AI 中文总结
研究针对近距离放射治疗中导管数字化难题，开发数据高效、物理引导框架，经两阶段实现多导管数字化。先预训练植入区域感知网络再微调，结合结构感知重建模块。实验验证其有效性，能为临床工作流程提供高效数字化方案。
AI 中文摘要
在CT引导的间质近距离放射治疗中，准确的导管数字化是一项关键但耗时的任务，尤其是对于复杂的植入配置。我们开发了一个数据高效、物理引导的框架，用于在最少临床注释的情况下自动进行多导管数字化。该流程包括两个阶段。首先，一个植入区域感知网络在具有模拟金属特征的合成CT体积上进行预训练，然后仅使用10个临床病例进行微调。其次，一个结构感知重建模块将方向约束的3D霍夫变换与同步物理约束的向内跟踪相结合，以分离粘连的导管轨迹。通过对38例患者的203个治疗部分进行患者级五折交叉验证来评估该方法。微调后的网络实现了0.853±0.362mm的HD95。端到端评估产生了0.891±0.178的F1分数，轴误差和尖端误差分别为0.334±0.367mm和0.896±0.680mm。在导管严重粘连的情况下，跟踪F1分数仍为0.843±0.190。完整的工作流程每个病例大约需要11.6秒。这些结果表明，将少样本合成到真实学习与物理引导的结构跟踪相结合，可以为对时间敏感的临床工作流程提供强大而有效的多导管数字化。
英文摘要
Accurate catheter digitization in CT-guided interstitial brachytherapy is a critical but time-consuming task, especially for complex implant configurations. We developed a data-efficient, physics-guided framework for automated multi-catheter digitization with minimal clinical annotation. The pipeline consists of two stages. First, an implant region-aware network was pretrained on synthetic CT volumes with simulated metallic signatures and then fine-tuned using only 10 clinical cases. Second, a structure-aware reconstruction module combined a direction-constrained 3D Hough transform with synchronous physics-constrained inward tracking to separate adherent catheter trajectories. The method was evaluated by patient-level five-fold cross-validation on 203 treatment fractions from 38 patients. The fine-tuned network achieved an HD95 of 0.853 +/- 0.362 mm. End-to-end evaluation yielded an F1 score of 0.891 +/- 0.178, with shaft and tip errors of 0.334 +/- 0.367 mm and 0.896 +/- 0.680 mm, respectively. In cases with severe catheter adhesion, the tracking F1 score remained 0.843 +/- 0.190. The complete workflow required approximately 11.6 s per case. These results indicate that combining few-shot synthetic-to-real learning with physics-guided structural tracking can provide robust and efficient multi-catheter digitization for time-sensitive clinical workflows.
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