Murphy Martin J, Dieterich Sonja
Department of Radiation Oncology, VCU Health System, Virginia Commonwealth University, Richmond, VA 23298-0058, USA.
Phys Med Biol. 2006 Nov 21;51(22):5903-14. doi: 10.1088/0031-9155/51/22/012. Epub 2006 Oct 26.
Breathing adaptation during external-beam radiotherapy is a matter of great concern because uncompensated tumour motion requires extended treatment margins that endanger sensitive tissue. Compensation strategies include beam gating, collimator tracking and robotic beam re-alignment. All of these schemes have a system latency of up to several hundred milliseconds, which calls in turn for predictive control loops. Irregularities in breathing make prediction difficult. We have evaluated the performance of two classes of control loop algorithms-the linear adaptive filter and the adaptive nonlinear neural network-for highly irregular patient breathing behaviours. The neural network demonstrated robust adaptability to all of the observed breathing patterns while the linear filter failed in a significant percentage of cases. For those cases where the linear filter could function, it made less accurate predictions than the neural network. Because the neural network presents no additional computational burden in the control loop we conclude that it is the preferred choice among heuristic predictive algorithms.
体外放射治疗期间的呼吸适应性是一个备受关注的问题，因为未得到补偿的肿瘤运动会需要扩大治疗边界，这会危及敏感组织。补偿策略包括束流门控、准直器跟踪和机器人束流重新对准。所有这些方案都有高达几百毫秒的系统延迟，这反过来又需要预测控制回路。呼吸的不规则性使得预测变得困难。我们评估了两类控制回路算法——线性自适应滤波器和自适应非线性神经网络——对于高度不规则患者呼吸行为的性能。神经网络对所有观察到的呼吸模式都表现出强大的适应性，而线性滤波器在相当大比例的情况下失效。对于线性滤波器能够起作用的那些情况，它做出的预测不如神经网络准确。由于神经网络在控制回路中不会带来额外的计算负担，我们得出结论，它是启发式预测算法中的首选。
