Radiation Physics Laboratory, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.
Med Phys. 2013 Apr;40(4):041705. doi: 10.1118/1.4794497.
The accuracy of motion prediction, utilized to overcome the system latency of motion management radiotherapy systems, is hampered by irregularities present in the patients' respiratory pattern. Audiovisual (AV) biofeedback has been shown to reduce respiratory irregularities. The aim of this study was to test the hypothesis that AV biofeedback improves the accuracy of motion prediction.
An AV biofeedback system combined with real-time respiratory data acquisition and MR images were implemented in this project. One-dimensional respiratory data from (1) the abdominal wall (30 Hz) and (2) the thoracic diaphragm (5 Hz) were obtained from 15 healthy human subjects across 30 studies. The subjects were required to breathe with and without the guidance of AV biofeedback during each study. The obtained respiratory signals were then implemented in a kernel density estimation prediction algorithm. For each of the 30 studies, five different prediction times ranging from 50 to 1400 ms were tested (150 predictions performed). Prediction error was quantified as the root mean square error (RMSE); the RMSE was calculated from the difference between the real and predicted respiratory data. The statistical significance of the prediction results was determined by the Student's t-test.
Prediction accuracy was considerably improved by the implementation of AV biofeedback. Of the 150 respiratory predictions performed, prediction accuracy was improved 69% (103/150) of the time for abdominal wall data, and 78% (117/150) of the time for diaphragm data. The average reduction in RMSE due to AV biofeedback over unguided respiration was 26% (p < 0.001) and 29% (p < 0.001) for abdominal wall and diaphragm respiratory motion, respectively.
This study was the first to demonstrate that the reduction of respiratory irregularities due to the implementation of AV biofeedback improves prediction accuracy. This would result in increased efficiency of motion management techniques affected by system latencies used in radiotherapy.
运动预测的准确性受到患者呼吸模式不规则性的限制，运动管理放射治疗系统利用运动预测来克服这一限制。视听（AV）生物反馈已被证明可以减少呼吸不规则性。本研究旨在检验以下假设，即 AV 生物反馈可提高运动预测的准确性。
本项目中实施了一种视听生物反馈系统，该系统结合了实时呼吸数据采集和磁共振成像。从 15 名健康人体受试者的 30 项研究中获得了（1）腹壁（30 Hz）和（2）膈肌（5 Hz）的一维呼吸数据。要求受试者在每项研究中都在有和没有视听生物反馈指导的情况下进行呼吸。然后将获得的呼吸信号应用于核密度估计预测算法中。对于 30 项研究中的每一项，测试了从 50 到 1400 ms 的五个不同预测时间（进行了 150 次预测）。通过将真实和预测的呼吸数据之间的差异来量化预测误差，即均方根误差（RMSE）。通过学生 t 检验确定预测结果的统计学意义。
通过实施视听生物反馈，预测准确性得到了显著提高。在进行的 150 次呼吸预测中，腹壁数据的预测准确率提高了 69%（103/150），膈肌数据的预测准确率提高了 78%（117/150）。由于实施视听生物反馈，与无指导呼吸相比，腹壁和膈肌呼吸运动的 RMSE 平均分别降低了 26%（p < 0.001）和 29%（p < 0.001）。
本研究首次证明，由于实施视听生物反馈而减少的呼吸不规则性可提高预测准确性。这将提高受放射治疗中系统延迟影响的运动管理技术的效率。
