Department of Medical Physics, Centre Oscar Lambret, Lille, France. University Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL, Lille, France. Author to whom any correspondence should be addressed.
Phys Med Biol. 2020 Apr 2;65(7):075002. doi: 10.1088/1361-6560/ab7633.
The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data. This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis. It takes on average of [Formula: see text] to generate a complete sCT (88 slices) for a patient on our GPU. The average MAE in HU between the sCT and actual patient CT (within the body contour) is 48.5 ± 6 HU with our method. The maximum dose difference to the target is 1.3%. This study demonstrates that an sCT can be generated in a multicentric context, with fewer pre-processing steps while being fast and accurate.
在放射治疗中建立仅依赖于 MRI 的工作流程取决于生成用于剂量计算的准确合成 CT（sCT）的能力。以前提出的方法使用生成对抗网络（GAN）来快速生成 sCT，以简化临床工作流程并降低不确定性。在当前的论文中，我们使用了一种称为 pix2pixHD 的条件生成对抗网络（cGAN）框架来创建一个稳健的模型，易于多中心数据。这项研究包括来自 3 个不同部位的 19 名治疗体位患者的 T2 加权 MR 和 CT 图像。cGAN 在来自 2 个不同部位的 11 名患者的 2D 横切片上进行了训练。训练完成后，该网络用于生成来自第 3 个部位的 8 名患者的 sCT 图像。评估了真实和合成 CT 之间每个患者的平均绝对误差（MAE）。在 sCT 系列上优化放射治疗计划，并在 CT 上重新计算，以评估体素剂量差异和剂量体积直方图（DVH）分析方面的剂量分布。在我们的 GPU 上，为一名患者生成完整的 sCT（88 个切片）平均需要[公式：见文本]。我们的方法在 sCT 和实际患者 CT（在身体轮廓内）之间的 HU 平均 MAE 为 48.5 ± 6 HU。靶区的最大剂量差异为 1.3%。这项研究表明，可以在多中心环境中生成 sCT，预处理步骤更少，速度更快，准确性更高。
