Lausch Anthony, Chen Jeff, Ward Aaron D, Gaede Stewart, Lee Ting-Yim, Wong Eugene
Department of Medical Biophysics, Western University, Schulich School of Medicine and Dentistry, London, Ontario, N6A3K7, Canada.
Phys Med Biol. 2014 Nov 21;59(22):7039-58. doi: 10.1088/0031-9155/59/22/7039. Epub 2014 Oct 31.
Parametric response map (PRM) analysis is a voxel-wise technique for predicting overall treatment outcome, which shows promise as a tool for guiding personalized locally adaptive radiotherapy (RT). However, image registration error (IRE) introduces uncertainty into this analysis which may limit its use for guiding RT. Here we extend the PRM method to include an IRE-related PRM analysis confidence interval and also incorporate multiple graded classification thresholds to facilitate visualization. A Gaussian IRE model was used to compute an expected value and confidence interval for PRM analysis. The augmented PRM (A-PRM) was evaluated using CT-perfusion functional image data from patients treated with RT for glioma and hepatocellular carcinoma. Known rigid IREs were simulated by applying one thousand different rigid transformations to each image set. PRM and A-PRM analyses of the transformed images were then compared to analyses of the original images (ground truth) in order to investigate the two methods in the presence of controlled IRE. The A-PRM was shown to help visualize and quantify IRE-related analysis uncertainty. The use of multiple graded classification thresholds also provided additional contextual information which could be useful for visually identifying adaptive RT targets (e.g. sub-volume boosts). The A-PRM should facilitate reliable PRM guided adaptive RT by allowing the user to identify if a patient's unique IRE-related PRM analysis uncertainty has the potential to influence target delineation.
参数反应映射（PRM）分析是一种用于预测总体治疗结果的体素级技术，作为指导个性化局部自适应放射治疗（RT）的工具显示出前景。然而，图像配准误差（IRE）给该分析带来了不确定性，这可能会限制其在指导放射治疗中的应用。在此，我们扩展了PRM方法，纳入与IRE相关的PRM分析置信区间，并引入多个分级分类阈值以促进可视化。使用高斯IRE模型来计算PRM分析的期望值和置信区间。使用来自接受胶质瘤和肝细胞癌放射治疗患者的CT灌注功能图像数据对增强型PRM（A-PRM）进行评估。通过对每个图像集应用一千种不同的刚性变换来模拟已知的刚性IRE。然后将变换后图像的PRM和A-PRM分析与原始图像（真实情况）的分析进行比较，以便在存在可控IRE的情况下研究这两种方法。结果表明，A-PRM有助于可视化和量化与IRE相关的分析不确定性。使用多个分级分类阈值还提供了额外的背景信息，这对于直观识别自适应放射治疗靶区（例如子体积增强）可能有用。A-PRM应通过允许用户识别患者独特的与IRE相关的PRM分析不确定性是否有可能影响靶区勾画，来促进可靠的PRM引导的自适应放射治疗。
