Advanced Deep Learning Methods for MLC Leaf Error Classification in Fluence Maps of Radiation Therapy

Background Multi-leaf collimator (MLC) positioning errors are a major source of delivery uncertainty in modern radiation therapy techniques such as volumetric modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT). Traditional quality assurance (QA) methods, particularly gamma analysis, have limited sensitivity for detecting individual leaf errors and provide only binary pass or fail outcomes without precise localization. Although artificial intelligence (AI)-based approaches have been introduced to improve error detection, most have focused on overall treatment plan assessments rather than identifying leaf-specific deviations, reducing their clinical usefulness for targeted error correction. To address these limitations, this study developed and validated a deep learning method to identify MLC leaf positioning errors with high precision. Methods Treatment plans from forty patients with prostate cancer, each containing two full arcs, were retrospectively analyzed using the Varian High Definition MLC system. Inner leaves numbered twenty-one to forty, each with a width of 2.5 mm, were selected for analysis. Fluence maps were generated from DICOM radiotherapy plan files, and systematic MLC positioning errors ranging from − 5 mm to + 5 mm were introduced, producing 121 error combinations per leaf and a total of 48,400 samples. The dataset was divided into training, validation, and test subsets in an 8:1:1 ratio. Each fluence segment was converted into a two-channel image and used to train a convolutional neural network (CNN) to classify the magnitude and direction of the deviations. Results The model achieved a final test accuracy of 97.21% and maintained consistent performance during cross-validation, detecting over 95% of leaf errors within 1 mm of the true offset. Conclusions This CNN-based framework enables accurate, leaf-specific error identification and has strong potential to enhance the efficiency and reliability of modern radiation therapy QA.