[Research on Automatic Detection Technology of Radioactive Source Position Accuracy of Brachytherapy Machine Based on Machine Vision and Image Feature Recognition].

Abstract

Objective: To realize the integration of radioactive source position accuracy detection technology for clinical brachytherapy machines with artificial intelligence.

Methods: Based on multiple machine vision algorithms, including video angle correction using affine transformation function, line segment detector (LSD) algorithm, Canny edge detection operator, and identification of moving radioactive source targets via findContours function, automatic identification of radioactive source position was achieved for detecting the position accuracy of radioactive sources in brachytherapy machines, with a comparative analysis with traditional manual detection methods conducted.

Results: Compared with the actual set reference values, the accuracy of the radioactive source repeated positioning error measured by the visual observation method and machine vision analysis method was (0.70±0.48) mm and (0.20±0.42) mm, respectively; the accuracy of the repeated timing error was (0.32±0.12) s and (0.03±0.00) s, respectively; and the accuracy of the cumulative positioning error was (0.55±0.42) mm and (0.28±0.47) mm, respectively. The detection accuracy of the machine vision analysis method was higher than that of the visual observation method, with a statistically significant difference ( P<0.05). In terms of time spent on result analysis, for the three parameters (radioactive source positioning error, repeated positioning error, and cumulative positioning error), the time consumed by the visual observation method and the machine vision analysis method was approximately 1, 10, 20 minutes and 1, 6, 10 minutes, respectively, indicating that the machine vision analysis method was more time-efficient.

Conclusion: The automatic detection technology for radioactive source position accuracy based on machine vision analysis can meet the requirements of brachytherapy machine quality control guidelines as well as the needs of basic and complex quality control items. It has the advantages of time-saving, high stability, and high automation, and serves as a practical technology integrating artificial intelligence with clinical medicine.