Li Ruijiang, Lewis John H, Cerviño Laura I, Jiang Steve B
Department of Radiation Oncology, University of California San Diego, 3855 Health Sciences Drive, La Jolla, CA 92037-0843, USA.
Phys Med Biol. 2009 Aug 7;54(15):4821-33. doi: 10.1088/0031-9155/54/15/012. Epub 2009 Jul 22.
Respiratory motion during free-breathing computed tomography (CT) scan may cause significant errors in target definition for tumors in the thorax and upper abdomen. A four-dimensional (4D) CT technique has been widely used for treatment simulation of thoracic and abdominal cancer radiotherapy. The current 4D CT techniques require retrospective sorting of the reconstructed CT slices oversampled at the same couch position. Most sorting methods depend on external surrogates of respiratory motion recorded by extra instruments. However, respiratory signals obtained from these external surrogates may not always accurately represent the internal target motion, especially when irregular breathing patterns occur. We have proposed a new sorting method based on multiple internal anatomical features for multi-slice CT scan acquired in the cine mode. Four features are analyzed in this study, including the air content, lung area, lung density and body area. We use a measure called spatial coherence to select the optimal internal feature at each couch position and to generate the respiratory signals for 4D CT sorting. The proposed method has been evaluated for ten cancer patients (eight with thoracic cancer and two with abdominal cancer). For nine patients, the respiratory signals generated from the combined internal features are well correlated to those from external surrogates recorded by the real-time position management (RPM) system (average correlation: 0.95+/-0.02), which is better than any individual internal measures at 95% confidence level. For these nine patients, the 4D CT images sorted by the combined internal features are almost identical to those sorted by the RPM signal. For one patient with an irregular breathing pattern, the respiratory signals given by the combined internal features do not correlate well with those from RPM (correlation: 0.68+/-0.42). In this case, the 4D CT image sorted by our method presents fewer artifacts than that from the RPM signal. Our 4D CT internal sorting method eliminates the need of externally recorded surrogates of respiratory motion. It is an automatic, accurate, robust, cost efficient and yet simple method and therefore can be readily implemented in clinical settings.
自由呼吸计算机断层扫描（CT）期间的呼吸运动可能会在胸部和上腹部肿瘤的靶区定义中导致显著误差。四维（4D）CT技术已广泛用于胸部和腹部癌症放射治疗的治疗模拟。当前的4D CT技术需要对在相同治疗床位置过采样重建的CT切片进行回顾性排序。大多数排序方法依赖于额外仪器记录的呼吸运动外部替代物。然而，从这些外部替代物获得的呼吸信号可能并不总是准确地代表内部靶区运动，尤其是当出现不规则呼吸模式时。我们提出了一种基于多个内部解剖特征的新排序方法，用于在电影模式下采集的多层CT扫描。本研究分析了四个特征，包括空气含量、肺面积、肺密度和身体面积。我们使用一种称为空间相干性的度量来在每个治疗床位置选择最佳内部特征，并生成用于4D CT排序的呼吸信号。该方法已在10例癌症患者（8例胸癌患者和2例腹癌患者）中进行了评估。对于9例患者，由组合内部特征生成的呼吸信号与实时位置管理（RPM）系统记录的外部替代物的呼吸信号高度相关（平均相关性：0.95±0.02），在95%置信水平下优于任何单个内部测量值。对于这9例患者，由组合内部特征排序的4D CT图像与由RPM信号排序的图像几乎相同。对于1例呼吸模式不规则的患者，组合内部特征给出的呼吸信号与RPM的呼吸信号相关性不佳（相关性：0.68±0.42）。在这种情况下，我们的方法排序的4D CT图像比RPM信号排序的图像伪影更少。我们的4D CT内部排序方法无需外部记录的呼吸运动替代物。它是一种自动、准确、稳健、经济高效且简单的方法，因此可在临床环境中轻松实现。
