>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 (DVFR–S). The same DIR is used to generate DVFS–R. Additionally, our PIDVF generator is used to create PIDVFS–R. Back-and-forth mapping of a set of points (used as surrogates of contours) using DVFR–S and DVFS–R is compared to back-and-forth mapping performed with DVFR–S and PIDVFS–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 DVFS–R and PIDVFS–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.
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机译：>目的：展示，实现和测试自洽的伪逆位移矢量场（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后续迭代搜索的迭代方案。使用肺4DCT数据集对算法的性能进行基准测试，该数据集包含来自不同呼吸阶段的六张CT图像和针对在不同日期采集的一名俯卧患者的八张CT图像。微分形可变形图像配准用于验证我们的PIDVF。此外，PIDVF用于测量两种不使用自洽约束的非微分形算法的自洽性：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样条算法。恶魔和B样条曲线均已通过轮廓比较进行了质量检查。通过使用DIR生成参考图像R和研究图像S（DVFR–S）之间的位移矢量场（DVF）来确定自洽。相同的DIR用于生成DVFS–R。此外，我们的PIDVF生成器用于创建PIDVFS–R。将使用DVFR-S和DVFS-R对一组点的来回映射（用作轮廓的替代）与使用DVFR-S和PIDVFS- R 。原始未映射点与映射点之间的欧几里得距离用作自洽一致性度量。>结果：测试结果表明，来回映射中观察到的一致性误差可以减少为两个当使用PIDVF代替B样条算法时，点映射的九倍和剂量映射的1.5至三倍。这些自我一致性的提高不受 R 和 S 交换的影响。还证明了DVF S – R 与PIDVF S – R 之间的差异可以用作判据>结论：使用DVF及其PIDVF将改善图像引导自适应治疗中点，轮廓和剂量图的自洽性。
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