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针对儿科重症监护患者和新生儿的后处理算法SimGrid和S-Enhance的评估。

Evaluation of the post-processing algorithms SimGrid and S-Enhance for paediatric intensive care patients and neonates.

作者信息

Krueger Paul-Christian, Ebeling Katharina, Waginger Matthias, Glutig Katja, Scheithauer Marcel, Schlattmann Peter, Proquitté Hans, Mentzel Hans-Joachim

机构信息

Section of Pediatric Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany.

Department of Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany.

出版信息

Pediatr Radiol. 2022 May;52(6):1029-1037. doi: 10.1007/s00247-021-05279-2. Epub 2022 Feb 22.

DOI:10.1007/s00247-021-05279-2
PMID:35192022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9107410/
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.

摘要

背景

后处理软件可用于数字放射摄影，以获得更高的图像质量，尤其是在存在散射辐射的情况下。SimGrid是一种基于卷积神经网络的网格状软件，可估计X光片中散射辐射的分布和程度，从而通过模拟反散射网格来提高图像质量。S-Enhance是一种旨在提高异物对比度可见性的算法。

目的

本研究的目的是评估SimGrid和S-Enhance数字放射摄影后处理方法在新生儿科和儿科重症监护中的应用。

材料与方法

使用移动数字放射摄影系统在日常临床常规中对新生儿重症监护病房（n = 101，0至6个月龄）和儿科重症监护病房（n = 109，6个月至18岁）的210张X光片进行后处理，采用其中一种算法进行匿名处理，然后由两名经验丰富的儿科放射科医生进行比较评估。收集并分析每张X光片的患者数据和曝光数据。

结果

对不同X光片的分析表明，SimGrid显著提高了体重超过10kg患者的图像质量（范围：10 - 30kg：优势比[OR] = 6.683，P < 0.0001），特别是在气管支气管系统、肠道气体和骨骼方面。对于所有年龄和体重的儿童，使用S-Enhance显著提高了对异物（OR = 136.111，P < 0.0001）和骨骼（OR = 34.917，P < 0.0001）的评估，而整体图像质量下降。

结论

SimGrid为新生儿期后的儿童提供了差异化的图像改善范围，而S-Enhance尤其提高了所有患者异物和骨骼的可见性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/68b10545bb3c/247_2021_5279_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/dc85634c6f62/247_2021_5279_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/713b5da889e2/247_2021_5279_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/cf8d86133c2a/247_2021_5279_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/13def56e8f95/247_2021_5279_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/c99a748b2266/247_2021_5279_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/de9167938c4c/247_2021_5279_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/0b98baa68236/247_2021_5279_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/eb0aadee7d6d/247_2021_5279_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/68b10545bb3c/247_2021_5279_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/dc85634c6f62/247_2021_5279_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/713b5da889e2/247_2021_5279_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/cf8d86133c2a/247_2021_5279_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/13def56e8f95/247_2021_5279_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/c99a748b2266/247_2021_5279_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/de9167938c4c/247_2021_5279_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/0b98baa68236/247_2021_5279_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/eb0aadee7d6d/247_2021_5279_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb9/9107410/68b10545bb3c/247_2021_5279_Fig9_HTML.jpg

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