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SU-F-T-531: Determination of Site-Specific Dynamic-Jaw Versus Static-Jaw RapidArc Delivery.
Medical Physics 2016 June
PURPOSE: Dynamic-jaw tracking maximizes the area blocked by both jaw and MLC in RapidArc. We developed a method to quantify jaw tracking.
METHODS: An Eclipse Scripting API (ESAPI) was used to export beam parameters for each arc's control points. The specific beam parameters extracted were: gantry angle, control point number, meterset, x-jaw positions, y-jaw positions, MLC bank-number, MLC leaf-number, and MLC leaf-position. Each arc contained 178 control points with 120 MLC positions. MATLAB routines were written to process these parameters in order to calculate both the beam aperture (unblocked) size for each control point. An average aperture size was weighted by meterset. Jaw factor was defined as the ratio between dynamic-jaw to static-jaw aperture size. Jaw factor was determined for forty retrospectively replanned patients treated with static-jaw delivery sites including lung, brain, prostate, H&N, rectum, and bladder.
RESULTS: Most patients had multiple arcs and reduced-field boosts, resulting in 151 fields. Of these, the lowest (0.4722) and highest (0.9622) jaw factor was observed in prostate and rectal cases, respectively. The median jaw factor was 0.7917 meaning there is the potential unincreased blocking by 20%. Clinically, the dynamic-jaw tracking represents an area surrounding the target which would receive MLC-only leakage transmission of 1.68% versus 0.1% with jaws. Jaw-tracking was more pronounced at areas farther from the target. In prostate patients, the rectum and bladder had 5.5% and 6.3% lower mean dose, respectively; the structures closer to the prostate such as the rectum and bladder both had 1.4% lower mean dose.
CONCLUSION: A custom ESAPI script was coupled with a MATLAB routine in order to extract beam parameters from static-jaw plans and their replanned dynamic-jaw deliveries. The effects were quantified using jaw factor which is the ratio between the meterset weighted aperture size for dynamic-jaw fields versus static-jaw fields.
METHODS: An Eclipse Scripting API (ESAPI) was used to export beam parameters for each arc's control points. The specific beam parameters extracted were: gantry angle, control point number, meterset, x-jaw positions, y-jaw positions, MLC bank-number, MLC leaf-number, and MLC leaf-position. Each arc contained 178 control points with 120 MLC positions. MATLAB routines were written to process these parameters in order to calculate both the beam aperture (unblocked) size for each control point. An average aperture size was weighted by meterset. Jaw factor was defined as the ratio between dynamic-jaw to static-jaw aperture size. Jaw factor was determined for forty retrospectively replanned patients treated with static-jaw delivery sites including lung, brain, prostate, H&N, rectum, and bladder.
RESULTS: Most patients had multiple arcs and reduced-field boosts, resulting in 151 fields. Of these, the lowest (0.4722) and highest (0.9622) jaw factor was observed in prostate and rectal cases, respectively. The median jaw factor was 0.7917 meaning there is the potential unincreased blocking by 20%. Clinically, the dynamic-jaw tracking represents an area surrounding the target which would receive MLC-only leakage transmission of 1.68% versus 0.1% with jaws. Jaw-tracking was more pronounced at areas farther from the target. In prostate patients, the rectum and bladder had 5.5% and 6.3% lower mean dose, respectively; the structures closer to the prostate such as the rectum and bladder both had 1.4% lower mean dose.
CONCLUSION: A custom ESAPI script was coupled with a MATLAB routine in order to extract beam parameters from static-jaw plans and their replanned dynamic-jaw deliveries. The effects were quantified using jaw factor which is the ratio between the meterset weighted aperture size for dynamic-jaw fields versus static-jaw fields.
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