首页> 外文期刊>Medical Physics >SU‐C‐BRC‐03: Development of a Novel Strategy for On‐Demand Monte Carlo and Deterministic Dose Calculation Treatment Planning and Optimization for External Beam Photon and Particle Therapy
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SU‐C‐BRC‐03: Development of a Novel Strategy for On‐Demand Monte Carlo and Deterministic Dose Calculation Treatment Planning and Optimization for External Beam Photon and Particle Therapy

机译:SU-C-BRC-03:开发一种新颖的按需蒙特卡罗策略和确定性剂量计算治疗计划和外部光束光子和颗粒疗法的优化

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Purpose: Accurate and fast dose calculation is a prerequisite of precision radiation therapy in modern photon and particle therapy. While Monte Carlo (MC) dose calculation provides high dosimetric accuracy, the drastically increased computational time hinders its routine use. Deterministic dose calculation methods are fast, but problematic in the presence of tissue density inhomogeneity. We leverage the useful features of deterministic methods and MC to develop a hybrid dose calculation platform with autonomous utilization of MC and deterministic calculation depending on the local geometry, for optimal accuracy and speed. Methods: Our platform utilizes a Geant4 based “localized Monte Carlo” (LMC) method that isolates MC dose calculations only to volumes that have potential for dosimetric inaccuracy. In our approach, additional structures are created encompassing heterogeneous volumes. Deterministic methods calculate dose and energy fluence up to the volume surfaces, where the energy fluence distribution is sampled into discrete histories and transported using MC. Histories exiting the volume are converted back into energy fluence, and transported deterministically. By matching boundary conditions at both interfaces, deterministic dose calculation account for dose perturbations “downstream” of localized heterogeneities. Hybrid dose calculation was performed for water and anthropomorphic phantoms. Results: We achieved 1% agreement between deterministic and MC calculations in the water benchmark for photon and proton beams, and dose differences of 2%–15% could be observed in heterogeneous phantoms. The saving in computational time (a factor ~4–7 compared to a full Monte Carlo dose calculation) was found to be approximately proportional to the volume of the heterogeneous region. Conclusion: Our hybrid dose calculation approach takes advantage of the computational efficiency of deterministic method and accuracy of MC, providing a practical tool for high performance dose calculation in modern RT. The approach is generalizable to all modalities where heterogeneities play a large role, notably particle therapy.
机译:目的:准确和快速剂量计算是现代光子和颗粒疗法精密放射治疗的先决条件。虽然Monte Carlo(MC)剂量计算提供高剂量准确度,但急剧增加的计算时间阻碍了其常规使用。确定性剂量计算方法是快速的,但在组织密度不均匀性存在下有问题。我们利用确定性方法和MC的有用特征来开发混合剂量计算平台,其自主利用MC和确定性计算,根据局部几何体,以获得最佳的精度和速度。方法:我们的平台利用基于Geant4的“局部蒙特卡罗”(LMC)方法,该方法将MC剂量计算分离到具有潜力的MC剂量计算。在我们的方法中,额外的结构被创建包含异构体积。确定性方法计算给体积表面的剂量和能量流量,其中能量流量分布被取样到离散历史中并使用MC运输。离开体积的历史转换回能量流量,并确定地运输。通过匹配两个接口的边界条件,确定性剂量计算算法用于局部异质性“下游”的剂量扰动。对水和拟蒽晶模型进行杂化剂量计算。结果:我们实现了光子和质子梁的水基准测试中的确定性和MC计算之间的达成协议,并且在异质的模糊中可以观察到2%-15%的剂量差异。发现计算时间(与完全蒙特卡罗剂量计算相比的因子〜4-7)近似与异质区域的体积成比例。结论:我们的混合剂量计算方法利用了MC的确定性方法和准确性的计算效率,为现代RT中提供了高性能剂量计算的实用工具。该方法是概遍的所有模式,其中异质性发挥着大作用,特别是颗粒疗法。

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