Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy
Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy
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DOI：
10.1002/mp.12351
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发表时间：
2017-08-01
期刊：
影响因子：
3.8
通讯作者：
Hiraoka, Masahiro
中科院分区：
文献类型：
--
作者：
Mukumoto, Nobutaka;Nakamura, Mitsuhiro;Hiraoka, Masahiro
目的：探讨训练数据采集的采样间隔对替代信号为基础的动态肿瘤跟踪的intrafractional预测误差的影响使用万向节安装linac.Materials和方法：20对呼吸运动从20例患者（10肺，5肝，胰腺癌患者）谁进行了动态肿瘤跟踪与Vero 4DRT。首先，在照射前采集呼吸运动作为用于初始构建预测模型的训练数据。接下来，由于照射期间呼吸模式的变化，采集额外的呼吸运动用于更新预测模型。在第二次采集呼吸运动之前经过的时间为12.6 +/- 3.1 min。四轴移动体模再现了患者的三维（3D）目标运动和一维替代运动。为了从外部替代运动预测未来的内部目标运动，通过最小化使用正交kV X射线成像系统以80和320 ms采样间隔采集20 s以及以500、1，000和2，000 ms采样间隔采集60 s的训练数据的残差预测误差来构建预测模型。在训练过程中，基于每个采样间隔的训练数据来估计以不同采样间隔训练的预测模型的精度。然后在以500 ms的恒定采样间隔拍摄30 s的分次监测图像上计算各种预测模型的分次预测误差，以公平地评估相同运动模式的预测准确性。此外，第一呼吸运动用于训练和第二呼吸运动被用于改变呼吸运动的分数内预测误差的评估，以评估预测models.Results的鲁棒性：预测模型的训练误差为1.7 +/- 0.7 mm，在3D的所有采样间隔。对于80 ms采样间隔，相同运动模式的分次内预测误差在3D中为1.9 +/- 0.7 mm，在以2，000 ms采样间隔，差异显著其他采样间隔的平均检出率均在2.5%以下，差异无统计学意义（P > 0.05）。对于80 ms采样间隔，3D中改变的呼吸运动模式的分次内预测误差增加至5.1 +/- 2.4 mm;然而，80 ms采样间隔与其他采样间隔之间的预测模型稳健性无显著差异（P > 0.05）。虽然预测模型的训练误差对于所有采样间隔是一致的，但是使用2，000 ms增加了相同运动模式的分数内预测误差。在训练过程中，使用较大的采样间隔很难估计预测模型的实际精度。建议在采样间隔>= 1,000 ms时构建预测模型，并在治疗过程中重建模型。
Purpose: To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac.Materials and methods: Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 +/- 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models.Results: The training error of the prediction model was 1.7 +/- 0.7 mm in 3D for all sampling intervals. The intrafractional prediction error for the same motion pattern was 1.9 +/- 0.7 mm in 3D for an 80 ms sampling interval, which increased larger than 1 mm in 10.0% of prediction models trained at a 2,000 ms sampling interval with a significant difference (P < 0.01) and up to 2.5% for the other sampling intervals without a significant difference (P > 0.05). The intrafractional prediction error for the changed respiratory motion pattern increased to 5.1 +/- 2.4 mm in 3D for an 80 ms sampling interval; however, there was not a significant difference in the robustness of the prediction model between the 80 ms sampling interval and other sampling intervals (P > 0.05).Conclusions: Although the training error of the prediction model was consistent for the all sampling intervals, the prediction model using the larger sampling interval of the 2,000 ms increased the intrafractional prediction error for the same motion pattern. The realistic accuracy of the prediction model was difficult to estimate using the larger sampling interval during the training process. It is recommended to construct the prediction model at sampling interval >= 1,000 ms and to reconstruct the model during treatment.