Craig Tim, Battista Jerry, Van Dyk Jake
London Regional Cancer Centre, Department of Medical Biophysics, University of Western Ontario, London, Ontario N6A 4L6, Canada.
Med Phys. 2003 Aug;30(8):2012-20. doi: 10.1118/1.1589493.
Convolution methods can be used to model the effect of geometric uncertainties on the planned dose distribution in radiation therapy. This requires several assumptions, including that the patient is treated with an infinite number of fractions, each delivering an infinitesimally small dose. The error resulting from this assumption has not been thoroughly quantified. This is investigated by comparing dose distributions calculated using the Convolution method with the result of Stochastic simulations of the treatment. Additionally, the dose calculated using the conventional Static method, a Corrected Convolution method, and a Direct Simulation are compared to the Stochastic result. This analysis is performed for single beam, parallel opposed pair, and four-field box techniques in a cubic water phantom. Treatment plans for a simple and a complex idealized anatomy were similarly analyzed. The average maximum error using the Static method for a 30 fraction simulation for the three techniques in phantoms was 23%, 11% for Convolution, 10% for Corrected Convolution, and 10% for Direct Simulation. In the two anatomical examples, the mean error in tumor control probability for Static and Convolution methods was 7% and 2%, respectively, of the result with no uncertainty, and 35% and 9%, respectively, for normal tissue complication probabilities. Convolution provides superior estimates of the delivered dose when compared to the Static method. In the range of fractions used clinically, considerable dosimetric variations will exist solely because of the random nature of the geometric uncertainties. However, the effect of finite fractionation appears to have a greater impact on the dose distribution than plan evaluation parameters.
卷积方法可用于模拟几何不确定性对放射治疗中计划剂量分布的影响。这需要几个假设，包括患者接受无限多个分次治疗，每次给予无限小的剂量。由该假设导致的误差尚未得到充分量化。通过将使用卷积方法计算的剂量分布与治疗的随机模拟结果进行比较来对此进行研究。此外，还将使用传统静态方法、校正卷积方法和直接模拟计算的剂量与随机结果进行比较。在立方水体模中对单束、平行相对野和四野盒式技术进行了此分析。对简单和复杂理想化解剖结构的治疗计划也进行了类似分析。在体模中对三种技术进行30次分次模拟时，使用静态方法的平均最大误差为23%，卷积方法为11%，校正卷积方法为10%，直接模拟为10%。在两个解剖学示例中，静态和卷积方法的肿瘤控制概率平均误差分别为无不确定性结果的7%和2%，正常组织并发症概率分别为35%和9%。与静态方法相比，卷积能更好地估计实际给予的剂量。在临床使用的分次范围内，仅由于几何不确定性的随机性就会存在相当大的剂量学变化。然而，有限分次的影响似乎对剂量分布的影响比对计划评估参数的影响更大。
