Yujia Qian, Riao Dao, Lewei Zhao, Shiyi Zhou, Qingkun Fan, Guillaume Janssens, Bas A. de Jong, Stefan Both, Erik Korevaar, Ting Hu, Gang Peng, Zhiyong Yang, Sheng Zhang, FangFang Yin, Manju Liu, Kunyu Yang, Hong Quan, Xuanfeng Ding, Gang Liu
{"title":"一种基于机器特异性输送特性的质子弧治疗自适应能量转换算法。","authors":"Yujia Qian, Riao Dao, Lewei Zhao, Shiyi Zhou, Qingkun Fan, Guillaume Janssens, Bas A. de Jong, Stefan Both, Erik Korevaar, Ting Hu, Gang Peng, Zhiyong Yang, Sheng Zhang, FangFang Yin, Manju Liu, Kunyu Yang, Hong Quan, Xuanfeng Ding, Gang Liu","doi":"10.1002/mp.70011","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) is treatment delivery efficiency. Previous studies focus on reducing the number of energy layers by ascending switching to shorten the beam delivery time. However, this is not true of all proton therapy systems. The new energy layer switching system was recently upgraded in the University Medical Center Groningen (UMCG), which enables a fast energy layer ascending switching (ELAS).</p>\n </section>\n \n <section>\n \n <h3> Purpose</h3>\n \n <p>We introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc-<sub>AES</sub>) based on the machine-specific delivery characteristics of proton therapy systems.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The SPArc-<sub>AES</sub> optimization algorithm is based on the polynomial increasing feature of energy layer ascending switching. <i>K</i>-Medoids clustering analysis and simulated annealing algorithm were used to optimize the energy delivery sequence. Ten cases were selected to evaluate the plan quality, plan robustness, and the delivery efficiency compared with the previously SPArc energy sequence optimization algorithm, SPArc_seq.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Without extra constraints in the energy ascending constraints, the SPArc-<sub>AES</sub> offers a better plan quality and robustness, while the treatment delivery efficiency was significantly improved compared to the SPArc_seq. More specifically, SPArc-<sub>AES</sub> effectively shortened the energy layer switching time and the beam delivery time by 34.03% and 31.10%, respectively, while offering better target dose conformality and generally lower dose to organs-at-risk.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Based on the machine-specific delivery characteristics, we introduced a novel adaptive energy switching algorithm for efficient SPArc optimization, which could significantly improve delivery efficiency while enhancing the plan quality by eliminating no longer necessary constraints on the total number of energy layer ascending switching.</p>\n </section>\n </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 10","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel adaptive energy switching algorithm for proton arc therapy based on the machine-specific delivery characteristics\",\"authors\":\"Yujia Qian, Riao Dao, Lewei Zhao, Shiyi Zhou, Qingkun Fan, Guillaume Janssens, Bas A. de Jong, Stefan Both, Erik Korevaar, Ting Hu, Gang Peng, Zhiyong Yang, Sheng Zhang, FangFang Yin, Manju Liu, Kunyu Yang, Hong Quan, Xuanfeng Ding, Gang Liu\",\"doi\":\"10.1002/mp.70011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) is treatment delivery efficiency. Previous studies focus on reducing the number of energy layers by ascending switching to shorten the beam delivery time. However, this is not true of all proton therapy systems. The new energy layer switching system was recently upgraded in the University Medical Center Groningen (UMCG), which enables a fast energy layer ascending switching (ELAS).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Purpose</h3>\\n \\n <p>We introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc-<sub>AES</sub>) based on the machine-specific delivery characteristics of proton therapy systems.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>The SPArc-<sub>AES</sub> optimization algorithm is based on the polynomial increasing feature of energy layer ascending switching. <i>K</i>-Medoids clustering analysis and simulated annealing algorithm were used to optimize the energy delivery sequence. Ten cases were selected to evaluate the plan quality, plan robustness, and the delivery efficiency compared with the previously SPArc energy sequence optimization algorithm, SPArc_seq.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Without extra constraints in the energy ascending constraints, the SPArc-<sub>AES</sub> offers a better plan quality and robustness, while the treatment delivery efficiency was significantly improved compared to the SPArc_seq. More specifically, SPArc-<sub>AES</sub> effectively shortened the energy layer switching time and the beam delivery time by 34.03% and 31.10%, respectively, while offering better target dose conformality and generally lower dose to organs-at-risk.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Based on the machine-specific delivery characteristics, we introduced a novel adaptive energy switching algorithm for efficient SPArc optimization, which could significantly improve delivery efficiency while enhancing the plan quality by eliminating no longer necessary constraints on the total number of energy layer ascending switching.</p>\\n </section>\\n </div>\",\"PeriodicalId\":18384,\"journal\":{\"name\":\"Medical physics\",\"volume\":\"52 10\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical physics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70011\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"3","ListUrlMain":"https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.70011","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
A novel adaptive energy switching algorithm for proton arc therapy based on the machine-specific delivery characteristics
Background
One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) is treatment delivery efficiency. Previous studies focus on reducing the number of energy layers by ascending switching to shorten the beam delivery time. However, this is not true of all proton therapy systems. The new energy layer switching system was recently upgraded in the University Medical Center Groningen (UMCG), which enables a fast energy layer ascending switching (ELAS).
Purpose
We introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc-AES) based on the machine-specific delivery characteristics of proton therapy systems.
Methods
The SPArc-AES optimization algorithm is based on the polynomial increasing feature of energy layer ascending switching. K-Medoids clustering analysis and simulated annealing algorithm were used to optimize the energy delivery sequence. Ten cases were selected to evaluate the plan quality, plan robustness, and the delivery efficiency compared with the previously SPArc energy sequence optimization algorithm, SPArc_seq.
Results
Without extra constraints in the energy ascending constraints, the SPArc-AES offers a better plan quality and robustness, while the treatment delivery efficiency was significantly improved compared to the SPArc_seq. More specifically, SPArc-AES effectively shortened the energy layer switching time and the beam delivery time by 34.03% and 31.10%, respectively, while offering better target dose conformality and generally lower dose to organs-at-risk.
Conclusions
Based on the machine-specific delivery characteristics, we introduced a novel adaptive energy switching algorithm for efficient SPArc optimization, which could significantly improve delivery efficiency while enhancing the plan quality by eliminating no longer necessary constraints on the total number of energy layer ascending switching.
期刊介绍：
Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments
Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.
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