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首页> 外文期刊>Medical Physics >Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis.
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Predictive modeling of lung motion over the entire respiratory cycle using measured pressure-volume data, 4DCT images, and finite-element analysis.

机译：使用测得的压力-体积数据，4DCT图像和有限元分析对整个呼吸周期中的肺运动进行预测建模。

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PURPOSE: Predicting complex patterns of respiration can benefit the management of the respiratory motion for radiation therapy of lung cancer. The purpose of the present work was to develop a patient-specific, physiologically relevant respiratory motion model which is capable of predicting lung tumor motion over a complete normal breathing cycle. METHODS: Currently employed techniques for generating the lung geometry from four-dimensional computed tomography data tend to lose details of mesh topology due to excessive surface smoothening. Some of the existing models apply displacement boundary conditions instead of the intrapleural pressure as the actual motive force for respiration, while others ignore the nonlinearity of lung tissues or the mechanics of pleural sliding. An intermediate nonuniform rational basis spline surface representation is used to avoid multiple geometric smoothing procedures used in the computational mesh preparation. Measured chest pressure-volume relationships are used to simulate pressure loading on the surface of the model for a given lung volume, as in actual breathing. A hyperelastic model, developed from experimental observations, has been used to model the lung tissue material. Pleural sliding on the inside of the ribcage has also been considered. RESULTS: The finite-element model has been validated using landmarks from four patient CT data sets over 34 breathing phases. The average differences of end-inspiration in position between the landmarks and those predicted by the model are observed to be 0.450 +/- 0.330 cm for Patient P1, 0.387 +/- 0.169 cm for Patient P2, 0.319 +/- 0.186 cm for Patient P3, and 0.204 +/- 0.102 cm for Patient P4 in the magnitude of error vector, respectively. The average errors of prediction at landmarks over multiple breathing phases in superior-inferior direction are less than 3 mm. CONCLUSIONS: The prediction capability of pressure-volume curve driven nonlinear finite-element model is consistent over the entire breathing cycle. The biomechanical parameters in the model are physiologically measurable, so that the results can be extended to other patients and additional neighboring organs affected by respiratory motion.
机译：目的：预测复杂的呼吸模式可以有益于肺癌放射治疗中呼吸运动的管理。本工作的目的是开发一种特定于患者的生理相关的呼吸运动模型，该模型能够预测整个正常呼吸周期内的肺肿瘤运动。方法：由于过度的表面平滑，目前使用的从四维计算机断层扫描数据生成肺几何形状的技术往往会丢失网格拓扑的详细信息。现有的一些模型将位移边界条件而不是胸膜内压力作为呼吸的实际动力，而其他模型则忽略了肺组织的非线性或胸膜滑动的机制。使用中间非均匀有理基础样条曲面表示来避免在计算网格准备中使用多个几何平滑过程。如在实际呼吸中一样，所测量的胸部压力-体积关系用于模拟给定肺体积下模型表面的压力载荷。通过实验观察得到的超弹性模型已用于对肺组织材料进行建模。还考虑了胸腔内侧的胸膜滑动。结果：有限元模型已使用来自34个呼吸阶段的四个患者CT数据集的界标进行了验证。观察到地标与模型预测的位置之间的最终吸气位置的平均差异为：患者P1为0.450 +/- 0.330 cm，患者P2为0.387 +/- 0.169 cm，患者为0.319 +/- 0.186 cm P3，患者P4的误差为0.204 +/- 0.102 cm。在上下方向上的多个呼吸阶段，地标上的预测平均误差小于3 mm。结论：压力-容量曲线驱动的非线性有限元模型的预测能力在整个呼吸周期中是一致的。该模型中的生物力学参数是生理可测量的，因此该结果可以扩展到其他患者以及受呼吸运动影响的其他邻近器官。

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