Validation of a Robust Enhanced Leaf Model for Beam-Matched Linear Accelerators: Impact of Physical Leaf Gap Variations
Abstract
Purpose
This study characterized inter-machine variations in physical leaf gaps and associated MLC parameters, including dosimetric leaf gap (DLG) and leaf gap (LG). We provide practical guidance for robust Enhanced Leaf Model (ELM) implementation through physical leaf gap adjustment.
Methods
Five TrueBeam (TBs) equipped with Millennium120 were grouped into GroupA (3-TBs) and GroupB (2-TBs), with beam matching performed during initial commissioning within each group. Variations in physical leaf gap, DLG, and machine-specific LG were evaluated. Physical leaf gap differences greater than 0.050 cm were adjusted by moving them halfway toward the machine-averaged value. Clinical ELM models were validated for individual TBs using 3-mm narrow-MLC (nMLC), subsequent static-MLC (sMLC), and dynamic-MLC (dMLC) fields with 3-mm sweeping gaps over central and off-axis. GafChromic film measurements were compared with ELM-calculated profiles before and after adjustment using FWHM and 1D g-analysis (2%/1mm). Patient-specific QA (PSQA) with isocenter point dose measurement was performed for IMRT, VMAT, and SRS/SBRT plans.
Results
GroupA TBs with identical physical leaf gaps exhibited DLG and LG differences below 0.02 cm with strong agreement in MLC profile comparisons and PSQA results. GroupB exhibited physical leaf gap differences above 0.05 cm requiring adjustment by approximately 0.028 cm, which significantly minimized DLG and LG discrepancies. Following adjustment, γ-passing rates exceeded 95% and FWHM differences were below 0.01 cm for nMLC tests. sMLC γ-passing rates surpassed 98%, while dMLC central dose differences remained under 2%. PSQA performance for SRS plans improved from 88.4% to 100% (3%/1mm) for a 1-cm target at 5 cm off-axis, while VMAT (>99%) and IMRT (>95%) γ-passing rates remained comparable. Isocenter dose difference was <2% for VMAT/SBRT, <6.5% for IMRT using sweeping gaps.
Conclusion
Implementation of a robust ELM model requires explicit consideration of mechanical leaf gap variability, with additional validation using the MLC test fields to ensure clinical suitability