Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
Phys Med Biol. 2013 Jul 21;58(14):4951-79. doi: 10.1088/0031-9155/58/14/4951. Epub 2013 Jun 27.
Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base-of-tongue robotic surgery.
经口机器人手术（TORS）为舌根肿瘤的切除提供了一种微创方法。然而，由于术中设置的高度变形，手术目标和相邻关键结构的精确定位可能会受到挑战。我们提出了一种使用术中锥形束计算机断层扫描（CBCT）的变形配准方法，以将术前 CT 或 MR 图像与术中场景准确对齐。该配准方法结合了高斯混合（GM）模型和 Demons 算法的变体。首先，在对感兴趣的体积（即延伸到舌骨的舌体体积）进行分割后，应用 GM 模型对表面点云进行刚性初始化（GM 刚性），然后进行非刚性变形（GM 非刚性）。其次，应用 Demons 算法对（GM 配准的）术前图像和术中 CBCT 的距离图变换进行配准细化。在重复尸体研究（25 对图像）中，根据目标配准误差（TRE）、熵相关系数（ECC）和归一化点对点互信息（NPMI）评估性能。在 TORS 手术设置中，舌体的回缩会导致超过 30mm 的大体变形。在 GM 刚性、GM 非刚性和 Demons 步骤之后，TRE 的平均值分别为 4.6、2.1 和 1.7mm。ECC 分别为 0.57、0.70 和 0.73，NPMI 分别为 0.46、0.57 和 0.60。配准精度在舌体的上表面和靠近舌骨的区域最好（由于这些结构的表面点的 GM 配准）。Demons 步骤主要在离表面和舌骨更远的舌体深部细化配准。由于该方法不直接使用图像强度，因此适合于将术前 CT 或 MR 与术中 CBCT 进行多模态配准。预计将 3D 图像配准扩展到立体内窥镜视频中图像和规划数据的融合，将支持更安全、高精度的舌根机器人手术。
