(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose:
Proton arc therapy (PAT) has the potential benefit of improved dose homogeneity and normal tissue sparing. However, conventional PAT plans could be very delivery-inefficient due to an enormous number of spots and energy switches required. In this work, we proposed a novel planning scheme to achieve high quality PAT plans with efficient delivery.
Methods:
We formulated the efficient PAT plan optimization problem as to find a minimal set of energies to approach the dose objectives and solved the optimization using column generation (CG) that progressively improves the plan quality. We used a Head&Neck case to study the proposed PAT-CG scheme and compared the results with those of using IMPT and conventional PAT (PAT-full). All plans were optimized with the PTV objective of 100Gy and underdose penalty being 10x overdose penalty. PTV dose-range (PTV-DR) and maximum OAR dose (OAR-D2) were used as plan quality metrics, and the inverse ratio of delivery time to that of IMPT was used to measure the delivery efficiency.
Results:
CG-based PAT plans achieved comparable plan qualities to those of PAT-full within 4 energy layers (PAT-CG4) and were much better than the IMPT plans. The PTV-DRs were 28.75Gy, 26.42Gy, 24.03Gy, and the OAR-D2s were 86.50Gy, 33.56Gy and 32.59Gy, for IMPT, PAT-CG4 and PAT-full, respectively. The delivery efficiency of PAT-CG4 and PAT-full were 84% and 19%, respectively. PAT-CG4 improved efficiency by four times without sacrificing plan quality compared to PAT-full.
Conclusion:
PAT is known to improve plan quality at the cost of long treatment time. With the novel energy optimization algorithm, it allows time-minimization in spots and energy switches while maintaining the PAT plan quality. The proposed method may pave the way to clinical implementation of PAT.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by NIH grants (R01 CA235723, R01 CA218402).
Keywords
Optimization, Treatment Planning
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