论文标题
使用条件生成对抗网络（CGAN）的实时头颈IMRT计划生成的人工智能驱动的代理
An Artificial Intelligence-Driven Agent for Real-Time Head-and-Neck IMRT Plan Generation using Conditional Generative Adversarial Network (cGAN)
论文作者
论文摘要
目的：为完全自动化的快速头和脖子（H＆N）IMRT计划生成的人工智能（AI）代理，而无需耗费时间的倒数计划。$$$$$ 方法：该AI代理是使用条件生成对抗网络体系结构训练的。发电机Pyranet是一个新颖的深度学习网络，在类似金字塔的串联中实现了28个经典的重新结构块。鉴别器是一种定制的4层densenet。 AI代理首先从3D CT体积和患者的结构中以9个模板梁角生成自定义的2D投影。然后将这些投影堆叠为Pyranet的4D输入，同时生成9个辐射通量图。最后，预测的通量图被进口到商业治疗计划系统（TPS），以进行计划完整性检查。 AI代理是在TPS计划库中对231个口咽计划进行了测试的。仅研究了顺序提升制度中的主要计划。采用定制的Harr小波损失以进行通量图比较。测试AI计划和TPS计划中的iSodose分布进行了定性评估。统计比较了关键的剂量指标。$$$$ 结果：所有测试AI计划均已成功生成。 AI计划中PTV以外的等速梯度与TPS计划相当。 PTV覆盖范围后，AI计划中的腮腺和口腔的$ d_ {平均值} $在没有统计学意义的情况下是可比性的。 AI计划在0.01cc的脑干和绳索+5mm的情况下实现了可比的$ d_ {max} $，而没有临床相关的差异，但是身体$ d_ {max} $高于TPS计划结果。 AI代理需要每个案例约3s来预测通量图。$$$$ 结论：开发的AI代理可以以令人满意的剂量质量生成H＆N IMRT计划。通过快速和全自动实施，它具有临床应用的巨大潜力。
Purpose: To develop an Artificial Intelligence (AI) agent for fully-automated rapid head and neck (H&N) IMRT plan generation without time-consuming inverse planning.$$$$ Methods: This AI agent was trained using a conditional Generative Adversarial Network architecture. The generator, PyraNet, is a novel Deep Learning network that implements 28 classic ResNet blocks in pyramid-like concatenations. The discriminator is a customized 4-layer DenseNet. The AI agent first generates customized 2D projections at 9 template beam angles from 3D CT volume and structures of a patient. These projections are then stacked as 4D inputs of PyraNet, from which 9 radiation fluence maps are generated simultaneously. Finally, the predicted fluence maps are imported into a commercial treatment planning system (TPS) for plan integrity checks. The AI agent was built and tested upon 231 oropharyngeal plans from a TPS plan library. Only the primary plans in the sequential boost regime were studied. A customized Harr wavelet loss was adopted for fluence map comparison. Isodose distributions in test AI plans and TPS plans were qualitatively evaluated. Key dosimetric metrics were statistically compared.$$$$ Results: All test AI plans were successfully generated. Isodose gradients outside of PTV in AI plans were comparable with TPS plans. After PTV coverage normalization, $D_{mean}$ of parotids and oral cavity in AI plans and TPS plans were comparable without statistical significance. AI plans achieved comparable $D_{max}$ at 0.01cc of brainstem and cord+5mm without clinically relevant differences, but body $D_{max}$ was higher than the TPS plan results. The AI agent needs ~3s per case to predict fluence maps.$$$$ Conclusions: The developed AI agent can generate H&N IMRT plans with satisfying dosimetry quality. With rapid and fully automated implementation, it holds great potential for clinical applications.
