Development and clinical application of a data analysis and management system (QAManager) for accelerator quality assurance(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第5期
- Page:
- 545-550
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Development and clinical application of a data analysis and management system (QAManager) for accelerator quality assurance
- Author(s):
- WANG Xuemin; WU Xiangyang; CHANG Xiaobin; FENG Tao; QU Ximei; ZHAO Qiang; YANG Di; DENG Jia
- Department of Radiotherapy, Shaanxi Provincial Cancer Hospital, Xian 710061, China
- Keywords:
- Keywords: medical accelerator quality assurance automatic image analysis web-based data statistics network-based management
- PACS:
- R811.1；R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2021.05.004
- Abstract:
- Abstract: Objective To develop and test Python Django-based automatic analysis web system (QAManager) for accelerator quality assurance (QA). Methods QAManager was developed based on Python Django and MySQL database, with the functions of statistical analysis on accelerator QA data and automatic analysis on images obtained by on-board imaging (OBI) system. Half-a-year QA results of TrueBeam accelerator were statistically analyzed by QAManager. Besides, several functional tests were also conducted. [①] The distribution and variation trend of all QA items were analyzed by QAManager, thereby comprehensively evaluating the working state of TrueBeam accelerator during the period. [②] The results of manual analysis and QAManager analysis on Catphan504 images obtained by TrueBeam OBI system in the past year were analyzed and compared, thereby analyzing the accuracy and precision of QAManager image automatic recognition algorithm. [③ ]Catphan504 phantom which was set up on the treatment couch was deliberately shifted by 1 cm in lateral and longitudinal (no rotational) directions from the reference position, and then QAManager was used for automatic detection, thereby analyzing algorithm robustness. Results The interface of QAManager was user-friendly and compact. According to the custom QA item graphs drawn by QAManager, the rules of QA data distribution and the trends of data varying with time were pointed out and all modules in Catphan504 CBCT image detection were identified precisely by QAManager. No significant difference was found between manual analysis and QAManager analysis on Catphan504 images obtained by TrueBeam OBI system (P>0.05), except for slice thickness (P=0.05). Conclusion QAManager is accuracy and reliable, and the algorithm can be used to accurately detect all OBI parameters. Therefore, QAManager can be widely used in clinic and is vital for network-based department management.
Last Update: 2021-05-31