3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy
3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy
复制标题
DOI：
10.1118/1.3582693
复制
发表时间：
2011-05-01
期刊：
影响因子：
3.8
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Li, Ruijiang;Lewis, John H.;Jiang, Steve B.
目的：评估用于肺癌放射治疗的单个 X 射线投影图像的实时 3D 肿瘤定位算法。方法：最近，我们开发了一种用于重建体积图像并从单个 X 射线投影中提取 3D 肿瘤运动信息的算法 [Li et al., Med. 2017]。物理。 37, 2822-2826 (2010)]。我们已经使用具有规则呼吸模式的数字呼吸模型证明了其可行性。在这项工作中，我们对改进算法进行了详细描述和综合评估。通过结合呼吸运动预测对该算法进行了改进。然后在 (1) 数字呼吸模型、(2) 物理呼吸模型和 (3) 5 名肺癌患者上评估使用该算法进行 3D 肿瘤定位的准确性和效率。这些评估案例包括与训练数据集不同的规则和不规则呼吸模式。结果：对于规则和不规则呼吸的数字呼吸体模，平均3D肿瘤定位误差小于1毫米，似乎不受幅度变化、周期变化或基线偏移的影响。在 NVIDIA Tesla C1060 图形处理单元 (GPU) 卡上，对于规则呼吸和不规则呼吸，每个投影的 3D 肿瘤定位平均计算时间在 0.19 到 0.26 秒之间，这比之前报告的结果提高了约 10%。对于物理呼吸模型，对于规则呼吸和不规则呼吸，在同一图形处理单元（GPU）卡上的平均计算时间分别为 0.13 和 0.16 秒，实现了低于 1 毫米的平均肿瘤定位误差。对于5名肺癌患者，轴向和切向的平均肿瘤定位误差均低于2毫米。同一 GPU 卡上的平均计算时间在 0.26 至 0.34 秒之间。结论：通过对我们的算法的综合评估，我们确定其 3D 肿瘤定位精度对于数字体模和物理体模来说平均在 1 mm 左右，95% 时为 2 mm；对于肺癌患者，3D 肿瘤定位精度平均在 2 mm 左右，95% 时为 4 mm。结果还表明，准确性不受呼吸模式（无论是规则还是不规则）的影响。在GPU上可以实现很高的计算效率，每次X射线投影需要0.1-0.3秒。 (C) 2011 年美国医学物理学家协会。 [DOI：10.1118/1.3582693]
Purpose: To evaluate an algorithm for real-time 3D tumor localization from a single x-ray projection image for lung cancer radiotherapy.Methods: Recently, we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection [Li et al., Med. Phys. 37, 2822-2826 (2010)]. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency of using this algorithm for 3D tumor localization were then evaluated on (1) a digital respiratory phantom, (2) a physical respiratory phantom, and (3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset.Results: For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm which does not seem to be affected by amplitude change, period change, or baseline shift. On an NVIDIA Tesla C1060 graphic processing unit (GPU) card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 s, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 s on the same graphic processing unit (GPU) card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 s.Conclusions: Through a comprehensive evaluation of our algorithm, we have established its accuracy in 3D tumor localization to be on the order of 1 mm on average and 2 mm at 95 percentile for both digital and physical phantoms, and within 2 mm on average and 4 mm at 95 percentile for lung cancer patients. The results also indicate that the accuracy is not affected by the breathing pattern, be it regular or irregular. High computational efficiency can be achieved on GPU, requiring 0.1-0.3 s for each x-ray projection. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3582693]