Crooks S M, Xing L
Department of Radiation Oncology, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305-5304, USA.
Int J Radiat Oncol Biol Phys. 2002 Nov 15;54(4):1217-24. doi: 10.1016/s0360-3016(02)03810-5.
The purpose of this work was to apply the method of constrained least-squares to inverse treatment planning and to explore its potential for providing a fast interactive planning environment for intensity-modulated radiation therapy (IMRT).
The description of the dose inside a patient is a linear matrix transformation of beamlet weights. The constrained least-squares method adds additional matrix operators and produces beamlet weights by a direct linear transformation. These matrix operators contain a priori knowledge about the radiation distribution. The constrained least-squares technique was applied to obtain IMRT plans for prostate and paraspinal cancer patients and compared with the corresponding plans optimized using the CORVUS inverse planning system.
It was demonstrated that a constrained least-squares technique is suitable for IMRT plan optimization with significantly increased computing speed. For the two cases we have tested, the constrained least-squares method was an order of magnitude faster than conventional iterative techniques because of the avoidance of the iterative calculations. We also found that the constrained least-squares method is capable of generating clinically acceptable treatment plans with less trial-and-error adjustments of system variables, and with improved target volume coverage as well as sensitive structure sparing in comparison with that obtained using CORVUS.
The constrained least-squares method has the advantage that it does not require iterative calculation and thus significantly speeds up the therapeutic plan optimization process. Besides shedding important insight into the inverse planning problem, the technique has strong potential to provide a fast and interactive environment for IMRT treatment planning.
本研究旨在将约束最小二乘法应用于逆向治疗计划，并探索其为调强放射治疗（IMRT）提供快速交互式计划环境的潜力。
患者体内剂量的描述是子野权重的线性矩阵变换。约束最小二乘法添加了额外的矩阵算子，并通过直接线性变换生成子野权重。这些矩阵算子包含有关辐射分布的先验知识。将约束最小二乘法应用于前列腺癌和脊柱旁癌患者以获得IMRT计划，并与使用CORVUS逆向计划系统优化的相应计划进行比较。
结果表明，约束最小二乘法适用于IMRT计划优化，且计算速度显著提高。对于我们测试的两种情况，由于避免了迭代计算，约束最小二乘法比传统迭代技术快一个数量级。我们还发现，与使用CORVUS获得的结果相比，约束最小二乘法能够生成临床上可接受的治疗计划，减少系统变量的反复调整，改善靶区覆盖，并更好地保护敏感结构。
约束最小二乘法的优点是不需要迭代计算，从而显著加快了治疗计划优化过程。除了为逆向计划问题提供重要见解外，该技术还有很强的潜力为IMRT治疗计划提供快速且交互式的环境。
