(Wednesday, 7/15/2020) 10:30 AM - 11:30 AM [Eastern Time (GMT-4)]
Room: Track 3
Purpose:
Spot-scanning proton arc therapy (SPAT) improves plan dosimetry and delivery efficiency. However, existing greedy and heuristic algorithms do not promise dosimetry or efficiency optimality. To improve the dosimetry, planning and delivery efficiency of SPAT, we developed an optimization framework with integrated energy layer sequencing.
Methods:
The Energy Layer Optimization for SPAT (ELO-SPAT) framework includes dose fidelity, group sparsity regularization, log barrier regularization, and energy-sequencing (ES) penalty. Group sparsity and log barrier function allow one energy layer selected per control point. Since delivery efficiency is most affected by the energy-layer switching-time (ELST) for switching from low to high energies, the ES regularization sorts the delivery sequence from high to low energy to reduce total ELST and subsequently the total delivery time. Four cases including frontal base-of-skull, chordoma, head-and-neck and lung were tested. We compared ELO-SPAT with IMPT using discrete beams and SPArc by Ding et al. For the two arc algorithms, both the plans with and without energy sequencing were created and compared.
Results:
ELO-SPAT reduced the runtime of optimization by 84% on average compared with the greedy SPArc method. By ES regularization, the number of energy switch-up was reduced to under 20 from 40-60 without ES in ELO-SPAT. Compared with the energy sequenced SPArc plans, ELO-SPAT with ES led to 24% less total ELST for synchrotron plans and 14% less for cyclotron plans. Both ELO-SPAT and SPArc achieved better sparing than IMPT. Without ES, ELO-SPAT achieved further OARs improvement than SPArc, with an averaged reduction of OAR [Dmean, Dmax] by [1.57, 3.34] GyRBE. Adding ES regularization, ELO-SPAT reduced OAR [Dmean, Dmax] by [1.42, 2.34] GyRBE from SPArc on average.
Conclusion:
We developed a computationally efficient SPAT optimization method, which solved energy layer selection and sequencing in an integrated framework, generating plans with good dosimetry and high delivery efficiency.
Funding Support, Disclosures, and Conflict of Interest: NIH Grants Nos. R44CA183390, R43CA183390, and R01CA230278
Keywords
Not Applicable / None Entered.
Taxonomy
Not Applicable / None Entered.
Contact Email
