- 期刊
在新冠肺炎疫情期間改善移動式肺部X光影像品質穩定與一致性
Improvement of the Consistency and Stability in Image Quality of Portable Chest Digital Radiographs during Covid-19 Pandemic
摘要
新冠肺炎具高度傳染力，患者會在急診的篩檢站、負壓加護病房內以移動式X光機進行檢查。當執行床邊攝影時存在著許多變動的因素會直接影響影像品質，如：X光射源與影像板的距離（source to image receptor distance, SID）、呼吸和角度等。本研究目的在於討論如何改變這些變因，以提升床邊攝影影像品質的一致性。收集從2021年5月15日至9月30日共189張影像（改善前107張、改善後82張），以魚骨圖分析影像品質不良的原因，主要影響影像品質的變因為SID、病床角度、病人呼吸及配合度、不同的移動式X光機（CR GO2和DR Nano）的運用與虛擬鉛柵的應用。以暴露指數（Senstivity, S-value）的範圍作為影像品質一致性控管的規則，以盲測法的評分作為改善前後影像品質分級評量的指標。盲測法是由三位放射科醫師評估六種項目：對比度、黑化度、銳利度、顆粒度、失真度、吸氣程度，比較改善前後的影像品質之變化。經統計分析，暴露指數無統計上的顯著性（p=0.931），另一方面，盲測法的評分顯示對比度、黑化度、銳利度、失真度、顆粒度皆極具顯著性（p<0.001），和吸氣程度（p=0.006）具有顯著性。總結來說，藉由改良影響影像品質的變因和提升硬體及軟體的功能，可以在床邊攝影中有更好且一致的影像品質，為未來後疫情時代做出更大的貢獻。
並列摘要
Due to the aggressive infection of coronavirus disease-19 (Covid-19), radiologic technologists have to take chest X-rays for patients with Covid-19 in different places (e.g., emergency screening station and negative pressure intensive care units). When bedside radiography was performed, multiple changing factors (e.g., SID, respiration, angle, etc.) influenced image quality directly. The purposes of this research are to improve the consistency of bedside radiologic image quality by altering these factors. We collected 189 X-ray images (107 images before improvement and 82 images after improvement) from 15 May to 30 September 2021 and analyzed the reasons for poor image quality with fishbone diagram. The dominant factors affecting image quality were SID, the angle of bed, the breathing and coordination of patient, the usage of different mobile X-ray machines (CR GO2 and DR Nano), and the application of virtual grid. The range of exposure index (Sensitivity, S-value) was used to control the consistency of image quality and the scoring of blind trial was used as the index of image quality graded assessment before and after improvement. There were three radiologists joined in this blind trial and scored in the points of contrast, density, sharpness, graininess, distortion, and respiration of patients to compare the change about the image quality before and after improvement. After statistical analysis, the results showed that there was no significant difference in sensitivity (p=0.931). On the other hand, the results of the scoring of blind trial showed that there were extremely significant differences in contrast, density, sharpness, graininess, distortion (p<0.001), and significant difference in respiration (p=0.006). In summary, our research improved the variables affecting image quality and upgraded the function of hardware and software to keep better and consistent image quality in bedside radiography. This finding would be much helpful for further investigation in the future of post Covid-19 pandemic era.