Improving radiotherapy precision through MLC model optimization for a Monaco treatment planning system

Abstract

This study optimizes the Multileaf Collimator (MLC) model in the Monaco Treatment Planning System (TPS), seeking to enhance dose calculation accuracy in radiotherapy. The focus is on refining key parameters such as leaf transmission, leaf groove width, and leaf offset to improve the alignment between calculated and measured dose distributions. The optimization process was conducted at Lam Dong General Hospital using the Elekta MLCi2 system. Iterative adjustments were made to the MLC parameters by analyzing the dose profiles of various test fields, such as FOURL, 3ABUT, and 7SEGA configurations. These profiles were used to detect discrepancies between calculated and measured doses, guiding the adjustments. After each adjustment, gamma analysis was applied to assess whether the refinements improved the quality of dose distribution. The refined MLC model was further validated using complex fields like DMLC, HIMRT, and HDMLC, and applied to clinical treatment plans to evaluate its effectiveness in real-world scenarios. Significant improvements in dose conformity and precision were observed. For instance, optimization of leaf transmission reduced the difference between measured and calculated doses, improving from −2.238 cGy to 0.037 cGy. Adjustments to leaf groove width and leaf offset similarly enhanced dose accuracy. Validation results showed notable improvements in gamma pass rates, particularly for the stringent 1 %/1 mm criterion. The optimized model also demonstrated substantial improvements in gamma pass rates across various clinical scenarios, including rectum, prostate, esophageal, head-neck, breast, and lung cases. Optimization of the MLC model within the Monaco TPS has proven to be a valuable approach in enhancing dose calculation accuracy. The study highlights the importance of continuous quality assurance and fine-tuning of MLC parameters to maintain high standards of treatment accuracy, ultimately contributing to better clinical outcomes for patients.