Sharp Gregory C, Jiang Steve B, Shimizu Shinichi, Shirato Hiroki
Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
Phys Med Biol. 2004 Feb 7;49(3):425-40. doi: 10.1088/0031-9155/49/3/006.
Image guidance in radiotherapy and extracranial radiosurgery offers the potential for precise radiation dose delivery to a moving tumour. Recent work has demonstrated how to locate and track the position of a tumour in real-time using diagnostic x-ray imaging to find implanted radio-opaque markers. However, the delivery of a treatment plan through gating or beam tracking requires adequate consideration of treatment system latencies, including image acquisition, image processing, communication delays, control system processing, inductance within the motor, mechanical damping, etc. Furthermore, the imaging dose given over long radiosurgery procedures or multiple radiotherapy fractions may not be insignificant, which means that we must reduce the sampling rate of the imaging system. This study evaluates various predictive models for reducing tumour localization errors when a real-time tumour-tracking system targets a moving tumour at a slow imaging rate and with large system latencies. We consider 14 lung tumour cases where the peak-to-peak motion is greater than 8 mm, and compare the localization error using linear prediction, neural network prediction and Kalman filtering, against a system which uses no prediction. To evaluate prediction accuracy for use in beam tracking, we compute the root mean squared error between predicted and actual 3D motion. We found that by using prediction, root mean squared error is improved for all latencies and all imaging rates evaluated. To evaluate prediction accuracy for use in gated treatment, we present a new metric that compares a gating control signal based on predicted motion against the best possible gating control signal. We found that using prediction improves gated treatment accuracy for systems that have latencies of 200 ms or greater, and for systems that have imaging rates of 10 Hz or slower.
放射治疗和颅外放射外科中的图像引导技术为向移动肿瘤精确输送辐射剂量提供了可能。最近的研究表明了如何利用诊断性x光成像来定位和实时跟踪肿瘤位置，以便找到植入的不透射线标记物。然而，通过门控或束流跟踪来实施治疗计划时，需要充分考虑治疗系统的延迟，包括图像采集、图像处理、通信延迟、控制系统处理、电机电感、机械阻尼等。此外，在长时间的放射外科手术或多次放射治疗过程中所给予的成像剂量可能不容忽视，这意味着我们必须降低成像系统的采样率。本研究评估了各种预测模型，以便在实时肿瘤跟踪系统以低成像速率和大系统延迟对移动肿瘤进行靶向时，减少肿瘤定位误差。我们考虑了14例峰峰值运动大于8毫米的肺肿瘤病例，并将使用线性预测、神经网络预测和卡尔曼滤波的定位误差与不使用预测的系统进行比较。为了评估用于束流跟踪的预测准确性，我们计算预测的和实际的3D运动之间的均方根误差。我们发现，通过使用预测，在所评估的所有延迟和所有成像速率下，均方根误差都得到了改善。为了评估用于门控治疗的预测准确性，我们提出了一种新的指标，该指标将基于预测运动的门控控制信号与最佳可能的门控控制信号进行比较。我们发现，对于延迟为200毫秒或更长以及成像速率为10赫兹或更低的系统，使用预测可提高门控治疗的准确性。
