Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, United States of America.
Phys Med Biol. 2019 Oct 21;64(20):205022. doi: 10.1088/1361-6560/ab41af.
The purpose of this work is to validate the application of a deep learning-based method for pelvic synthetic CT (sCT) generation that can be used for prostate proton beam therapy treatment planning. We propose to integrate dense block minimization into 3D cycle-consistent generative adversarial networks (cycleGAN) framework to effectively learn the nonlinear mapping between MRI and CT pairs. A cohort of 17 patients with co-registered CT and MR pairs were used to test the deep learning-based sCT generation method by leave-one-out cross-validation. Image quality between the sCT and CT images, gamma analysis passing rate, dose-volume metrics, distal range displacement, and the individual pencil beam Bragg peak shift between sCT- and CT-based proton plans were evaluated. The average mean absolute error (MAE) was 51.32 ± 16.91 HU. The relative differences of the statistics of the PTV dose-volume histogram (DVH) metrics in between sCT and CT were generally less than 1%. Mean values of dose difference, absolute dose difference (in percent of the prescribed dose) were -0.07% ± 0.07% and 0.23% ± 0.08%. Mean gamma analysis pass rate of 1 mm/1%, 2 mm/2%, 3 mm/3% criteria with 10% dose threshold were 92.39% ± 5.97%, 97.95% ± 2.95% and 98.97% ± 1.62% respectively. The median, mean and standard deviation of absolute maximum range differences were 0.09 cm and 0.23 ± 0.25 cm. The median and mean Bragg peak shifts among the 17 patients were 0.09 cm and 0.18 ± 0.07 cm. The image similarity, dosimetric and distal range agreement between sCT and original CT suggests the feasibility of further development of an MRI-only workflow for prostate proton radiotherapy.
这项工作的目的是验证一种基于深度学习的骨盆合成 CT（sCT）生成方法的应用，该方法可用于前列腺质子束治疗计划。我们建议将密集块最小化集成到 3D 循环一致生成对抗网络（cycleGAN）框架中，以有效地学习 MRI 和 CT 对之间的非线性映射。使用 17 名具有配准 CT 和 MR 对的患者的队列，通过留一法交叉验证来测试基于深度学习的 sCT 生成方法。通过 gamma 分析通过率、剂量体积指标、远端范围位移和基于 sCT 和 CT 的质子计划之间的个体铅笔束布拉格峰位移来评估 sCT 图像与 CT 图像之间的图像质量。平均均方误差（MAE）为 51.32 ± 16.91 HU。sCT 和 CT 之间 PTV 剂量体积直方图（DVH）指标统计的相对差异通常小于 1%。sCT 和 CT 之间剂量差异和绝对剂量差异（规定剂量的百分比）的平均值分别为-0.07% ± 0.07%和 0.23% ± 0.08%。1mm/1%、2mm/2%和 3mm/3%标准的 10%剂量阈值的 gamma 分析通过率分别为 92.39% ± 5.97%、97.95% ± 2.95%和 98.97% ± 1.62%。绝对最大范围差异的中位数、均值和标准差分别为 0.09cm 和 0.23 ± 0.25cm。17 名患者中，绝对最大峰位移的中位数和均值分别为 0.09cm 和 0.18 ± 0.07cm。sCT 和原始 CT 之间的图像相似性、剂量学和远端范围一致性表明，进一步开发仅基于 MRI 的前列腺质子放射治疗工作流程是可行的。
