Image Group, Department of Computer Science, University of Copenhagen, Denmark.
Med Image Anal. 2012 May;16(4):786-95. doi: 10.1016/j.media.2011.11.001. Epub 2012 Jan 14.
This paper presents a mass preserving image registration algorithm for lung CT images. To account for the local change in lung tissue intensity during the breathing cycle, a tissue appearance model based on the principle of preservation of total lung mass is proposed. This model is incorporated into a standard image registration framework with a composition of a global affine and several free-form B-Spline transformations with increasing grid resolution. The proposed mass preserving registration method is compared to registration using the sum of squared intensity differences as a similarity function on four groups of data: 44 pairs of longitudinal inspiratory chest CT scans with small difference in lung volume; 44 pairs of longitudinal inspiratory chest CT scans with large difference in lung volume; 16 pairs of expiratory and inspiratory CT scans; and 5 pairs of images extracted at end exhale and end inhale phases of 4D-CT images. Registration errors, measured as the average distance between vessel tree centerlines in the matched images, are significantly lower for the proposed mass preserving image registration method in the second, third and fourth group, while there is no statistically significant difference between the two methods in the first group. Target registration error, assessed via a set of manually annotated landmarks in the last group, was significantly smaller for the proposed registration method.
本文提出了一种用于肺部 CT 图像的质量保持图像配准算法。为了在呼吸周期中考虑肺部组织强度的局部变化，提出了一种基于保持总肺质量原理的组织外观模型。该模型被纳入具有全局仿射和几个自由形态 B 样条变换的标准图像配准框架中，这些变换的网格分辨率逐渐增加。在四组数据上，将基于质量保持的配准方法与基于平方和强度差的配准方法进行了比较：44 对具有较小肺容量差异的纵向吸气胸部 CT 扫描；44 对具有较大肺容量差异的纵向吸气胸部 CT 扫描；16 对呼气和吸气 CT 扫描；以及 4D-CT 图像中提取的 5 对呼气末和吸气末的图像。在第二、第三和第四组中，基于质量保持的图像配准方法的配准误差（通过匹配图像中的血管树中心线之间的平均距离来测量）明显较低，而在第一组中，两种方法之间没有统计学上的显著差异。在最后一组中通过一组手动标记的地标评估的目标配准误差，对于所提出的配准方法显著更小。
