用扩散模型生成放疗计划，一次成型，快速优化。
Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization
- 用扩散模型学习临床可行的射野强度分布，一次生成。
- 结合LSTM动态优化，快速逼近理想剂量分布。
- 提升计划效率与设备可执行性，适合临床部署。
调强弧形放疗（VMAT）是现代放射治疗的核心技术，可实现肿瘤精准照射并保护健康组织。然而，其计划过程需对多叶准直器、监测单位和剂量参数进行嵌套逆向优化，并确保机械可实施性，导致治疗方案调整时需反复重优化，耗时长。为此，本文提出一种基于扩散模型的端到端学习优化（L2O）方法。通过分布匹配的蒸馏扩散模型学习临床可行的射野强度分布流形，实现一次性生成；在此基础上，采用基于LSTM的L2O模块学习梯度更新动态，在推理阶段快速修正强度分布以逼近目标剂量。在临床及公开前列腺癌数据集上的实验表明，该方法相比现有端到端VMAT规划器，显著提升了规划效率、灵活性与机器可执行性。
原文摘要 · Abstract (English)
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.
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