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Purpose: Any Linac will show geometric imprecisions, including non-ideal alignment of the gantry, collimator and couch axes, and gantry sag or wobble. Their angular dependence can be quantified and resulting changes of the dose distribution predicted (Wack, JACMP 20(5), 2020). We analyzed whether it is feasible to correct geometric shifts during treatment planning. The successful implementation of such a correction procedure was verified by measurements of different stereotactic treatment plans. Methods: Isocentric shifts were quantified for two Elekta Synergy Agility Linacs using the QualiForMed ISO-CBCT+ module, yielding the shift between kV and MV isocenters, the gantry flex and wobble as well as the positions of couch and collimator rotation axes. Next, the position of each field's isocenter in the Pinnacle treatment planning system was adjusted accordingly using a script. Fifteen stereotactic treatment plans of cerebral metastases (0.34 to 26.53 cm 3 ) comprising 9-11 beams were investigated; 54 gantry and couch combinations in total. Unmodified plans and corrected plans were measured using the Sun Nuclear SRS-MapCHECK with the Stereophan phantom and evaluated using gamma analysis. Results: Geometric imprecisions, such as shifts of up to 0.8 mm between kV and MV isocenter, a couch rotation axis 0.9 mm off the kV isocente,r and gantry flex with an amplitude of 1.1 mm, were found. For eight, mostly small PTVs D98 values declined more than 5% by simulating these shifts. The average gamma (2%/2 mm, absolute, global, 20% threshold) was reduced from 0.53 to 0.31 (0.32 to 0.30) for Linac 1 (Linac 2) when including the isocentric corrections. Thus, Linac 1 reached the accuracy level of Linac 2 after correction. Conclusion: Correcting for Linac geometric deviations during the planning process is feasible and was dosimetrically validated. The dosimetric impact of the geometric imperfections can vary between Linacs and should be assessed and corrected where necessary.

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Developed as a plan-specific pre-treatment QA tool, Varian portal dosimetry promises a fast, high-resolution, and integrated QA solution. In this study, the agreement between predicted fluence and measured cumulative portal dose was determined for the first 140 patient plans at our Halcyon linear accelerator. Furthermore, the capability of portal dosimetry to detect incorrect plan delivery was compared to that of a common QA phantom. Finally, tolerance criteria for verification of VMAT plan delivery with Varian portal dosimetry were derived. Methods: All patient plans and the corresponding verification plans were generated within the Eclipse treatment planning system. Four representative plans of different treatment sites (prostate, prostate with lymphatic drainage, rectum, and head & neck) were intentionally altered to model incorrect plan delivery. Investigated errors included both systematic and random errors. Gamma analysis was conducted on both portal dose (criteria γ 2%/2 mm , γ 2%/1 mm , and γ 1%/1 mm ) and ArcCHECK measurements (criteria γ 3%/3 mm , γ 3%/2 mm , and γ 2%/2 mm ) with a 10% low-dose threshold. Performance assessment of various acceptance criteria for plan-specific treatment QA utilized receiver operating characteristic (ROC) analysis. Results: Predicted and acquired portal dosimetry fluences demonstrated a high agreement evident by average gamma passing rates for the clinical patient plans of 99.90%, 96.64%, and 91.87% for γ 2%/2 mm , γ 2%/1 mm , and γ 1%/1 mm , respectively. The ROC analysis demonstrated a very high capability of detecting erroneous plan delivery for portal dosimetry (area under curve (AUC) > 0.98) and in this regard outperforms QA with the ArcCHECK phantom (AUC ≈ 0.82). With the suggested optimum decision thresholds excellent sensitivity and specificity is simultaneously possible. Conclusions: Owing to the high achievable spatial resolution, portal dosimetry at the Halcyon can reliably be deployed as plan-specific pre-treatment QA tool to screen for errors. It is recommended to support the fluence integrated portal dosimetry QA by independent phantom-based measurements of a random sample survey of treatment plans.

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