伪逆变形向量场生成器及其应用。
A pseudoinverse deformation vector field generator and its applications.
机构信息
Department of Radiation Oncology, Virginia Commonwealth University, P.O. Box 980058, Richmond, Virginia 23298, USA.
出版信息
Med Phys. 2010 Mar;37(3):1117-28. doi: 10.1118/1.3301594.
PURPOSE
To present, implement, and test a self-consistent pseudoinverse displacement vector field (PIDVF) generator, which preserves the location of information mapped back-and-forth between image sets.
METHODS
The algorithm is an iterative scheme based on nearest neighbor interpolation and a subsequent iterative search. Performance of the algorithm is benchmarked using a lung 4DCT data set with six CT images from different breathing phases and eight CT images for a single prostrate patient acquired on different days. A diffeomorphic deformable image registration is used to validate our PIDVFs. Additionally, the PIDVF is used to measure the self-consistency of two nondiffeomorphic algorithms which do not use a self-consistency constraint: The ITK Demons algorithm for the lung patient images and an in-house B-Spline algorithm for the prostate patient images. Both Demons and B-Spline have been QAed through contour comparison. Self-consistency is determined by using a DIR to generate a displacement vector field (DVF) between reference image R and study image S (DVF(R-S)). The same DIR is used to generate DVF(S-R). Additionally, our PIDVF generator is used to create PIDVF(S-R). Back-and-forth mapping of a set of points (used as surrogates of contours) using DVF(R-S) and DVF(S-R) is compared to back-and-forth mapping performed with DVF(R-S) and PIDVF(S-R). The Euclidean distances between the original unmapped points and the mapped points are used as a self-consistency measure.
RESULTS
Test results demonstrate that the consistency error observed in back-and-forth mappings can be reduced two to nine times in point mapping and 1.5 to three times in dose mapping when the PIDVF is used in place of the B-Spline algorithm. These self-consistency improvements are not affected by the exchanging of R and S. It is also demonstrated that differences between DVF(S-R) and PIDVF(S-R) can be used as a criteria to check the quality of the DVF.
CONCLUSIONS
Use of DVF and its PIDVF will improve the self-consistency of points, contour, and dose mappings in image guided adaptive therapy.
目的
提出、实现和测试一个自一致的伪逆位移矢量场 (PIDVF) 生成器，该生成器保留了在图像集之间来回映射的信息的位置。
方法
该算法是一种基于最近邻插值和随后的迭代搜索的迭代方案。使用具有六个来自不同呼吸阶段的 CT 图像的肺部 4DCT 数据集以及单个前列腺患者的八个来自不同日期的 CT 图像来对算法的性能进行基准测试。使用变形图像配准来验证我们的 PIDVFs。此外，还使用 PIDVF 来测量两个不使用自一致性约束的非变形算法的自一致性：用于肺部患者图像的 ITK Demons 算法和用于前列腺患者图像的内部 B-Spline 算法。Demons 和 B-Spline 都已经通过轮廓比较进行了 QA。通过使用 DIR 在参考图像 R 和研究图像 S 之间生成位移矢量场 (DVF)（DVF(R-S)）来确定自一致性。使用相同的 DIR 在 S-R 之间生成 DVF。此外，我们的 PIDVF 生成器用于创建 PIDVF(S-R)。使用 DVF(R-S) 和 DVF(S-R) 对一组点（用作轮廓的替代物）进行来回映射，并将其与使用 DVF(R-S) 和 PIDVF(S-R) 进行的来回映射进行比较。原始未映射点和映射点之间的欧几里得距离用作自一致性度量。
结果
测试结果表明，当在 B-Spline 算法中使用 PIDVF 时，点映射的来回映射中的一致性误差可以减少两到九倍，剂量映射中的一致性误差可以减少 1.5 到三倍。这些自一致性改进不受 R 和 S 的交换的影响。还表明，DVF(S-R) 和 PIDVF(S-R) 之间的差异可以用作检查 DVF 质量的标准。
结论
在图像引导自适应治疗中，使用 DVF 和其 PIDVF 将提高点、轮廓和剂量映射的自一致性。
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