论文标题
放射疗法治疗机参数的对抗预测
Adversarial Prediction of Radiotherapy Treatment Machine Parameters
论文作者
论文摘要
现代的外束癌放射疗法将处方辐射剂量适用于肿瘤靶标，同时最小化附近的脆弱器官风险（OARS）。制定治疗计划是困难的，而且耗时，而不能保证最佳性。基于知识的计划（KBP）通过基于先前临床质量计划的种群指导计划模型来指导计划，从而减轻了这种不确定性。我们已经开发了一种以KBP为灵感的计划模型，该模型将计划预测为处理机器参数的实现。这些是线性加速器（Linac）龙门角，多叶准准直仪（MLC）孔的孔，可塑造光束的孔，以及可以在坐标框架框架同构中以图形方式表示患者靶靶性解剖学的坐标框架同构（光束的视图）。这些配对的数据序列有条件的生成对抗网络（CGAN）估算了新型患者的MLC孔径和权重，从而预测了治疗计划。预测的计划的拨片与临床计划接近；预测的目标覆盖范围需要优化才能匹配临床计划的质量。尽管如此，预测的计划可以作为计划质量的下限，并且通过初始化MLC孔径形状和重量细化可以大大减少该细化的计算时间。
Modern external beam cancer radiotherapy applies prescribed radiation doses to tumor targets while minimally affecting nearby vulnerable organs-at-risk (OARs). Creating a treatment plan is difficult and time-consuming with no guarantee of optimality. Knowledge-based planning (KBP) mitigates this uncertainty by guiding planning with probabilistic models based on populations of prior clinical-quality plans. We have developed a KBP-inspired planning model that predicts plans as realizations of the treatment machine parameters. These are tuples of linear accelerator (Linac) gantry angles, multi-leaf collimator (MLC) apertures that shape the beam, and aperture-intensity weights that can be represented graphically in a coordinate frame isomorphic with projections (beam's-eye views) of the patient's target anatomy. These paired data train conditional generative adversarial networks (cGANs) that estimate the MLC apertures and weights for a novel patient, thereby predicting a treatment plan. The predicted plans' OAR sparing is close to that of the clinical plans; the predicted target coverage requires refinement to match the clinical plans' quality. Nonetheless, the predicted plans can serve as lower bounds on plan quality, and by initializing the MLC aperture shape and weight refinement can substantially reduce the compute times for that refinement.
