Yoo Sua, Kowalok Michael E, Thomadsen Bruce R, Henderson Douglass L
Department of Medical Physics, University of Wisconsin-Madison, 1530 MSC, 1300 University Ave., Madison, WI 53706, USA.
Phys Med Biol. 2003 Dec 21;48(24):4077-90. doi: 10.1088/0031-9155/48/24/006.
We have developed an efficient treatment-planning algorithm for prostate implants that is based on region of interest (ROI) adjoint functions and a greedy heuristic. For this work, we define the adjoint function for an ROI as the sensitivity of the average dose in the ROI to a unit-strength brachytherapy source at any seed position. The greedy heuristic uses a ratio of target and critical structure adjoint functions to rank seed positions according to their ability to irradiate the target ROI while sparing critical structure ROIs. This ratio is computed once for each seed position prior to the optimization process. Optimization is performed by a greedy heuristic that selects seed positions according to their ratio values. With this method, clinically acceptable treatment plans are obtained in less than 2 s. For comparison, a branch-and-bound method to solve a mixed integer-programming model took more than 50 min to arrive at a feasible solution. Both methods achieved good treatment plans, but the speedup provided by the greedy heuristic was a factor of approximately 1500. This attribute makes this algorithm suitable for intra-operative real-time treatment planning.
我们已经开发出一种用于前列腺植入物的高效治疗计划算法，该算法基于感兴趣区域（ROI）伴随函数和贪婪启发式算法。在这项工作中，我们将ROI的伴随函数定义为ROI中平均剂量对任意种子位置处单位强度近距离放射治疗源的敏感度。贪婪启发式算法使用目标和关键结构伴随函数的比率，根据种子位置照射目标ROI同时 sparing关键结构ROI的能力对种子位置进行排序。在优化过程之前，为每个种子位置计算一次该比率。通过根据比率值选择种子位置的贪婪启发式算法进行优化。使用这种方法，在不到2秒的时间内即可获得临床可接受的治疗计划。相比之下，用于解决混合整数规划模型的分支定界法需要超过50分钟才能得出可行解。两种方法都实现了良好的治疗计划，但贪婪启发式算法提供的加速比约为1500倍。这一特性使得该算法适用于术中实时治疗计划。
