Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy

• Published in: Bioengineering (Basel) Vol. 11; no. 4; p. 362
• Main Authors: Xie, Lizhang, Zhang, Lei, Hu, Ting, Li, Guangjun, Yi, Zhang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.04.2024
• Subjects: Accuracy; branch architecture; Complexity; Datasets; Feature extraction; gamma passing rate (GPR); multi-branch neural network (MBNN); Neural networks; Patient safety; Performance prediction; Predictions; Prostate; Quality assurance; quality assurance (QA); Quality control; Radiation therapy; Subject specialists; quality assurance (QA); branch architecture; multi-branch neural network (MBNN); gamma passing rate (GPR); radiation therapy
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering11040362

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.