目的:以血管内动脉瘤修复术(EVAR)为例,开发一种神经网络增强的工作流程,用于在介入放射学(IR)中自动快速建立/更新局部诊断参考水平(DRL)。
方法:回顾性收集46例连续EVAR手术的辐射剂量报告。这些报告是制定拟议方法的示范数据。开发了一种算法来接收多剂量报告,自动提取角质面积产品(KAP),空气系数(Ka,r),曝光图像的数量,和透视时间(FT)从每个报告和计算第一个,第二,第三四分位数以及提取参数的最大值和最小值。要从剂量报告中提取感兴趣的值,魔方,采用了开源的光学字符识别(OCR)引擎。此外,评估了所提出方法的准确性和时间效率。具体来说,将从算法中提取的值与地面实况值进行比较,并将算法的处理时间与手动提取和处理感兴趣值所需的相应时间进行比较。
结果:基于OCR的算法设法从184个目标值中正确识别出182个,导致99%的准确度。此外,拟议的管道将建立DRL的处理时间减少了98%。EVAR程序的DRL值,设置为KAP的第三个四分位数,发现为551Gy*cm2。
结论:为建立介入放射学中的局部DRL开发了一种准确且省时的工作流程。
OBJECTIVE: To develop a neural network-enhanced workflow for the automatic and rapid establishment/update of local diagnostic reference levels (DRLs) in interventional radiology (IR) using endovascular aneurysm repair (EVAR) procedures as a
case example.
METHODS: Radiation dose
reports were collected retrospectively for 46 consecutive EVAR procedures. These
reports served as demonstrative data for the development of the proposed methodology. An algorithm was developed to receive multiple dose
reports, automatically extract the kerma area product (KAP), air kerma (Ka,r), number of exposure images, and fluoroscopy time (FT) from each report and calculate the first, second, third quartiles as well as the maximum and minimum values of the extracted parameters. To extract the values of interest from the dose
reports, Tesseract, an open-source optical character recognition (OCR) engine was employed. Furthermore, the accuracy and time efficiency of the proposed methodology were assessed. Specifically, the values extracted from the algorithm were compared with the ground truth values and the algorithm\'s processing time was compared with the respective time needed to manually extract and process the values of interest.
RESULTS: The OCR-based algorithm managed to correctly recognize 182 from the 184 target values, resulting in an accuracy of 99%. Moreover, the proposed pipeline reduced the processing time for the establishment of DRLs by 98%. DRL value for EVAR procedures, set as the third quartile of KAP was found to be 551 Gy*cm2.
CONCLUSIONS: An accurate and time-efficient workflow was developed for the establishment of local DRLs in interventional radiology.