Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images
Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images
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DOI：
10.1118/1.2161408
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发表时间：
2006-02-01
期刊：
影响因子：
3.8
通讯作者：
Mageras, GS
中科院分区：
文献类型：
--
作者：
Pevsner, A;Davis, B;Mageras, GS
我们评估了一种基于不同呼吸阶段获得的胸部 CT 图像来预测肿瘤和肺部变形的自动配准程序。该方法使用组织变形的粘性流体模型将体素从一个 CT 数据集映射到另一个数据集。为了验证可变形匹配算法，我们使用呼吸相关 CT 协议来采集 6 名非小细胞肺癌患者呼吸周期不同阶段的图像。将算法预测的吸气末 (EI) 阶段可变形肉眼肿瘤体积 (GTV) 的位置和形状与四位观察者绘制的位置和形状进行比较。为了最大限度地减少观察者之间的差异，所有观察者都使用单个观察者在呼气末 (EE) 阶段绘制的轮廓作为指导来勾画 El 处的 GTV 轮廓。使用为此目的编写的轮廓比较算法来评估 El 处模型预测和观察者绘制的 GTV 表面之间的差异，以及 El 处观察者描绘的结构之间的差异（观察者间差异），该算法确定两个表面沿不同方向的距离。所有患者和所有方向的模型预测与观察者绘制的 GTV 表面差异的平均值和 90% 置信区间分别为 2.6 和 5.1 mm，而观察者间差异的平均值和 90% 置信区间分别为 2.1 和 3.7 mm。我们还通过检查每个观察者在 EE 和 EI 图像集之间的肺部支气管和血管分支点放置的 41 个标志点的三维 (3-D) 矢量位移来评估该算法预测正常组织变形的能力（平均位移和 90% 置信区间位移分别为 11.7 毫米和 25.1 毫米）。所有患者的模型预测和观察者确定的标志性位移之间的平均差异和 90% 置信区间差异分别为 2.9 和 7.3 朗姆，而观察者间差异为 2.8 和 6.0 毫米。配对 t 检验表明模型预测的结构和观察者绘制的结构之间没有显着的统计差异。我们得出的结论是，在不同呼吸阶段的 CT 图像中绘制肺部解剖结构的算法的准确性与手动描绘的可变性相当。因此，该方法具有预测和量化呼吸引起的肺部肿瘤运动的潜力。 (c) 2006 年美国医学物理学家协会。
We have evaluated an automated registration procedure for predicting tumor and lung deformation based on CT images of the thorax obtained at different respiration phases. The method uses a viscous fluid model of tissue deformation to rnap voxels from one CT dataset to another. To validate the deformable matching algorithm we used a respiration-correlated CT protocol to acquire images at different phases of the respiratory cycle for six patients with nonsmall cell lung carcinoma. The position and shape of the deformable gross tumor volumes (GTV) at the end-inhale (EI) phase predicted by the algorithm was compared to those drawn by four observers. To minimize interobserver differences, all observers used the contours drawn by a single observer at end-exhale (EE) phase as a guideline to outline GTV contours at El. The differences between model-predicted and observer-drawn GTV surfaces at El, as well as differences between structures delineated by observers at El (interobserver variations) were evaluated using a contour comparison algorithm written for this purpose, which determined the distance between the two surfaces along different directions. The mean and 90% confidence interval for model-predicted versus observer-drawn GTV surface differences over all patients and all directions were 2.6 and 5.1 mm, respectively, whereas the mean and 90% confidence interval for interobserver differences were 2.1 and 3.7 mm. We have also evaluated the algorithm's ability to predict normal tissue deformations by examining the three-dimensional (3-D) vector displacement of 41 landmarks placed by each observer at bronchial and vascular branch points in the lung between the EE and EI image sets (mean and 90% confidence interval displacements of 11.7 and 25.1 mm, respectively). The mean and 90% confidence interval discrepancy between model-predicted and observer-determined landmark displacements over all patients were 2.9 and 7.3 rum, whereas interobserver discrepancies were 2.8 and 6.0 mm. Paired t tests indicate no significant statistical differences between model predicted and observer drawn structures. We conclude that the accuracy of the algorithm to map lung anatomy in CT images at different respiratory phases is comparable to the variability in manual delineation. This method has therefore the potential for predicting and quantifying respiration-induced tumor motion in the lung. (c) 2006 American Association of Physicists in Medicine.