Kim Hojin, Chen Josephine, Phillips Justin, Pukala Jason, Yom Sue S, Kirby Neil
Department of Radiation Oncology, University of California, San Francisco, CA, USA.
Department of Radiation Oncology, Asan Medical Center, University of Uslan College of Medicine, Seoul, Korea.
Technol Cancer Res Treat. 2017 Dec;16(6):885-892. doi: 10.1177/1533034617708076. Epub 2017 May 11.
Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm's performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool-produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated deformable image registration evaluation of confidence tool was also found to produce accurate confidence intervals for the dose-volume histograms of the deformed dose.
可变形图像配准是一种强大的工具，用于将诸如放射治疗剂量计算等信息从一幅计算机断层扫描图像映射到另一幅图像。然而，可变形图像配准容易出现映射错误。最近，一种用于自动评估可变形图像配准置信度的工具被提出来，以便逐患者预测体素特异性可变形图像配准剂量映射误差。这项工作的目的是对自动评估可变形图像配准置信度的工具进行广泛分析，以展示其在估计剂量映射误差方面的有效性。所提出的自动评估可变形图像配准置信度工具的形式利用4种模拟患者变形（3种基于B样条的变形和1种刚性变换）来预测可变形图像配准算法性能的不确定性。该工作流程在2种DIR算法（来自Velocity和Plastimatch的B样条多步算法）上得到验证，使用了1个物理体模和11个虚拟体模（它们具有已知的真实变形），以及3对真实患者肺部图像（它们有数百个已识别的地标点）。对于验证测试，真实剂量映射误差分布紧密遵循自动评估可变形图像配准置信度工具预测的学生分布：平均而言，自动评估可变形图像配准置信度工具产生的50%、68%和95%的置信水平分别包含了来自Velocity和Plastimatch的实际误差的48.8%、66.3%和93.8%，以及50.1%、67.6%和93.8%。尽管地标点稀疏，但来自3个肺部患者数据集的观察到的误差分布也遵循预期的误差分布。自动评估可变形图像配准置信度工具的剂量误差分布也与真实剂量误差分布表现出良好的相似性。还发现自动评估可变形图像配准置信度工具能为变形剂量的剂量体积直方图产生准确的置信区间。
