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数字放射成像中的拒收率分析:澳大利亚一家急诊影像科的案例研究

Reject rate analysis in digital radiography: an Australian emergency imaging department case study.

作者信息

Atkinson Samantha, Neep Michael, Starkey Deborah

机构信息

South Coast Radiology, Pindara Private Hospital, Benowa, Queensland, Australia.

Department of Medical Imaging, Logan Hospital, Meadowbrook, Queensland, Australia.

出版信息

J Med Radiat Sci. 2020 Mar;67(1):72-79. doi: 10.1002/jmrs.343. Epub 2019 Jul 18.

DOI:10.1002/jmrs.343
PMID:31318181
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7063252/
Abstract

INTRODUCTION

Reject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imaging department to help form a benchmark of reject rates for DR and to assess what radiographs are being rejected and why.

METHODS

A retrospective longitudinal study was undertaken as an in-depth clinical audit. The data were collected using automated reject analysis software from two digital x-ray systems from June 2015 to April 2017. The overall reject rate, reasons for rejection as well as the reject rates for individual radiographers, examination types and projections were analysed.

RESULTS

A total of 90,298 radiographic images were acquired and included in the analysis. The average reject rate was 9%, and the most frequent reasons for image rejection were positioning error (49%) and anatomy cut-off (21%). The reject rate varied between radiographers as well as for individual examination types and projections.

CONCLUSIONS

The variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images.

摘要

引言

数字放射成像(DR)中的拒收分析有助于指导工作人员的教育和培训,影响科室工作流程,降低患者辐射剂量并提高科室效率。本研究的目的是调查一家大型都市急诊影像科的拒收射线照片情况,以帮助形成DR拒收率的基准,并评估哪些射线照片被拒收以及原因。

方法

作为一项深入的临床审计,进行了一项回顾性纵向研究。使用自动拒收分析软件从2015年6月至2017年4月期间的两个数字X射线系统收集数据。分析了总体拒收率、拒收原因以及各个放射技师、检查类型和投照方式的拒收率。

结果

总共采集并纳入分析了90298张射线照片图像。平均拒收率为9%,图像拒收的最常见原因是定位错误(49%)和解剖结构截断(21%)。放射技师之间以及不同的检查类型和投照方式的拒收率有所不同。

结论

放射技师拒收率的差异以及某些投照方式的高拒收率表明,拒收分析作为DR的质量保证工具仍然是必要的。放射科医生和放射技师之间的反馈系统可能通过标准化用于评估图像质量的技术因素来降低定位错误的高比例。未来应定期进行拒收分析,纳入曝光指标分析以及对个别拒收图像的回顾性评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/59a5f0f236c5/JMRS-67-72-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/a5aa54527470/JMRS-67-72-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/af7aa290d0f9/JMRS-67-72-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/59a5f0f236c5/JMRS-67-72-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/a5aa54527470/JMRS-67-72-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/af7aa290d0f9/JMRS-67-72-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44f1/7063252/59a5f0f236c5/JMRS-67-72-g003.jpg

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