Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL 60637, United States of America.
PLoS One. 2019 Jan 11;14(1):e0210385. doi: 10.1371/journal.pone.0210385. eCollection 2019.
Robotic stabilization of a therapeutic radiation beam with respect to a dynamically moving tumor target can be accomplished either by moving the radiation source, the patient, or both. As the treatment beam is on during this process, the primary goal is to minimize exposure of normal tissue to radiation as much as possible when moving the target back to the desired position. Due to the complex mechanical structure of 6 degree-of-freedom (6DoF) robots, it is not intuitive as to what 6 dimensional (6D) correction trajectory is optimal in achieving such a goal. With proportional-integrative-derivative (PID) and other controls, the potential exists that the controller may generate a trajectory that is highly curved, slow, or suboptimal in that it leads to unnecessary exposure of healthy tissue to radiation. This work investigates a novel feedback planning method that takes into account a robot's mechanical joint structure, patient safety tolerances, and other system constraints, and performs real-time optimization to search the entire 6D trajectory space in each time cycle so it can respond with an optimal 6D correction trajectory.
Computer simulations were created for two 6DoF robotic patient support systems: a Stewart-Gough platform for moving a patient's head in frameless maskless stereotactic radiosurgery, and a linear accelerator treatment table for moving a patient in prostate cancer radiation therapy. Motion planning was formulated as an optimization problem and solved at real-time speeds using the L-BFGS algorithm. Three planning methods were investigated, moving the platform as fast as possible (platform-D), moving the target along a straight-line (target-S), and moving the target based on the fastest descent of position error (target-D). Both synthetic motion and prior recorded human motion were used as input data and output results were analyzed.
For randomly generated 6D step-like and sinusoidal synthetic input motion, target-D planning demonstrated the smallest net trajectory error in all cases. On average, optimal planning was found to have a 45% smaller target trajectory error than platform-D control, and a 44% smaller target trajectory error than target-S planning. For patient head motion compensation, only target-D planning was able to maintain a ≤0.5mm and ≤0.5deg clinical tolerance objective for 100% of the treatment time. For prostate motion, both target-S planning and target-D planning outperformed platform-D control.
A general 6D target trajectory optimization framework for robotic patient motion compensation systems was investigated. The method was found to be flexible as it allows control over various performance requirements such as mechanical limits, velocities, acceleration, or other system control objectives.
通过移动辐射源、患者或两者来实现治疗辐射束相对于动态移动的肿瘤靶标的机器人稳定。在此过程中，治疗束处于开启状态，主要目标是在将目标移动回所需位置时尽量减少正常组织对辐射的暴露。由于 6 自由度（6DoF）机器人的复杂机械结构，在实现这一目标时，哪种 6 维（6D）校正轨迹是最佳的，并不直观。使用比例积分微分（PID）和其他控制方法，控制器可能会生成一条高度弯曲、缓慢或次优的轨迹，导致健康组织不必要地暴露于辐射下。这项工作研究了一种新的反馈规划方法，该方法考虑了机器人的机械关节结构、患者安全容限和其他系统约束，并在每个时间周期内执行实时优化，以搜索整个 6D 轨迹空间，从而能够响应最佳的 6D 校正轨迹。
为两个 6DoF 机器人患者支撑系统创建了计算机模拟：用于在无框架无面罩立体定向放射外科中移动患者头部的 Stewart-Gough 平台，以及用于在前列腺癌放射治疗中移动患者的线性加速器治疗台。运动规划被制定为一个优化问题，并使用 L-BFGS 算法实时快速求解。研究了三种规划方法，即尽可能快速地移动平台（platform-D）、沿直线移动目标（target-S）和根据位置误差的最快下降移动目标（target-D）。使用合成运动和先前记录的人体运动作为输入数据，输出结果进行了分析。
对于随机生成的 6D 阶跃式和正弦合成输入运动，target-D 规划在所有情况下都表现出最小的净轨迹误差。平均而言，最优规划的目标轨迹误差比 platform-D 控制小 45%，比 target-S 规划小 44%。对于患者头部运动补偿，只有 target-D 规划能够在 100%的治疗时间内保持≤0.5mm 和≤0.5deg 的临床容限目标。对于前列腺运动，target-S 规划和 target-D 规划都优于 platform-D 控制。
研究了一种用于机器人患者运动补偿系统的通用 6D 目标轨迹优化框架。该方法具有灵活性，因为它允许控制各种性能要求，如机械限制、速度、加速度或其他系统控制目标。
