESUMEN
INTRODUCTION: This study aimed to test whether Advanced Edge Enhancement (AEE) software could improve the localisation of tubes, catheters or wires, while also affecting the overall image quality in chest x-rays (CXR). METHODS: In total, 50 retrospective CXRs were included. All images were obtained utilising the Canon X-ray system (CANON/Arcoma Precision T3 DR System, Canon Europe, Amsterdam, NL) with a CXDI-810C wireless detector. A clinical image, plus three additional AEE algorithms were applied using post processing (two intensity variations 1 and 4) on all CXRs totalling 350 different images. Three radiologists evaluated the images using a subjective Absolute Visual Grading Analysis (VGA). The clinical images used in post processing were not applied as reference in the analysis. Each radiologist graded the images separately in a randomized order, with a score of three indicating suitability for diagnostic assessment. RESULTS: The three AEE algorithms contributed to an overall improvement (average 16-49%) in visualisation of tube, catheter or wire on CXR images. The Mann-Whitney U tests showed a statistically significant (p < 0.05) improvement in contrast resolution and sharpness, indicating an increased ability to differentiate tubes, wires or catheters tips from surrounding tissues. For the noise criterion, not applying any AEE algorithm showed a significantly higher homogeneity in soft tissue (p < 0.001), reducing the ability to visualise soft tissue. The high-intensity catheter algorithm was the only algorithm to achieve a statistically significant (p = 0.017) increase in the ability to differentiate pulmonary tissues of similar density. CONCLUSION: An overall improvement in the visualisation of tube, catheter and wire placement was obtained using the three AEE-algorithms. The bone and catheter algorithms showed the highest consistency, with the small structure algorithm underperforming in resolution and low contrast resolution. In general, image noise increased regardless of algorithm type or applied intensity. The AEE-algorithms should therefore be seen as a supplementary tool to the clinical image protocol, while having the potential to improve image quality to specific clinical situations. IMPLICATIONS FOR PRACTICE: AEE filtered images appear to be a supplement to the current practice of using CXRs in the diagnosis in placement of catheters, tubes and wires in the chest region. The use of AEE-algorithms has the potential to improve the daily work in clinical practice, which serves the basis for further investigation of its effect on radiographic practices.
Asunto(s)
Intensificación de Imagen Radiográfica , Programas Informáticos , Humanos , Intensificación de Imagen Radiográfica/métodos , Estudios Retrospectivos , Radiografía , CatéteresRESUMEN
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.