Monte Carlo Based Continuous Aperture Optimization for Volumetric Modulated Arc Therapy On MR-Linacs
S Su1*, P Atwal2, I Popescu3, (1) University of Michigan, Ann Arbor, MI, (2) BC Cancer - Abbotsford, Abbotsford, BC, CA, (3) BC Cancer - Vancouver, Vancouver, BC, CA
Presentations
MO-G-BRC-5 (Monday, 7/11/2022) 2:45 PM - 3:45 PM [Eastern Time (GMT-4)]
Ballroom C
Purpose: Currently, the commercial treatment planning systems for magnetic resonance (MR)-linacs only support step-and-shoot intensity-modulated radiation therapy. However, a recent study has shown the feasibility of delivering arc therapy on a MR-linac, which is expected to improve dose distributions and delivery speed. Due to the electron return effect, a Monte Carlo (MC) algorithm is ideally suited for the inverse treatment planning of this technique, which has not been done yet. We propose a novel MC-based continuous aperture optimization (MCCAO) algorithm for volumetric modulated arc therapy (VMAT) technique, which we applied in the context of a generic MR-linac.
Methods: A unique feature of MCCAO is that the continuous character of gantry rotation and multi-leaf collimator (MLC) motion is accounted for at every stage of the optimization. Only one simulation of 4D dose distribution is required for the entire optimization process. A phase space is scored at the top surface of the MLC and the energy deposition of each particle history is mapped to its position in this phase space. A progressive sampling method is used, where both MLC leaf positions and monitor unit weights are randomly changed, while respecting the machine delivery constraints. Due to the continuous nature of the leaf motion, such changes affect not only a single control point, but propagate to the adjacent ones as well, and the corresponding dose distribution changes are accounted for. A dose-volume cost function is used, which includes the MC statistical uncertainty.
Results: We applied our optimization technique to a lung stereotactic body radiation therapy plan, using a 6 MV flattening-filter-free beam model, with and without a 1.5 T magnetic field. In both cases, all clinical constraints were met.
Conclusion: We proved that the novel MCCAO method generates VMAT plans for a MR-linac of comparable clinical quality to conventional VMAT.
Keywords
Monte Carlo, Magnetic Fields, Inverse Planning
Taxonomy
IM/TH- MRI in Radiation Therapy: MRI/Linear accelerator combined dose optimization
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