Pollock S, Lee D, Keall P, Kim T
University of Sydney, Sydney, NSW.
Med Phys. 2012 Jun;39(6Part28):3972. doi: 10.1118/1.4736208.
Prediction of respiratory-related tumor motion is hampered by irregularities present in the patient breathing patterns. Audiovisual (AV) biofeedback reduces irregularities, thereby producing a less complex breathing pattern. The aim of this project is to improve respiratory motion prediction accuracy using an AV biofeedback system.
An AV biofeedback system combined with real-time MRI was implemented in this project (4 human subjects across 5 studies (one subject had both an initial and follow-up study)). The AV biofeedback system consists of external marker positioned on the abdomen of human subjects, being tracked using an RPM system (Real-time Position Management, Varian) to guide the subject's breathing. Acquired respiratory data has been used as input for motion prediction through a dynamic multi-leaf collimator (DMLC) simulator developed by Prof. Keall. The prediction algorithm utilized was a kernel density estimation-based real-time prediction algorithm. A variety of prediction parameters were tested to determine optimum prediction performance. Prediction parameters adjusted were the delay time (DT) and training examples (TE); the parameters tested here were: DT/TE = 2500/1500, 2500/100, 1000/250, 500/250; Given that the data sampling rate was kept at 30 Hz, the resultant prediction training window lengths were 49.5, 8.25, 3.3 and 3.3seconds respectively.
The mean difference between measured and predicted data for free breathing was 1.98±2.32mm; and 0.65±0.65mm for when AV biofeedback was implemented (reduction of error of 67%). The most accurate prediction results were attained using the parameters: DT/TE = 500 ms/250.
This study demonstrates the improvement of respiratory motion prediction accuracy when AV biofeedback is implemented to produce a more regular breathing pattern.
患者呼吸模式中存在的不规则性阻碍了与呼吸相关的肿瘤运动预测。视听（AV）生物反馈可减少不规则性，从而产生较简单的呼吸模式。本项目的目的是使用AV生物反馈系统提高呼吸运动预测的准确性。
本项目实施了一个结合实时MRI的AV生物反馈系统（5项研究中的4名人类受试者（1名受试者进行了初始研究和后续研究））。AV生物反馈系统由放置在人类受试者腹部的外部标记组成，使用RPM系统（实时位置管理，瓦里安）进行跟踪，以指导受试者呼吸。采集到的呼吸数据已通过Keall教授开发的动态多叶准直器（DMLC）模拟器用作运动预测的输入。所使用的预测算法是基于核密度估计的实时预测算法。测试了各种预测参数以确定最佳预测性能。调整的预测参数是延迟时间（DT）和训练示例（TE）；此处测试的参数为：DT/TE = 2500/1500、2500/100、1000/250、500/250；鉴于数据采样率保持在30Hz，所得预测训练窗口长度分别为49.5、8.25、3.3和3.3秒。
自由呼吸时测量数据与预测数据之间的平均差异为1.98±2.32mm；实施AV生物反馈时为0.65±0.65mm（误差降低67%）。使用参数DT/TE = 500ms/250获得了最准确的预测结果。
本研究表明，实施AV生物反馈以产生更规则的呼吸模式时，呼吸运动预测准确性得到了提高。
