用Transformer模型直接从影像生成放疗处方图，速度快且质量高。
Fluence Map Prediction with Deep Learning: A Transformer-based Approach
- 用3D Swin-UNETR网络，从CT和解剖轮廓直接预测九野照射的剂量分布图。
- 测试集上相关系数达0.95，平均绝对误差0.035，伽马通过率85%（3%/3mm）。
- 无需逆向优化，自动化生成，适合临床快速规划需求。
精确的射束强度分布图对调强放疗（IMRT）至关重要，可最大化肿瘤覆盖同时最小化对健康组织的辐射。传统优化耗时且依赖医生经验。本研究提出一种深度学习框架，加速剂量分布图生成并保持临床质量。采用端到端的3D Swin-UNETR网络，基于99例前列腺IMRT病例（79例训练，20例测试），直接从体积CT图像和解剖轮廓预测九野剂量分布图。该基于Transformer的模型通过分层自注意力机制捕捉局部解剖结构与远距离空间依赖性。预测结果导入Eclipse治疗计划系统进行剂量重计算，性能评估使用射束级相关性、空间伽马分析及剂量体积直方图（DVH）指标。模型在测试集上平均R²为0.95±0.02，平均绝对误差0.035±0.008，伽马通过率为85±10%（3%/3mm），预测计划与临床计划在DVH参数上无显著差异。Swin-UNETR框架实现了完全自动化、无需逆向优化的剂量分布图生成，提升空间一致性、准确性和效率，为自动IMRT计划生成提供可扩展、一致的解决方案。
原文摘要 · Abstract (English)
Accurate fluence map prediction is essential in intensity-modulated radiation therapy (IMRT) to maximize tumor coverage while minimizing dose to healthy tissues. Conventional optimization is time-consuming and dependent on planner expertise. This study presents a deep learning framework that accelerates fluence map generation while maintaining clinical quality. An end-to-end 3D Swin-UNETR network was trained to predict nine-beam fluence maps directly from volumetric CT images and anatomical contours using 99 prostate IMRT cases (79 for training and 20 for testing). The transformer-based model employs hierarchical self-attention to capture both local anatomical structures and long-range spatial dependencies. Predicted fluence maps were imported into the Eclipse Treatment Planning System for dose recalculation, and model performance was evaluated using beam-wise fluence correlation, spatial gamma analysis, and dose-volume histogram (DVH) metrics. The proposed model achieved an average R^2 of 0.95 +/- 0.02, MAE of 0.035 +/- 0.008, and gamma passing rate of 85 +/- 10 percent (3 percent / 3 mm) on the test set, with no significant differences observed in DVH parameters between predicted and clinical plans. The Swin-UNETR framework enables fully automated, inverse-free fluence map prediction directly from anatomical inputs, enhancing spatial coherence, accuracy, and efficiency while offering a scalable and consistent solution for automated IMRT plan generation.
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