目的探究医科达Agility多叶准直器直线加速器在调强放射治疗计划中的剂量学优势。方法选择采用Monaco系统的30例肿瘤患者,其中男性14例,女性16例;年龄44~69岁,平均年龄58岁;肿瘤类型,头部肿瘤5例,颈部肿瘤5例,胸部肿瘤5例,乳腺癌5例,腹...目的探究医科达Agility多叶准直器直线加速器在调强放射治疗计划中的剂量学优势。方法选择采用Monaco系统的30例肿瘤患者,其中男性14例,女性16例;年龄44~69岁,平均年龄58岁;肿瘤类型,头部肿瘤5例,颈部肿瘤5例,胸部肿瘤5例,乳腺癌5例,腹部肿瘤5例,盆腔肿瘤5例。先用MLCi2多叶准直器进行放射治疗设计(MLCi2 MLC计划)。不改变计划,再用Agility多叶准直器进行放射治疗设计(Agility MLC计划)。加速器升级成Agility多叶准直器前后调强放射治疗计划的适形度指数、均匀性指数及各类危及器官剂量的差异性进行比较。结果不同放射治疗设计下,30例癌症患者计划靶区的均匀性指数(0.13±0.07 vs 0.10±0.05)和适形度指数(0.73±0.17 vs 0.75±0.13),腹部肿瘤患者计划靶区的均匀性指数(0.12±0.09 vs 0.09±0.03)和适形度指数(0.78±0.05 vs 0.81±0.06),以及肺(V20)[(23.53±11.80)cm^(3)vs(20.85±9.23)cm^(3)]、小肠(D_(max))[(5344.67±407.89)cGy vs(5244.76±361.13)cGy]、腮腺(V_(50))[(18.34±7.72)cm^(3)vs(12.23±10.27)cm^(3)]重要危及器官差异具有统计学意义(P<0.05)。Agility多叶准直器会产生大量的孤岛野,孤岛野达到总射野数的50.0%、52.3%、55.6%,孤岛野会大幅度提高双靶区患者的靶区适形度和均匀性。结论Agility多叶准直器对肿瘤的调强放射治疗计划的靶区均匀性和危及器官的保护限制均具有优势,尤其针对腹部肿瘤和双靶区患者。展开更多
In the quantum Monte Carlo(QMC)method,the pseudo-random number generator(PRNG)plays a crucial role in determining the computation time.However,the hidden structure of the PRNG may lead to serious issues such as the br...In the quantum Monte Carlo(QMC)method,the pseudo-random number generator(PRNG)plays a crucial role in determining the computation time.However,the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process.Here,we systematically analyze the performance of different PRNGs on the widely used QMC method known as the stochastic series expansion(SSE)algorithm.To quantitatively compare them,we introduce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms.After testing several representative observables of the Heisenberg model in one and two dimensions,we recommend the linear congruential generator as the best choice of PRNG.Our work not only helps improve the performance of the SSE method but also sheds light on the other Markov-chain-based numerical algorithms.展开更多
文摘目的探究医科达Agility多叶准直器直线加速器在调强放射治疗计划中的剂量学优势。方法选择采用Monaco系统的30例肿瘤患者,其中男性14例,女性16例;年龄44~69岁,平均年龄58岁;肿瘤类型,头部肿瘤5例,颈部肿瘤5例,胸部肿瘤5例,乳腺癌5例,腹部肿瘤5例,盆腔肿瘤5例。先用MLCi2多叶准直器进行放射治疗设计(MLCi2 MLC计划)。不改变计划,再用Agility多叶准直器进行放射治疗设计(Agility MLC计划)。加速器升级成Agility多叶准直器前后调强放射治疗计划的适形度指数、均匀性指数及各类危及器官剂量的差异性进行比较。结果不同放射治疗设计下,30例癌症患者计划靶区的均匀性指数(0.13±0.07 vs 0.10±0.05)和适形度指数(0.73±0.17 vs 0.75±0.13),腹部肿瘤患者计划靶区的均匀性指数(0.12±0.09 vs 0.09±0.03)和适形度指数(0.78±0.05 vs 0.81±0.06),以及肺(V20)[(23.53±11.80)cm^(3)vs(20.85±9.23)cm^(3)]、小肠(D_(max))[(5344.67±407.89)cGy vs(5244.76±361.13)cGy]、腮腺(V_(50))[(18.34±7.72)cm^(3)vs(12.23±10.27)cm^(3)]重要危及器官差异具有统计学意义(P<0.05)。Agility多叶准直器会产生大量的孤岛野,孤岛野达到总射野数的50.0%、52.3%、55.6%,孤岛野会大幅度提高双靶区患者的靶区适形度和均匀性。结论Agility多叶准直器对肿瘤的调强放射治疗计划的靶区均匀性和危及器官的保护限制均具有优势,尤其针对腹部肿瘤和双靶区患者。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12274046,11874094,and 12147102)Chongqing Natural Science Foundation(Grant No.CSTB2022NSCQ-JQX0018)Fundamental Research Funds for the Central Universities(Grant No.2021CDJZYJH-003).
文摘In the quantum Monte Carlo(QMC)method,the pseudo-random number generator(PRNG)plays a crucial role in determining the computation time.However,the hidden structure of the PRNG may lead to serious issues such as the breakdown of the Markov process.Here,we systematically analyze the performance of different PRNGs on the widely used QMC method known as the stochastic series expansion(SSE)algorithm.To quantitatively compare them,we introduce a quantity called QMC efficiency that can effectively reflect the efficiency of the algorithms.After testing several representative observables of the Heisenberg model in one and two dimensions,we recommend the linear congruential generator as the best choice of PRNG.Our work not only helps improve the performance of the SSE method but also sheds light on the other Markov-chain-based numerical algorithms.
