Jacques R, McNutt T
Johns Hopkins University, Baltimore, MD.
Med Phys. 2012 Jun;39(6Part18):3822. doi: 10.1118/1.4735597.
We developed a better method of accounting for the effects of heterogeneity in convolution algorithms. We integrated this method into our GPU-accelerated, multi-energetic convolution/superposition (C/S) implementation. In doing so, we have created a new dose algorithm: heterogeneity compensated superposition (HCS).
Convolution in the spherical density-scaled distance space, a.k.a. C/S, has proven to be a good estimator of the dose deposited in a homogeneous volume. However, near heterogeneities electron disequilibrium occurs, leading to faster fall-off and re-buildup than predicted by C/S. We propose to filter the actual patient density in a position and direction sensitive manner, allowing the dose deposited near interfaces to be increased or decreased relative to traditional C/S. We implemented the effective density function as a multivariate first-order recursive filter. We compared HCS against traditional C/S using the ICCR 2000 Monte-Carlo accuracy benchmark, 23 similar accuracy benchmarks and 5 patient cases. For the patient cases, we created custom routines capable of using the discrete material mappings used by Monte-Carlo. C/S normally considers each voxel to be a mixture of materials based on a piecewise-linear density look-up table.
Multi-energetic HCS increased the dosimetric accuracy for the vast majority of voxels; in many cases near Monte-Carlo results were achieved. HCS improved the mean Van Dyk error by 0.79 (% of Dmax or mm) on average for the patient volumes; reducing the mean error from 1.93%|mm to 1.14%|mm. We found a mean error difference of up to 0.30 %|mm between linear and discrete material mappings. Very low densities (i.e. <0.1 g / cm ) remained problematic, but may be solvable with a better filter function.
We have developed a novel dose calculation algorithm based on the principals of C/S that better accounts for the electron disequilibrium caused by patient heterogeneity. This work was funded in part by the National Science Foundation under Grant No. EEC9731748, in part by Johns Hopkins University internal funds and in part by Elekta.
我们开发了一种更好的方法来考虑卷积算法中异质性的影响。我们将此方法集成到我们的GPU加速的多能卷积/叠加（C/S）实现中。通过这样做，我们创建了一种新的剂量算法：异质性补偿叠加（HCS）。
在球形密度缩放距离空间中的卷积，即C/S，已被证明是均匀体积中沉积剂量的良好估计器。然而，在异质性附近会发生电子不平衡，导致剂量下降和再积累比C/S预测的更快。我们建议以位置和方向敏感的方式对实际患者密度进行滤波，使界面附近沉积的剂量相对于传统C/S增加或减少。我们将有效密度函数实现为多元一阶递归滤波器。我们使用ICCR 2000蒙特卡罗精度基准、23个类似的精度基准和5个患者病例将HCS与传统C/S进行了比较。对于患者病例，我们创建了能够使用蒙特卡罗所用离散材料映射的自定义例程。C/S通常基于分段线性密度查找表将每个体素视为材料混合物。
多能HCS提高了绝大多数体素的剂量学精度；在许多情况下，接近蒙特卡罗结果。对于患者体积，HCS平均将范戴克平均误差提高了0.79（Dmax的百分比或毫米）；将平均误差从1.93%|毫米降低到1.14%|毫米。我们发现线性和离散材料映射之间的平均误差差异高达0.30%|毫米。非常低的密度（即<0.1 g/cm）仍然存在问题，但可能通过更好的滤波函数解决。
我们基于C/S原理开发了一种新颖的剂量计算算法，该算法能更好地考虑患者异质性引起的电子不平衡。这项工作部分由美国国家科学基金会根据资助号EEC9731748资助，部分由约翰霍普金斯大学内部资金资助，部分由医科达资助。
