Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
PLoS One. 2019 Mar 15;14(3):e0213004. doi: 10.1371/journal.pone.0213004. eCollection 2019.
US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications.
美国图像配准是一项重要任务,例如在计算机辅助手术中。由于术前和介入图像之间经常发生组织变形,因此通常需要进行可变形配准。我们提出了一种专注于软组织表面结构(即显著特征)的配准方法,例如器官囊或血管。该方法的主要概念遵循代表性地标(所谓的关键点)的想法。这些地标代表了特定感兴趣区域中每个图像中的显著特征。变形的确定基于假设显著特征可以在局部用平面描述的几何模型。这些平面是从地标使用二维线性回归计算得出的。一旦在两幅图像中找到相应的区域,就会计算代表局部变形的位移向量场。最后,将变形后的图像变形以匹配术前图像。对于误差计算,我们使用代表膀胱和前列腺的幻影。该幻影可以变形以模拟组织变形。在两幅图像中使用相应的地标进行误差计算。该过程的目标配准误差达到 1.63 毫米。对于患者数据,我们对腹部的两个 3D-US 图像进行了完整的可变形配准。与简单刚性配准的 2.75 ± 0.57 毫米误差相比,得到的平均距离误差为 2.10 ± 0.66 毫米。双侧配对 t 检验显示 p 值 < 0.001。我们得出结论,该方法大大提高了刚性配准的结果。只要选择适当的滤波器,就有许多可能的应用领域。