通过束流图扩散模型生成和基于LSTM的优化学习优化放射治疗计划
Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization
AI总结：
本文提出一种扩散驱动的学习优化方法，用于端到端的VMAT计划，通过生成可行的束流图并利用LSTM优化模块提高计划效率和机器可交付性。
摘要：
体积调制弧形放疗（VMAT）是现代放射治疗的基石，能够实现高适应性的肿瘤照射和健康组织保护。然而，其计划过程涉及多叶准直器、监控单位和剂量参数的逆向和嵌套优化，并需确保其一致性以保证机械可交付性。然而，当治疗配置变化时，这一过程往往需要重复优化，导致每个患者规划时间显著增加。为了解决这些问题，我们提出了一种扩散驱动的学习优化（L2O）方法，用于端到端的VMAT计划。一个分布匹配的蒸馏扩散模型学习了一个临床可行的束流图流形，使其能够实现一次生成。在此基础上，一个基于LSTM的L2O模块学习梯度更新动态，以在推理过程中快速细化束流图以达到规定的剂量目标。在临床和公开前列腺癌队列上的实验结果表明，与目前可用的端到端VMAT计划程序相比，该方法在规划效率、灵活性和机器可交付性方面有所改进。
英文摘要：
Volumetric Modulated Arc Therapy (VMAT) is a cornerstone of modern radiation therapy, enabling highly conformal tumor irradiation and healthy-tissue sparing. Yet, its planning solves inverse and nested optimization for multi-leaf collimators, monitor units and dose parameters, while enforcing their consistency to ensure mechanical deliverability. Nevertheless, this process often requires repeated re-optimization when treatment configurations change, resulting in substantial planning time per patient. To address these problems, we present a diffusion-driven Learning-to-Optimize (L2O) method for end-to-end VMAT planning. A distribution-matching distilled diffusion model learns a clinically feasible manifold of fluence maps, enabling their one-shot generation. On top of this, an LSTM-based L2O module learns gradient update dynamics to swiftly refine fluence maps toward prescribed dose objectives during inference. Experimental results on clinical and public prostate cancer cohorts demonstrate improved planning efficiency, flexibility, and machine deliverability over currently available end-to-end VMAT planners.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
