D2T2——面向近距离放射治疗的多模态自动规划
D2T2 - Multimodal Automated Planning for Brachytherapy
把物理前向模型（剂量核线性组合）作为可微网络层嵌入端到端预测，并用可学习的代理指标（gamma index 预测器）构造损失，这一「物理模型嵌入 + 代理指标损失」范式可迁移到其他需要输出物理可行结果的医学影像回归任务。
原文依据 the second implements the physical model of radiation delivery, namely a linear combination of radiation dose kernels
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近距离放射治疗是一个复杂的放射肿瘤学问题，需要同时预测用于治疗计划的辐射剂量，以及一组用于治疗实施的机器参数（即驻留时间）。我们提出 Direct Dwell Time Transformer（D2T2），这是首个在剂量预测过程中直接预测驻留时间的深度学习架构。D2T2 是一个两阶段模型：第一阶段预测一个驻留时间向量，第二阶段实现辐射实施的物理模型，即辐射剂量核的线性组合。除自动预测驻留时间外，当进行端到端训练时，这还带来一个好处，即约束模型做出物理上合理的剂量预测。为增强这一训练，我们还提出一种新的损失函数，称为 gamma 损失，它基于对 gamma 指数的预测，而 gamma 指数是剂量比较的金标准。其实现方式是使用由真实剂量与预测剂量配对构成的合成数据集训练一个模型来预测 gamma 指数。我们在一个约 5,000 例临床近距离放射治疗计划的大型数据集上训练 D2T2——这是迄今最大的此类数据集——涵盖妇科、乳腺及其他治疗部位。结果表明，D2T2 在准确性和速度上均优于现有方法。值得注意的是，D2T2 对任意近距离放射治疗应用都能在单次前向传播中生成可实施的计划和物理上有效的剂量分布，比人工规划快数小时，比更新的自动化方法快数分钟。
英文原文摘要 1554 字符
Brachytherapy is a complex radiation oncology problem that requires the simultaneous prediction of radiation dose, which is used for treatment planning, and a set of machine parameters, known as dwell times, used for treatment delivery. We propose Direct Dwell Time Transformer (D2T2), the first deep learning architecture that directly predicts dwell times during dose prediction. D2T2 is a two stage model, where the first stage predicts a vector of dwell times and the second implements the physical model of radiation delivery, namely a linear combination of radiation dose kernels. Besides the automatic prediction of dwell times, this has the benefit of constraining the model to make physically plausible dose predictions, when trained end-to-end. To enhance this training, we also propose a new loss function, denoted as the gamma loss, based on the prediction of the gamma index, which is the gold standard of dose comparisons. This is implemented by training a model to predict the latter using a synthetic dataset of groundtruth and predicted dose pairs. We train D2T2 on a large dataset of ~5,000 clinical brachytherapy plans---the largest such dataset to date---spanning gynecological, breast, and other treatment sites. Results demonstrate that D2T2 outperforms existing methods in both accuracy and speed. Notably, D2T2 produces deliverable plans and physically valid dose distributions in a single forward pass, for any application of brachytherapy, hours faster than manual planning and minutes faster than more recent automated methods.