论文标题
基于学习的合成双能CT来自单个能量CT的质子辐射疗法中的功率比计算
Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy
论文作者
论文摘要
目的：通过获得光子相互作用的能量依赖性，双能CT（DECT）的精度比常规单能CT（SECT）更高，以更高的精度得出了停止功率比（SPR）图。但是，在质子辐射疗法模拟中，DECT并不像宗派那样广泛实施。这项工作提出了一种基于学习的方法，以合成从质子辐射疗法的SECT中的DECT图像。方法：所提出的方法使用残留的注意生成对抗网络。带有注意门的残留块被用来迫使模型关注DECT图和教派图像之间的差异。为了评估该方法的准确性，我们回顾性地研究了20名具有DECT和SECT扫描的头颈癌患者。从DECT获得的高能量CT图像是SECT数据集培训过程中的学习目标，并使用剩余的交叉验证策略对拟议方法的结果进行了评估。为了在实际应用中评估我们的方法，我们使用基于物理学的双能化学计量方法从SDECT生成了SPR图，并将这些地图与DECT产生的地图进行了比较。结果：合成的DECT图像显示了整个体积的30 Hounsfield单位（HU）附近的平均平均绝对误差。与原始DECT相比，由合成DECT产生的相应SPR图显示，噪声水平和伪影的平均均值均方根误差约为1％。结论：通过我们的基于机器学习的方法，通过使用合成的DECT生成SPR MAP来显示综合DECT图像的准确性，并在头颈患者上评估了质子治疗计划和剂量计算的潜在可行性。
Purpose: Dual-energy CT (DECT) has been shown to derive stopping power ratio (SPR) map with higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. However, DECT is not as widely implemented as SECT in proton radiation therapy simulation. This work presents a learning-based method to synthetize DECT images from SECT for proton radiation therapy. Methods: The proposed method uses a residual attention generative adversarial network. Residual blocks with attention gates were used to force the model focus on the difference between DECT maps and SECT images. To evaluate the accuracy of the method, we retrospectively investigated 20 head-and-neck cancer patients with both DECT and SECT scans available. The high and low energy CT images acquired from DECT acted as learning targets in the training process for SECT datasets and were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. To evaluate our method in the context of a practical application, we generated SPR maps from sDECT using physics-based dual-energy stoichiometric method and compared the maps to those generated from DECT. Results: The synthesized DECT images showed an average mean absolute error around 30 Hounsfield Unit (HU) across the whole-body volume. The corresponding SPR maps generated from synthetic DECT showed an average normalized mean square error of about 1% with reduced noise level and artifacts than those from original DECT. Conclusions: The accuracy of the synthesized DECT image by our machine-learning-based method was evaluated on head and neck patient, and potential feasibility for proton treatment planning and dose calculation was shown by generating SPR map using the synthesized DECT.
