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用于三维逆向治疗计划的快速迭代算法。

Fast iterative algorithms for three-dimensional inverse treatment planning.

作者信息

Xing L, Hamilton R J, Spelbring D, Pelizzari C A, Chen G T, Boyer A L

机构信息

Department of Radiation Oncology, Stanford University, California 94305-5304, USA.

出版信息

Med Phys. 1998 Oct;25(10):1845-9. doi: 10.1118/1.598374.

Abstract

Three types of iterative algorithms, algebraic inverse treatment planning (AITP), simultaneous iterative inverse treatment planning (SIITP), and iterative least-square inverse treatment planning (ILSITP), differentiated according to their updating sequences, were generalized to three dimension with true beam geometry and dose model. A rapid ray-tracing approach was developed to optimize the primary beam components. Instead of recalculating the dose matrix at each iteration, the dose distribution was generated by scaling up or down the dose matrix elements of the previous iteration. This significantly increased the calculation speed. The iterative algorithms started with an initial intensity profile for each beam, specified by a two-dimensional pixel beam map of M elements. The calculation volume was divided into N voxels, and the calculation was done by repeatedly comparing the calculated and desired doses and adjusting the values of the beam map elements to minimize an objective function. In AITP, the iteration is performed voxel by voxel. For each voxel, the dose discrepancy was evaluated and the contributing pencil beams were updated. In ILSITP and SIITP, the iteration proceeded pencil beam by pencil beam instead of voxel by voxel. In all cases, the iteration procedure was repeated until the best possible dose distribution was achieved. The algorithms were applied to two examples and the results showed that the iterative techniques were able to produce superior isodose distributions.

摘要

根据更新顺序区分的三种迭代算法,即代数逆治疗计划(AITP)、同步迭代逆治疗计划(SIITP)和迭代最小二乘逆治疗计划(ILSITP),被推广到具有真实射束几何形状和剂量模型的三维空间。开发了一种快速射线追踪方法来优化初级射束组件。在每次迭代时不再重新计算剂量矩阵,而是通过放大或缩小上一次迭代的剂量矩阵元素来生成剂量分布。这显著提高了计算速度。迭代算法从每个射束的初始强度分布开始,由一个M个元素的二维像素射束图指定。计算体积被划分为N个体素,通过反复比较计算剂量和期望剂量,并调整射束图元素的值以最小化目标函数来进行计算。在AITP中,逐个体素进行迭代。对于每个体素,评估剂量差异并更新贡献的笔形束。在ILSITP和SIITP中,逐笔形束而不是逐个体素进行迭代。在所有情况下,重复迭代过程,直到获得最佳可能的剂量分布。将这些算法应用于两个示例,结果表明迭代技术能够产生更优的等剂量分布。

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