Prediction of multi-leaf collimator leaf positional deviations based on artificial neural network(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第12期
- Page:
- 1495-1501
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Prediction of multi-leaf collimator leaf positional deviations based on artificial neural network
- Author(s):
- ZHANG Liyuan; JIA Nan; ZHENG Xiaona; ZHANG Bo; LI Songli; HAN Quanxiang
- Keywords: dynamic intensity-modulated radiotherapy multi-leaf collimator log file artificial neural network positional deviation
- Keywords:
- Department of Radiation Oncology; Zhengzhou Central Hospital; Zhengzhou 450052; China
- PACS:
- R312；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2021.12.008
- Abstract:
- Abstract: Objective To predict the multi-leaf collimator (MLC) leaf positional deviations during dynamic intensity-modulated radiotherapy (dIMRT) by log file analysis and artificial neural network (ANN) method. Methods The dIMRT DynaLog files from Varian Trilogy (50 cases) and the dIMRT Trajectory log files from TrueBeam and Edge (30 cases of each) were retrieved. A 3-layer ANN model with hidden layer of 22 neurons was developed for each MLC leaf, with 14 features extracted from the log files, such as leaf planned positions, dose, gantry angle, jaw position, leaf gap, leaf velocity and leaf motion status, as input parameters, and the delivered leaf position recorded by log files as output. The proposed model was trained on 70%, validated and tested on 30% of the total data. Mean square error (MSE) was taken as the cost function to evaluate the model performance. Results The leaf velocity was the most relevant input feature to positional deviation, with a Pearson correlation coefficient greater than 0.7. Significant differences were found in the mean absolute error (MAE) between moving and resting leaf. The maximum MSE in predicting the leaf positions on test set were less than 9×10-5, 3×10-5 and 3×10-5 mm2 for Trilogy, TrueBeam and EDGE, respectively and the predicted leaf positions closely matched the actual positions during the treatment delivery. There was significant difference in MAE of the model in predicting the positions of current leaf and other leaves (P<0.001). Conclusion The proposed log file-based ANN model is capable of predicting the Varian MLC leaf position during dIMRT.
Last Update: 2021-12-24