Institute of Medical Informatics, University of Lübeck, 23538 Lübeck, Germany.
IEEE Trans Med Imaging. 2011 Feb;30(2):251-65. doi: 10.1109/TMI.2010.2076299. Epub 2010 Sep 27.
Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patient's anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.
基于不同患者的胸部 4DCT 数据生成肺部的平均运动模型，以扩展运动建模能力。我们的建模过程包括三个步骤：对个体患者在不同呼吸相位的 3D 图像数据进行个体内配准，以生成特定于个体的运动模型；生成肺部的平均形状和强度图谱作为解剖参考框架；将特定于个体的运动模型配准到图谱上，以构建统计 4D 平均运动模型（4D-MMM）。此外，我们还提出了将 4D 平均运动模型适配到特定于患者的肺部几何形状的方法。在所有步骤中，都采用了对称的基于差分同胚非线性强度的配准方法。使用对数欧几里得框架计算差分同胚变换的统计学信息。然后，使用这些方法基于 17 名患者的胸部 4DCT 数据集构建平均呼吸肺部运动模型。我们通过将其应用于估计 10 名肺癌患者的呼吸运动来评估该模型。根据标志点和肿瘤运动评估预测，并且如果肺部动态不受大的肺部肿瘤或其他肺部疾病（例如肺气肿）的影响，则定量分析结果的平均目标配准误差（TRE）为 3.3±1.6mm。对于肺部肿瘤运动，我们表明在考虑的数据集内，预测准确性与肿瘤大小和肿瘤运动幅度无关。然而，附着于非肺部结构的肿瘤会显著降低局部肺部动态，并且在这些情况下，基于模型的预测准确性较低。统计呼吸运动模型能够为许多应用领域提供有价值的先验知识。我们提出了在放射治疗和图像引导诊断中两个可能的应用示例。
