Advanced prediction of multi‐leaf collimator leaf position using artificial neural network

Abstract Background Multi‐leaf collimators (MLCs) are crucial for modern radiotherapy as they ensure precise target irradiation through accurate leaf positioning. Accurate prediction of MLC leaf positions is vital for the effectiveness and safety of treatments. Purpose This study aims to establish three neural network models for predicting the delivered positions of MLCs in radiotherapy. Methods Fifty plans with sliding window dynamic intensity‐modulated radiation therapy delivery were selected from an Elekta linear accelerator, which features a 160‐leaf MLC system. The dose fraction, gantry angle, collimator angle, X1 and X2 jaw positions, Y1 and Y2 carriage positions, planned leaf positions, adjacent leaf positions, leaf gap, leaf velocity, and leaf acceleration were extracted from the planning data in the machine's log files and used as model inputs, with the delivered leaf positional serving as the target response. This establishes the input–output relationship for the neural network, and the predicted MLC positions are obtained through training. Particle Swarm Optimization Back Propagation Neural Network (PSOBPNN), Back Propagation Neural Network (BPNN), and Radial Basis Function Neural Network (RBFNN) architectures were developed to predict MLC leaf positional deviations during treatment. The training was conducted on 70% of the sample data, with the remaining 30% used for validation and testing. Model performance was assessed using metrics such as mean absolute error (MAE), mean squared error (MSE), regression plots, and error histograms. Results The proposed neural network models demonstrated high accuracy in predicting MLC leaf positions. The PSOBPNN model demonstrated superior performance with an MAE of 0.0043 mm and an MSE of 0.00003 mm 2 . In comparison, the BPNN model achieved an MAE of 0.0241 mm and an MSE of 0.001 mm 2 , while the RBFNN model exhibited an MAE of 0.0331 mm and an MSE of 0.0019 mm 2 . The correlation coefficient ( R = 0.9999) of models indicates a close match between predicted and delivered leaf positions for all MLC leaves. Conclusion Three models were evaluated for predicting the delivered MLC positions using data from an Elekta accelerator. The PSOBPNN model exhibited superior performance by achieving markedly lower MAE and MSE values while also demonstrating robust generalizability in predicting positions across various leaf indices, outperforming the conventional BPNN and RBFNN models.