Document Type

Conference Proceeding

Publication Date

6-2023

Publication Title

Medical Physics

Abstract

Purpose: The delivery efficiency is the bottleneck of spot-scanning proton arc therapy (SPArc) because of the numerous energy layers (EL) ascending switches. This study aims to develop a new algorithm to mitigate the need of EL ascending via water equivalent thickness (WET) sector selection followed by the particle swarm optimization (SPArc- particle swarm). Methods: SPArc- particle swarm divided the full arc trajectory into the optimal sectors based on the K-means clustering analysis of the relative mean WET. Within the sector, particle swarm optimization was used to minimize the total energy switch time, in which it optimized the energy selection integrated with EL relationship. Consequently, energy switch up are only allowed between two adjacent sectors. This novel planning framework was implemented on the open-source platform matRad (Department of Medical Physics in Radiation Oncology, German Cancer Research Center-DKFZ). Three representative cases (brain, liver, and prostate cancer patients) were selected for testing purpose. Two plans were generated: SPArc with uniform sector selection (SPArcoriginal) and SPArc-particle swarm. The plan quality and delivery efficiency were evaluated. Results: With a similar plan quality,the delivery efficiency was significantly improved using SPArc-particle swarm compared to the SPArcoriginal. More specifically, it reduces the number of EL switching ascending compared to the SPArcoriginal (from 81 to 7 in brain case, from 71 to 6 in liver case, from 93 to 8 in prostate case), leading to 50-70% delivery time reducing in the SPArc treatment. Conclusion: A novel planning framework SPArc-particle swarmcould significantly improve the delivery efficient which paves the roadmap towards the routine clinical implementation

Volume

50

Issue

6

First Page

e518

Last Page

e519

Comments

American Association of Physicists in Medicine 65th Annual Meeting & Exhibition, July 23-27, 2023, Houston, TX

DOI

10.1002/mp.16525

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