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首页> 外文期刊>Physics in medicine and biology. >Biomechanical deformable image registration of longitudinal lung CT images using vessel information
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Biomechanical deformable image registration of longitudinal lung CT images using vessel information

机译：使用血管信息对肺部纵向CT图像进行生物力学可变形图像配准

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Spatial correlation of lung tissue across longitudinal images, as the patient responds to treatment, is a critical step in adaptive radiotherapy. The goal of this work is to expand a biomechanical model-based deformable registration algorithm (Morfeus) to achieve accurate registration in the presence of significant anatomical changes. Six lung cancer patients previously treated with conventionally fractionated radiotherapy were retrospectively evaluated. Exhale CT scans were obtained at treatment planning and following three weeks of treatment. For each patient, the planning CT was registered to the follow-up CT using Morfeus, a biomechanical model-based deformable registration algorithm. To model the complex response of the lung, an extension to Morfeus has been developed: an initial deformation was estimated with Morfeus consisting of boundary conditions on the chest wall and incorporating a sliding interface with the lungs. It was hypothesized that the addition of boundary conditions based on vessel tree matching would provide a robust reduction of the residual registration error. To achieve this, the vessel trees were segmented on the two images by thresholding a vesselness image based on the Hessian matrix's eigenvalues. For each point on the reference vessel tree centerline, the displacement vector was estimated by applying a variant of the Demons registration algorithm between the planning CT and the deformed follow-up CT. An expert independently identified corresponding landmarks well distributed in the lung to compute target registration errors (TRE). The TRE was: 5.8 +/- 2.9, 3.4 +/- 2.3 and 1.6 +/- 1.3 mm after rigid registration, Morfeus and Morfeus with boundary conditions on the vessel tree, respectively. In conclusion, the addition of boundary conditions on the vessels significantly improved the accuracy in modeling the response of the lung and tumor over the course of radiotherapy. Minimizing and modeling these geometrical uncertainties will enable future plan adaptation strategies.
机译：当患者对治疗做出反应时，纵向图像上肺组织的空间相关性是适应性放射治疗的关键步骤。这项工作的目标是扩展基于生物力学模型的可变形配准算法（Morfeus），以在存在重大解剖变化的情况下实现精确配准。回顾性评估了六名先前接受常规分次放疗的肺癌患者。在治疗计划时和治疗三周后进行呼气CT扫描。对于每位患者，使用Morfeus（一种基于生物力学模型的可变形配准算法）将计划CT配准至后续CT。为了模拟肺部的复杂反应，已经开发了对Morfeus的扩展：用Morfeus估计初始变形，该变形由胸壁的边界条件组成，并与肺部形成滑动界面。假设基于血管树匹配的边界条件的添加将提供残余配准误差的有效降低。为此，通过基于Hessian矩阵的特征值对血管图像进行阈值化，在两个图像上分割血管树。对于参考血管树中心线上的每个点，通过在计划CT和变形后的CT之间应用恶魔配准算法的变体来估算位移矢量。专家独立地确定了肺中分布良好的相应地标，以计算目标配准误差（TRE）。 TRE为：刚性套准后为5.8 +/- 2.9、3.4 +/- 2.3和1.6 +/- 1.3 mm，Morfeus和Morfeus分别在血管树上具有边界条件。总之，在血管上增加边界条件大大提高了在放疗过程中对肺和肿瘤反应建模的准确性。最小化和建模这些几何不确定性将使将来的计划适应策略成为可能。

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