School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA.
Phys Med Biol. 2010 Sep 7;55(17):5189-202. doi: 10.1088/0031-9155/55/17/019. Epub 2010 Aug 16.
Cancer treatment with ionizing radiation is often compromised by organ motion, in particular for lung cases. Motion uncertainties can significantly degrade an otherwise optimized treatment plan. We present a spatiotemporal optimization method, which takes into account all phases of breathing via the corresponding 4D-CTs and provides a 4D-optimal plan that can be delivered throughout all breathing phases. Monte Carlo dose calculations are employed to warrant for highest dosimetric accuracy, as pertinent to study motion effects in lung. We demonstrate the performance of this optimization method with clinical lung cancer cases and compare the outcomes to conventional gating techniques. We report significant improvements in target coverage and in healthy tissue sparing at a comparable computational expense. Furthermore, we show that the phase-adapted 4D-optimized plans are robust against irregular breathing, as opposed to gating. This technique has the potential to yield a higher delivery efficiency and a decisively shorter delivery time.
癌症的放射治疗常因器官运动而受到影响,尤其是肺部病例。运动不确定性会显著降低原本优化的治疗计划的效果。我们提出了一种时空优化方法,通过相应的 4D-CT 考虑呼吸的所有阶段,并提供可以在所有呼吸阶段都能实施的 4D 最优计划。为了保证对肺部运动影响的最高剂量学准确性,我们采用了蒙特卡罗剂量计算。我们用临床肺癌病例演示了这种优化方法的性能,并将结果与传统的门控技术进行了比较。我们报告了在可比的计算代价下,目标覆盖度和健康组织保护方面的显著改善。此外,我们表明,与门控相比,自适应相位的 4D 优化计划对不规则呼吸具有更强的鲁棒性。这种技术有可能提高输送效率,并显著缩短输送时间。