Abstract
BACKGROUND: Post-processing software can be used in digital radiography to achieve higher image quality, especially in cases of scattered radiation. SimGrid is a grid-like software based on a Convolutional Neuronal Network that estimates the distribution and degree of scattered radiation in radiographs and thus improves image quality by simulating an anti-scatter grid. S-Enhance is an algorithm programmed to improve contrast visibility of foreign material. OBJECTIVE: The objective of this study was to evaluate the SimGrid and S-Enhance digital radiography post-processing methods for neonatology and paediatric intensive care. MATERIALS AND METHODS: Two hundred and ten radiographs from the neonatal (n = 101, 0 to 6 months of age) and paediatric (n = 109, 6 months to 18 years of age) intensive care units performed in daily clinical routine using a mobile digital radiography system were post-processed with one of the algorithms, anonymized and then evaluated comparatively by two experienced paediatric radiologists. For every radiograph, patient data and exposure data were collected and analysed. RESULTS: Analysis of different radiographs showed that SimGrid significantly improves image quality for patients with a weight above 10 kg (range: 10-30 kg: odds ratio [OR] = 6.683, P < 0.0001), especially regarding the tracheobronchial system, intestinal gas, and bones. Utilizing S-Enhance significantly advances the assessment of foreign material (OR = 136.111, P < 0.0001) and bones (OR = 34.917, P < 0.0001) for children of all ages and weight, whereas overall image quality decreases. CONCLUSION: SimGrid offers a differentiated spectrum in image improvement for children beyond the neonatal period whereas S-Enhance especially improves visibility of foreign material and bones for all patients.