Program Information
Optimization of Dosimetric Leaf Gap and Multi-Leaf Collimator Transmission Values
N Knutson1*, R Morris1 , F Reynoso1 , M Schmidt2 , B McClain1 , M Reilly1 , R Kashani1 , (1)Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110 (2) Varian Medical Systems, Education Department, Las Vegas, NV 89119
Presentations
PO-BPC-Exhibit Hall-14 (Saturday, March 18, 2017) Room: Exhibit Hall
Purpose: To describe a streamlined process to optimize the high definition multi-leaf collimator (MLC) dosimetric parameters within the Eclipse (Varian Medical Systems, Palo Alto CA) treatment planning system (TPS).
Methods: Measurements using a farmer-style ionization chamber of MLC transmission (MLCtrans) and dosimetric leaf gap (DLG) were used as baseline values for our optimization. Two 6MV dynamic MLC test fields were imported into the TPS: the dynamic chair and the AIDA test fields. These fields were delivered using integrated imaging on the MV imager. The measurements were then compared to the Eclipse predicted images within portal dosimetry. A 1D gamma analysis was performed using a 2% global dose difference and 2 mm distance-to-agreement gamma criteria. Pass rates were recorded for each field along with the average pass rate of the two fields. DLG and MLCtrans values were then iteratively varied from 0.1-1.2 mm and 0.6-1.25% respectively, testing 41 combinations. Once the optimal combination was selected, it was validated using 8 clinical treatment plans using the above criteria (Γ: 2%/2 mm).
Results: Pass rates collected from each of the two test fields varied depending on both DLG and MLCtrans often in conflicting directions for the two fields. Therefore, the maximum pass rate of the average of the two fields was selected as the optimum solution. Pass rates varied from 85.9%-94.1% (mean: 92.4%). The DLG was changed from 0.3 mm to 0.5 mm and MLCtrans from 1.1 to 1.0%. Post adjustment a 0.1%-0.5% increase in pass rate in most clinical cases was observed, however, in one highly modulated case, an 11% increase was observed.
Conclusion: QA test results are a function of both DLG, MLCtrans, and are plan dependent. Therefore to select optimal MLC parameters, multiple plans over multiple treatment sites must be used to systematically analyze multiple combinations of DLG and MLCtrans.
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