MLC error detection using a late-fusion convolutional neural network in quality assurance for IMRT(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第8期
- Page:
- 925-932
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- MLC error detection using a late-fusion convolutional neural network in quality assurance for IMRT
- Author(s):
- YAN Bing1; YU Xiao1; WANG Shuai1; WU Ailin2; ZHANG Hongyan1; WU Aidong1
- 1. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital), Hefei 230001, China 2. Department of Radiation Oncology, West Branch of the First Affiliated Hospital of University of Science and Technology of China (Anhui Cancer Hospital), Hefei 230001, China
- Keywords:
- Keywords: deep learning error classification intensity-modulated radiotherapy quality assurance
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.08.001
- Abstract:
- Abstract: Objective?o evaluate the feasibility and advantages of multi-channel multi-path DenseNet (MCMP-DenseNet) for detecting multi-leaf collimator (MLC) errors in quality assurance (QA) for intensity-modulated radiotherapy (IMRT) from dose difference maps and Gamma maps.?ethods?he MLC positions of 98 error-free IMRT plans were modified to simulate translation, extension, shift, and random errors. The plans with and without errors were re-imported into TPS for calculating the dose distributions in the PTW 729 phantom. The dose difference maps and the Gamma maps with two Gamma criteria which were created from the measured and calculated dose distributions were used for dataset establishment and MCMP-DenseNet training. Among them, 330 dose difference maps and 660 Gamma maps was adopted for the test set, and the remaining were divided into training and validation sets according to 5-fold cross-validation. Based on the prediction results of the test set, the overall classification accuracy, Macro-F1, and normalized confusion matrix were calculated for evaluating the model performance. Results MCMP-DenseNet had the highest overall classification accuracy (0.855) and Macro-F1 (0.853). The normalized confusion matrix revealed that the average classification accuracies of the MLC shift error, expansion error, error-free, random error, and translation error were 0.98, 1.00, 0.66, 0.63, and 1.00, respectively. Conclusion?he study demonstrates the feasibility and accuracy of MCMP-Densenet in terms of the overall classification accuracy, Macro-F1, and specific classification accuracy.
Last Update: 2023-09-06