背景:具有多叶准直器(MLC)跟踪的磁共振成像(MRI)引导放射治疗是一种用于分数内运动管理的有前途的技术,在不延长治疗时间的情况下实现高剂量适形性。为了改善光束-目标对准,应使用时间预测来减少由于系统延迟而导致的几何误差。
目的:通过实验比较线性回归(LR)和长短期记忆(LSTM)运动预测模型,在MRI直线加速器上使用多个患者衍生的具有不同复杂性的迹线进行MLC跟踪。
方法:在能够进行MLC跟踪的1.0TMRI直线加速器原型上进行实验。根据八个肺癌患者的呼吸运动轨迹,对运动模型进行编程,以在上下(SI)方向上移动目标。使用模板匹配算法从4Hz下采集的矢状2D电影MRI定位目标质心位置。质心位置被输入到四个运动预测模型之一。我们使用(1)LSTM网络,该网络在先前的研究中对来自另一个队列(离线LSTM)的患者数据进行了优化。我们还使用(2)相同的LSTM模型作为起点,在基于最近的运动(离线+在线LSTM)的实验过程中连续重新优化其权重。此外,我们实现了(3)一个不断更新的LR模型,这完全是基于最近的运动(在线LR)。最后,我们使用(4)没有任何变化的最后一个可用目标质心作为基线(无预测因子)。模型的预测用于实时移动MLC孔径。在实验过程中,使用电子射野成像设备(EPID)对目标和MLC孔径进行可视化。基于EPID帧,目标和MLC孔径位置之间的均方根误差(RMSE)用于评估不同运动预测因子的性能。运动跟踪和预测模型的每个组合重复两次以测试稳定性,共64个实验。
结果:系统的端到端延迟测量为(389±15)ms,并通过LR和LSTM模型成功缓解。对于所有调查的运动轨迹,离线+在线LSTM的表现优于其他模型。在所有迹线上获得的中值RMSE为(2.8±1.3)mm,与离线LSTM的(3.2±1.9)mm相比,在线LR的(3.3±1.4)mm和使用无预测因子时的(4.4±2.4)mm。根据统计检验,在成对比较中,所有模型之间的差异都是显著的(p值<0.05),但对于离线LSTM和在线LR对。发现离线+在线LSTM比离线LSTM和在线LR更可再现,在两次测量之间的RMSE的最大偏差为10%。
结论:这项研究代表了使用几种患者衍生的呼吸运动轨迹对MRI引导的MLC跟踪的不同预测模型的首次实验比较。我们已经证明,在所研究的模型中,持续重新优化的LSTM网络最有希望解决MRI引导放疗与MLC跟踪的端到端系统延迟问题.
BACKGROUND: Magnetic resonance imaging (MRI)-guided radiotherapy with multileaf collimator (MLC)-tracking is a promising technique for intra-fractional motion management, achieving high dose conformality without prolonging treatment times. To improve beam-target alignment, the geometric error due to system latency should be reduced by using temporal prediction.
OBJECTIVE: To experimentally compare linear regression (LR) and long-short-term memory (LSTM) motion prediction models for MLC-tracking on an MRI-linac using multiple patient-derived traces with different complexities.
METHODS: Experiments were performed on a prototype 1.0 T MRI-linac capable of MLC-tracking. A motion phantom was programmed to move a target in superior-inferior (SI) direction according to eight lung cancer patient respiratory motion traces. Target centroid positions were localized from sagittal 2D cine MRIs acquired at 4 Hz using a template matching algorithm. The centroid positions were input to one of four motion prediction models. We used (1) a LSTM network which had been optimized in a previous study on patient data from another cohort (offline LSTM). We also used (2) the same LSTM model as a starting point for continuous re-optimization of its weights during the experiment based on recent motion (offline+online LSTM). Furthermore, we implemented (3) a continuously updated LR model, which was solely based on recent motion (online LR). Finally, we used (4) the last available target centroid without any changes as a baseline (no-predictor). The predictions of the models were used to shift the MLC aperture in real-time. An electronic portal imaging device (EPID) was used to visualize the target and MLC aperture during the experiments. Based on the EPID frames, the root-mean-square error (RMSE) between the target and the MLC aperture positions was used to assess the performance of the different motion predictors. Each combination of motion trace and prediction model was repeated twice to test stability, for a total of 64 experiments.
RESULTS: The end-to-end latency of the system was measured to be (389 ± 15) ms and was successfully mitigated by both LR and LSTM models. The offline+online LSTM was found to outperform the other models for all investigated motion traces. It obtained a median RMSE over all traces of (2.8 ± 1.3) mm, compared to the (3.2 ± 1.9) mm of the offline LSTM, the (3.3 ± 1.4) mm of the online LR and the (4.4 ± 2.4) mm when using the no-predictor. According to statistical tests, differences were significant (p-value <0.05) among all models in a pair-wise comparison, but for the offline LSTM and online LR pair. The offline+online LSTM was found to be more reproducible than the offline LSTM and the online LR with a maximum deviation in RMSE between two measurements of 10%.
CONCLUSIONS: This study represents the first experimental comparison of different prediction models for MRI-guided MLC-tracking using several patient-derived respiratory motion traces. We have shown that among the investigated models, continuously re-optimized LSTM networks are the most promising to account for the end-to-end system latency in MRI-guided radiotherapy with MLC-tracking.