ArticlePediatric radiology2022
Evaluation of the post-processing algorithms SimGrid and S-Enhance for paediatric intensive care patients and neonates.
Article in Pediatric radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.Japanese journal of radiology · 2025Article
- Evaluation of an artificial intelligence noise reduction tool for conventional X-ray imaging - a visual grading study of pediatric chest examinations at different radiation dose levels using anthropomorphic phantoms.Pediatric radiology · 2025Article
- Performance and usefulness evaluation of a software-based scatter correction technique for mammographic images.Heliyon · 2024Article
- Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2023Review
- Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPost-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.
objectiveThe 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
methodsTwo 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.
resultsAnalysis 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.
conclusionSimGrid 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.
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