Department of Radiation Oncology, Virginia Commonwealth University, Richmond, Virginia 23298, USA.
Med Phys. 2013 Jul;40(7):071721. doi: 10.1118/1.4811105.
To evaluate deformable mesh registration (DMR) as a tool for validating automatic target registration algorithms used during image-guided radiation therapy.
DMR was implemented in a hierarchical model, with rigid, affine, and B-spline transforms optimized in succession to register a pair of surface meshes. The gross tumor volumes (primary tumor and involved lymph nodes) were contoured by a physician on weekly CT scans in a cohort of lung cancer patients and converted to surface meshes. The meshes from weekly CT images were registered to the mesh from the planning CT, and the resulting registered meshes were compared with the delineated surfaces. Known deformations were also applied to the meshes, followed by mesh registration to recover the known deformation. Mesh registration accuracy was assessed at the mesh surface by computing the symmetric surface distance (SSD) between vertices of each registered mesh pair. Mesh registration quality in regions within 5 mm of the mesh surface was evaluated with respect to a high quality deformable image registration.
For 18 patients presenting with a total of 19 primary lung tumors and 24 lymph node targets, the SSD averaged 1.3 ± 0.5 and 0.8 ± 0.2 mm, respectively. Vertex registration errors (VRE) relative to the applied known deformation were 0.8 ± 0.7 and 0.2 ± 0.3 mm for the primary tumor and lymph nodes, respectively. Inside the mesh surface, corresponding average VRE ranged from 0.6 to 0.9 and 0.2 to 0.9 mm, respectively. Outside the mesh surface, average VRE ranged from 0.7 to 1.8 and 0.2 to 1.4 mm. The magnitude of errors generally increased with increasing distance away from the mesh.
Provided that delineated surfaces are available, deformable mesh registration is an accurate and reliable method for obtaining a reference registration to validate automatic target registration algorithms for image-guided radiation therapy, specifically in regions on or near the target surfaces.
评估可变形网格配准(DMR)作为验证图像引导放射治疗中使用的自动靶标配准算法的工具。
DMR 在层次模型中实现,依次优化刚性、仿射和 B 样条变换,以配准一对表面网格。每周对肺癌患者的 CT 扫描进行轮廓勾画,确定肿瘤靶区(原发肿瘤和累及的淋巴结),并转换为表面网格。每周 CT 图像的网格与计划 CT 图像的网格进行配准,将配准后的网格与勾画的表面进行比较。还对网格施加已知变形,然后进行网格配准以恢复已知变形。通过计算每个配准网格对顶点之间的对称表面距离(SSD),在网格表面评估网格配准精度。在距离网格表面 5mm 以内的区域,评估网格配准质量与高质量可变形图像配准的关系。
18 例患者共 19 个原发肺肿瘤和 24 个淋巴结靶区,SSD 分别为 1.3±0.5mm 和 0.8±0.2mm。相对于应用的已知变形,肿瘤和淋巴结的顶点配准误差(VRE)分别为 0.8±0.7mm 和 0.2±0.3mm。在网格表面内,对应的平均 VRE 分别为 0.6-0.9mm 和 0.2-0.9mm。在网格表面外,平均 VRE 分别为 0.7-1.8mm 和 0.2-1.4mm。误差的大小通常随距离网格的增加而增加。
如果可勾画表面可用,那么可变形网格配准是一种准确可靠的方法,可以获得参考配准,以验证图像引导放射治疗中的自动靶标配准算法,特别是在靶区表面或附近区域。