Volumetric modulated arc therapy planning is a challenging problem in high-dimensional, non-convex optimization. Traditionally, heuristics such as fluence-map-optimization-informed segment initialization use locally optimal solutions to begin the search of the full arc therapy plan space from a reasonable starting point. These routines facilitate arc therapy optimization such that clinically satisfactory radiation treatment plans can be created in about 10 minutes. However, current optimization algorithms favor solutions near their initialization point and are slower than necessary due to plan overparameterization. In this work, arc therapy overparameterization is addressed by reducing the effective dimension of treatment plans with unsupervised deep learning. An optimization engine is then built based on low-dimensional arc representations which facilitates faster planning times.

中文翻译：

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潜空间弧治疗优化

体积调制弧形治疗计划是高维非凸优化中的一个具有挑战性的问题。传统上，诸如影响图优化通知的段初始化之类的启发式方法使用局部最优解来从合理的起点开始搜索完整的弧形治疗计划空间。这些例行程序有助于优化弧光治疗，从而可以在大约 10 分钟内创建临床上令人满意的放射治疗计划。然而，当前的优化算法倾向于在其初始化点附近的解决方案，并且由于计划过度参数化而比所需的速度慢。在这项工作中，通过使用无监督深度学习减少治疗计划的有效维度来解决弧形治疗过度参数化问题。