Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan 48202, USA.
Med Phys. 2010 Mar;37(3):970-9. doi: 10.1118/1.3302141.
Computer aided modeling of anatomic deformation, allowing various techniques and protocols in radiation therapy to be systematically verified and studied, has become increasingly attractive. In this study the potential issues in deformable image registration (DIR) were analyzed based on two numerical phantoms: One, a synthesized, low intensity gradient prostate image, and the other a lung patient's CT image data set. Each phantom was modeled with region-specific material parameters with its deformation solved using a finite element method. The resultant displacements were used to construct a benchmark to quantify the displacement errors of the Demons and B-Spline-based registrations. The results show that the accuracy of these registration algorithms depends on the chosen parameters, the selection of which is closely associated with the intensity gradients of the underlying images. For the Demons algorithm, both single resolution (SR) and multiresolution (MR) registrations required approximately 300 iterations to reach an accuracy of 1.4 mm mean error in the lung patient's CT image (and 0.7 mm mean error averaged in the lung only). For the low gradient prostate phantom, these algorithms (both SR and MR) required at least 1600 iterations to reduce their mean errors to 2 mm. For the B-Spline algorithms, best performance (mean errors of 1.9 mm for SR and 1.6 mm for MR, respectively) on the low gradient prostate was achieved using five grid nodes in each direction. Adding more grid nodes resulted in larger errors. For the lung patient's CT data set, the B-Spline registrations required ten grid nodes in each direction for highest accuracy (1.4 mm for SR and 1.5 mm for MR). The numbers of iterations or grid nodes required for optimal registrations depended on the intensity gradients of the underlying images. In summary, the performance of the Demons and B-Spline registrations have been quantitatively evaluated using numerical phantoms. The results show that parameter selection for optimal accuracy is closely related to the intensity gradients of the underlying images. Also, the result that the DIR algorithms produce much lower errors in heterogeneous lung regions relative to homogeneous (low intensity gradient) regions, suggests that feature-based evaluation of deformable image registration accuracy must be viewed cautiously.
计算机辅助解剖变形建模，允许各种技术和协议在放射治疗中得到系统的验证和研究，已经变得越来越有吸引力。在这项研究中，基于两个数值体模分析了可变形图像配准（DIR）的潜在问题：一个是合成的、低强度梯度前列腺图像，另一个是肺患者的 CT 图像数据集。每个体模都使用具有特定区域的材料参数进行建模，其变形使用有限元方法求解。所得位移用于构建基准，以量化 Demons 和基于 B-Spline 的配准的位移误差。结果表明，这些配准算法的准确性取决于所选择的参数，这些参数的选择与基础图像的强度梯度密切相关。对于 Demons 算法，单分辨率（SR）和多分辨率（MR）配准都需要大约 300 次迭代才能达到肺患者 CT 图像的 1.4 毫米平均误差精度（肺仅平均 0.7 毫米误差）。对于低梯度前列腺体模，这些算法（无论是 SR 还是 MR）都需要至少 1600 次迭代才能将其平均误差降低到 2 毫米。对于 B-Spline 算法，在低梯度前列腺体模上获得最佳性能（SR 的平均误差为 1.9 毫米，MR 的平均误差为 1.6 毫米），在每个方向上使用五个网格节点。增加更多的网格节点会导致更大的误差。对于肺患者的 CT 数据集，B-Spline 配准在每个方向上需要十个网格节点才能达到最高精度（SR 为 1.4 毫米，MR 为 1.5 毫米）。最佳配准所需的迭代次数或网格节点数取决于基础图像的强度梯度。总之，使用数值体模对 Demons 和 B-Spline 配准的性能进行了定量评估。结果表明，最佳精度的参数选择与基础图像的强度梯度密切相关。此外，DIR 算法在异质肺区域产生的误差明显低于均匀（低强度梯度）区域的结果表明，必须谨慎地看待基于特征的变形图像配准准确性评估。
