Bioengineering College, Chongqing University, Chongqing 400030, China.
Med Phys. 2013 Aug;40(8):081902. doi: 10.1118/1.4812419.
Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution.
As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs.
In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively.
The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation.
呼吸运动导致肿瘤位置和肺部变形的不确定性，这往往导致在胸部放射治疗中难以计算剂量分布。可变形图像配准（DIR）具有描述呼吸引起的肺部变形的能力，通过这种技术可以将高剂量输送到肿瘤，同时减少周围正常组织的辐射。作者的目标是提出一种 DIR 方法来克服以前的肺部变形生物力学模型的两个主要挑战，即需要精确的边界条件和缺乏弹性分布。
与生物力学建模中的典型方法不同，作者的方法假设肺组织是不均匀的。因此，作者提出了一种 DIR 方法，该方法将变强度流（VF）块匹配算法与有限元方法（FEM）相结合，用于从呼气末期到吸气末期的肺部变形。具体来说，肺部变形被公式化为一个应力-应变问题，其边界条件从 VF 块匹配算法获得，元素特定的杨氏模量分布通过使用拟牛顿法求解优化问题来估计。作者通过将这两种方法应用于六位患者的四维 CT 图像来测量非均匀模型和标准均匀模型的空间准确性。通过使用大量（>1000）专家确定的地标点对来计算配准产生的空间误差。
在左右、前后和上下方向上，标准均匀 FEM 模型产生的平均误差（标准差）分别为 1.42(1.42)、1.06(1.05)和 1.98(2.10)mm，而作者提出的非均匀模型将这些误差降低至 0.59(0.61)、0.52(0.51)和 0.78(0.89)mm。均匀和非均匀模型的整体 3D 平均误差分别为 3.05(2.36)和 1.30(0.97)mm。
结果表明，通过空间变化的杨氏模量估计，提出的非均匀模型可以模拟患者特异性和位置特异性的肺部变形，与均匀模型相比，这提高了配准精度，因此是对肺部变形的更合适描述。
