Using an external surrogate for predictor model training in real-time motion management of lung tumors.
Using an external surrogate for predictor model training in real-time motion management of lung tumors.
复制标题
使用外部替代物进行肺部肿瘤实时运动管理中的预测模型训练。
DOI：
10.1118/1.4901252
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发表时间：
2014
期刊：
影响因子：
3.8
通讯作者：
Berbeco,Ross
中科院分区：
文献类型：
--
作者：
Rottmann,Joerg;Berbeco,Ross
目的精确预测呼吸运动是真实的-时间运动补偿技术的先决条件，例如射束、动态治疗床或动态多叶准直器跟踪。大多数算法都需要收集肿瘤运动数据来训练预测模型。为了避免患者在此过程中暴露于成像的额外剂量，研究了用外部替代信号训练线性呼吸运动预测模型的可行性，并将其性能与直接用肿瘤位置训练模型进行基准测试。它的性能进行了研究的数据集91例患者的呼吸轨迹记录从基准标记跟踪在放射治疗输送到肺部的10名患者。将预期的3D几何误差量化为预测器前瞻时间、信号采样频率和历史向量长度的函数。此外，评估自适应模型再训练，即，初始训练后重复更新预测模型。这方面的培训长度随着输入（内部）数据的可用性而逐渐增加。为了评估实际的可行性，模型计算时间以及各种最小数据长度的再培训进行评估。评估了使用外部替代运动数据与肿瘤运动数据的模型训练的相对性能。然而，没有利用内部-外部运动相关性模型，即，预测是完全由内部运动在这两种cases. ResultsSimilarPrediction驱动实现了训练模型与外部替代数据与内部（肿瘤运动）数据。自适应模型再训练可以在外部代理训练的情况下大大提高性能，而对于使用内部运动数据进行训练的影响很小。8 s的最小自适应再训练数据长度和3 s的历史向量长度实现最大性能。采样频率似乎对性能影响不大，证实了以前发表的工作。通过使用线性预测器，实现了约50%的相对几何3D误差减少（使用自适应再训练，历史向量长度为3s，并且结果在所有研究的前瞻时间和信号采样频率上平均）。绝对平均误差可以从（2.0 ± 1.6）mm，当完全不使用预测时，（0.9 ± 0.8）mm，（1.0 ± 0.9）mm，当使用用内部肿瘤运动训练数据和外部替代运动训练数据训练的预测器时，分别结论线性预测模型可以将延迟引起的跟踪误差平均降低约50%在系统延迟高达300 ms的真实的实时图像引导放射治疗系统中。仅使用外部替代信号训练用于肺肿瘤运动预测的线性模型是可行的，并且导致与使用（内部）肿瘤运动训练类似的性能。特别是对于从具有电离辐射的荧光透视成像中提取运动数据的场景，这可以减轻在收集模型训练数据期间对附加成像剂量的需要。
PurposePrecise prediction of respiratory motion is a prerequisite for real‐time motion compensation techniques such as beam, dynamic couch, or dynamic multileaf collimator tracking. Collection of tumor motion data to train the prediction model is required for most algorithms. To avoid exposure of patients to additional dose from imaging during this procedure, the feasibility of training a linear respiratory motion prediction model with an external surrogate signal is investigated and its performance benchmarked against training the model with tumor positions directly.MethodsThe authors implement a lung tumor motion prediction algorithm based on linear ridge regression that is suitable to overcome system latencies up to about 300 ms. Its performance is investigated on a data set of 91 patient breathing trajectories recorded from fiducial marker tracking during radiotherapy delivery to the lung of ten patients. The expected 3D geometric error is quantified as a function of predictor lookahead time, signal sampling frequency and history vector length. Additionally, adaptive model retraining is evaluated, i.e., repeatedly updating the prediction model after initial training. Training length for this is gradually increased with incoming (internal) data availability. To assess practical feasibility model calculation times as well as various minimum data lengths for retraining are evaluated. Relative performance of model training with external surrogate motion data versus tumor motion data is evaluated. However, an internal–external motion correlation model is not utilized, i.e., prediction is solely driven by internal motion in both cases.ResultsSimilar prediction performance was achieved for training the model with external surrogate data versus internal (tumor motion) data. Adaptive model retraining can substantially boost performance in the case of external surrogate training while it has little impact for training with internal motion data. A minimum adaptive retraining data length of 8 s and history vector length of 3 s achieve maximal performance. Sampling frequency appears to have little impact on performance confirming previously published work. By using the linear predictor, a relative geometric 3D error reduction of about 50% was achieved (using adaptive retraining, a history vector length of 3 s and with results averaged over all investigated lookahead times and signal sampling frequencies). The absolute mean error could be reduced from (2.0 ± 1.6) mm when using no prediction at all to (0.9 ± 0.8) mm and (1.0 ± 0.9) mm when using the predictor trained with internal tumor motion training data and external surrogate motion training data, respectively (for a typical lookahead time of 250 ms and sampling frequency of 15 Hz).ConclusionsA linear prediction model can reduce latency induced tracking errors by an average of about 50% in real‐time image guided radiotherapy systems with system latencies of up to 300 ms. Training a linear model for lung tumor motion prediction with an external surrogate signal alone is feasible and results in similar performance as training with (internal) tumor motion. Particularly for scenarios where motion data are extracted from fluoroscopic imaging with ionizing radiation, this may alleviate the need for additional imaging dose during the collection of model training data.
Elekta 直线加速器上的目标跟踪交付：可行性研究
影响因子：
3.5
作者：
D. Mcquaid;Mike Partridge;J. Symonds;Philip M. Evans;Steve Webb
通讯作者：
Steve Webb
DOI：
10.1016/j.ijrobp.2010.03.043
发表时间：
2011-02-01
影响因子：
7
作者：
Krauss, Andreas;Nill, Simeon;Oelfke, Uwe
通讯作者：
Oelfke, Uwe
影响因子：
3.5
作者：
Ruan, Dan
通讯作者：
Ruan, Dan