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arXiv · 2411.18074

Development and experimental validation of an in-house treatment planning system with greedy energy layer optimization for fast IMPT

Aoxiang Wang·Ya-Nan Zhu·Jufri Setianegara·Yuting Lin·Peng Xiao·Qingguo Xie·Hao Gao
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Abstract

Background: Intensity-modulated proton therapy (IMPT) using pencil beam technique scans tumor in a layer by layer, then spot by spot manner. It can provide highly conformal dose to tumor targets and spare nearby organs-at-risk (OAR). Fast delivery of IMPT can improve patient comfort and reduce motion-induced uncertainties. Since energy layer switching time dominants the plan delivery time, reducing the number of energy layers is important for improving delivery efficiency. Although various energy layer optimization (ELO) methods exist, they are rarely experimentally validated or clinically implemented, since it is technically challenging to integrate these methods into commercially available treatment planning system (TPS) that is not open-source. Methods: The dose calculation accuracy of IH-TPS is verified against the measured beam data and the RayStation TPS. For treatment planning, a novel ELO method via greed selection algorithm is proposed to reduce energy layer switching time and total plan delivery time. To validate the planning accuracy of IH-TPS, the 3D gamma index is calculated between IH-TPS plans and RayStation plans for various scenarios. Patient-specific quality-assurance (QA) verifications are conducted to experimentally verify the delivered dose from the IH-TPS plans for several clinical cases. Results: Dose distributions in IH-TPS matched with those from RayStation TPS, with 3D gamma index results exceeding 95% (2mm, 2%). The ELO method significantly reduced the delivery time while maintaining plan quality. For instance, in a brain case, the number of energy layers was reduced from 78 to 40, leading to a 62% reduction in total delivery time. Patient-specific QA validation with the IBA Proteus ONE proton machine confirmed a >95% pass rate for all cases.

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Aoxiang Wang, Ya-Nan Zhu, Jufri Setianegara, Yuting Lin, Peng Xiao, Qingguo Xie, Hao Gao. 2024-11-27. Development and experimental validation of an in-house treatment planning system with greedy energy layer optimization for fast IMPT. https://arxiv.org/abs/2411.18074

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