The clinical significance of modifying X-ray tube current-time product based on prior image deviation index for digital radiography
The clinical significance of modifying X-ray tube current-time product based on prior image deviation index for digital radiography
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DOI：
10.1016/j.ejmp.2019.05.011
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发表时间：
2019-07-01
影响因子：
3.4
通讯作者：
Ohki, Masafumi
中科院分区：
文献类型：
--
作者：
Takaki, Takeshi;Fujibuchi, Toshioh;Ohki, Masafumi
目的：数字 X 射线系统中图像接收器处的吸收剂量会因 X 射线管电流时间乘积 (mAs) 的不正确调整而增加。因此，提出了暴露指数、目标暴露指数和偏差指数（DI）作为吸收剂量优化工具。我们的目标是通过使用先前使用的 mAs 和 DI 确定的 mAs 值，在短时间内减少 DI 的变化。方法：我们开发了软件，根据先前检查的 mAs 和 DI 值自动计算后续 X 射线检查的 mAs。在重症监护室 (ICU) 进行便携式胸部 X 光检查 16 周。 406 例病例在前 10 周内未使用该软件，216 例病例在剩余 6 周内使用了该软件。使用用于质量控制的p图评估16周内DI不合格率的变化。还评估了该软件对图像噪声的影响。结果：总共 42% 的案例在不使用该软件的情况下 DI 范围为 -1 到 1；使用该软件时，这一比例增加到 81%。使用和不使用软件的情况下 DI 的平均值和方差表现出统计上的显着差异。从 p 图中可以看出，使用软件时 DI 的不合格率有所下降。该软件还可以减少图像噪声的变化。结论：我们的方法在短时间内减少了 DI 的变化。
Purpose: The absorbed dose at the image receptor in digital X-ray systems increases with an incorrect adjustment of the X-ray tube current-time product (mAs). Accordingly, the exposure index, target exposure index, and deviation index (DI) are proposed as absorbed dose optimization tools. We aimed at reducing the variation of DI in a short period by employing the mAs value determined by previously used mAs and DI.Methods: We developed software that automatically calculates mAs for subsequent X-ray examinations based on mAs and DI values from prior examinations. Portable chest X-ray examinations in an intensive care unit (ICU) were performed for 16 weeks. The software was not used for the first 10 weeks in 406 cases and was used for the remaining 6 weeks in 216 cases. The changes in the non-conformance rate of DI for 16 weeks were evaluated using the p-chart used for quality control. The effect of the software on image noise was also evaluated.Results: In total, 42% of cases had a DI range of -1 to 1 without using the software; this increased to 81% when using the software. Averages and variances of DI in cases with and without the software demonstrated statistically significant differences. From the p-chart, the non-conformance rate of DI was shown to decrease when using software. The software also worked for reducing the variation in image noise.Conclusions: Our method reduced the variation in DI in a short period of time.