On the need for tuning the dosimetric leaf gap for stereotactic treatment plans in the Eclipse treatment planning system

The dosimetric leaf gap (DLG) and tongue-and-groove (T&G) effects are critical aspects in the modeling of multileaf collimators (MLC) in the treatment planning system (TPS). In this study, we investigated the dosimetric impact of limitations associated with the T&G modeling in stereotactic plans and its relationship with the need for tuning the DLG in the Eclipse TPS. Measurements were carried out using Varian TrueBeam STx systems from two different institutions. Test fields presenting MLC patterns with several MLC gap sizes (meanGap) and different amounts of T&G effect (TGi) were first evaluated. Secondly, dynamic conformal arc (DCA) and volumetric modulated arc therapy (VMAT) deliveries of stereotactic cases were analyzed in terms of meanGap and TGi. Two DLG values were used in the TPS: the measured DLG (DLG(meas)) and an optimal DLG (DLG(opt)). Measured and calculated doses were compared according to dose differences and gamma passing rates (GPR) with strict local gamma criteria of 2%/2 mm. The discrepancies were analyzed for DLG(meas) and DLG(opt), and their relationships with both TGi and meanGap were investigated. DCA arcs involved significantly lower TGi and larger meanGap than VMAT arcs (p < 0.0001). By using DLG(meas) in the TPS, the dose discrepancies increased as TGi increased and meanGap decreased for both test fields and clinical plans. Dose discrepancies dramatically increased with the ratio TGi/meanGap. Adjusting the DLG value was then required to achieve acceptable calculations and configuring the TPS with DLG(opt) led to an excellent agreement with median GPRs (2%/2 mm) > 99% for both institutions. We also showed that DLG(opt) could be obtained from the results of the test fields. We demonstrated that the need for tuning the DLG is due to the limitations of the T&G modeling in the Eclipse TPS. A set of sweeping gap tests modified to incorporate T&G effects can be used to determine the optimal DLG value.