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SU‐E‐T‐436: Fluence‐Based Trajectory Optimization for Non‐Coplanar VMAT
Author(s) -
Smyth G,
Evans PM,
Bamber JC,
Saran FH,
Mandeville HC,
Bedford JL
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4924797
Subject(s) - nuclear medicine , mathematics , fluence , metric (unit) , root mean square , volume (thermodynamics) , physics , medicine , optics , laser , operations management , quantum mechanics , economics
Purpose: To investigate a fluence‐based trajectory optimization technique for non‐coplanar VMAT for brain cancer. Methods: Single‐arc non‐coplanar VMAT trajectories were determined using a heuristic technique for five patients. Organ at risk (OAR) volume intersected during raytracing was minimized for two cases: absolute volume and the sum of relative volumes weighted by OAR importance. These trajectories and coplanar VMAT formed starting points for the fluence‐based optimization method. Iterative least squares optimization was performed on control points 24° apart in gantry rotation. Optimization minimized the root‐mean‐square (RMS) deviation of PTV dose from the prescription (relative importance 100), maximum dose to the brainstem (10), optic chiasm (5), globes (5) and optic nerves (5), plus mean dose to the lenses (5), hippocampi (3), temporal lobes (2), cochleae (1) and brain excluding other regions of interest (1). Control point couch rotations were varied in steps of up to 10° and accepted if the cost function improved. Final treatment plans were optimized with the same objectives in an in‐house planning system and evaluated using a composite metric ‐ the sum of optimization metrics weighted by importance. Results: The composite metric decreased with fluence‐based optimization in 14 of the 15 plans. In the remaining case its overall value, and the PTV and OAR components, were unchanged but the balance of OAR sparing differed. PTV RMS deviation was improved in 13 cases and unchanged in two. The OAR component was reduced in 13 plans. In one case the OAR component increased but the composite metric decreased ‐ a 4 Gy increase in OAR metrics was balanced by a reduction in PTV RMS deviation from 2.8% to 2.6%. Conclusion: Fluence‐based trajectory optimization improved plan quality as defined by the composite metric. While dose differences were case specific, fluence‐based optimization improved both PTV and OAR dosimetry in 80% of cases.