用深度学习快速选质子弧疗能量层，省时又保效果
Unsupervised deep learning model for fast energy layer pre-selection of delivery-efficient proton arc therapy plan optimization of nasopharyngeal carcinoma
- 用点数矩阵表示治疗数据，输入U-Net模型自动选能量层
- 相比传统方法，计划质量提升，能量层切换时间减少37.2%
- 适合需要快速生成高质量质子治疗计划的临床场景
质子弧疗（PAT）是放疗中前景广阔的新兴技术，相比调强质子治疗可提供更优剂量分布和更强鲁棒性。然而，最优能量层（EL）序列的确定因计算量大、交付时间长而困难。本研究提出一种无监督深度学习模型，用于快速预选能量层，在减少能量层切换（ELS）时间的同时保持高计划质量。我们引入一种新数据表示法——点数表示法，将靶区与危及器官（OAR）在不同机架角和能量层下交集的质子点数量编码为矩阵。该表示作为U-Net结构模型SPArc_dl的输入，通过三目标函数训练：最大化靶区点数、最小化危及器官点数、降低ELS时间。在35例鼻咽癌病例上评估，结果显著优于SPArc_particle_swarm（SPArc_ps）： conformity index 提升0.1（p<0.01），homogeneity index 降低0.71（p<0.01），脑干平均剂量降低0.25（p<0.01），ELS时间缩短37.2%（p<0.01）。意外发现维持原能量层顺序比降序更高效。SPArc_dl推理时间小于1秒。但其计划在鲁棒性方面仍有不足。所提点数表示法为无监督深度学习应用于能量层预选奠定基础。SPArc_dl是快速生成高质量质子弧疗计划的有效工具。
原文摘要 · Abstract (English)
Proton arc therapy (PAT) is an emerging and promising modality in radiotherapy, offering improved dose distribution and treatment robustness over intensity-modulated proton therapy. Yet, identifying the optimal energy layer (EL) sequence remains challenging due to the intensive computational demand and prolonged treatment delivery time. This study proposes an unsupervised deep learning model for fast EL pre-selection that minimizes EL switch (ELS) time while maintaining high plan quality. We introduce a novel data representation method, spot-count representation, which encodes the number of proton spots intersecting the target and organs at risk (OAR) in a matrix structured by sorted gantry angles and energy layers. This representation serves as the input of an U-Net style architecture, SPArc_dl, which is trained using a tri-objective function: maximizing spot-counts on target, minimizing spot-counts on OAR, and reducing ELS time. The model is evaluated on 35 nasopharyngeal cancer cases, and its performance is compared to SPArc_particle_swarm (SPArc_ps). SPArc_dl produces EL pre-selection that significantly improves both plan quality and delivery efficiency. Compared to SPArc_ps, it enhances the conformity index by 0.1 (p<0.01), reduces the homogeneity index by 0.71 (p<0.01), lowers the brainstem mean dose by 0.25 (p<0.01), and shortens the ELS time by 37.2% (p < 0.01). The results unintentionally reveal employing unchanged ELS is more time-wise efficient than descended ELS. SPArc_dl's inference time is within 1 second. However, SPArc_dl plan demonstrates limitation in robustness. The proposed spot-count representation lays a foundation for incorporating unsupervised deep learning approaches into EL pre-selection task. SPArc_dl is a fast tool for generating high-quality PAT plans by strategically pre-selecting EL to reduce delivery time while maintaining excellent dosimetric performance.
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