Inria Nancy Grand Est, Villers-les-Nancy, France.
Université de Lorraine, Nancy, France.
Int J Comput Assist Radiol Surg. 2018 Jun;13(6):805-813. doi: 10.1007/s11548-018-1755-1. Epub 2018 Apr 3.
Augmenting intraoperative cone beam computed tomography (CBCT) images with preoperative computed tomography data in the context of image-guided liver therapy is proposed. The expected benefit is an improved visualization of tumor(s), vascular system and other internal structures of interest.
An automatic elastic registration based on matching of vascular trees extracted from both the preoperative and intraoperative images is presented. Although methods dedicated to nonrigid graph matching exist, they are not efficient when large intraoperative deformations of tissues occur, as is the case during the liver surgery. The contribution is an extension of the graph matching algorithm using Gaussian process regression (GPR) (Serradell et al. in IEEE Trans Pattern Anal Mach Intell 37(3):625-638, 2015): First, an improved GPR matching is introduced by imposing additional constraints during the matching when the number of hypothesis is large; like the original algorithm, this extended version does not require a manual initialization of matching. Second, a fast biomechanical model is employed to make the method capable of handling large deformations.
The proposed automatic intraoperative augmentation is evaluated on both synthetic and real data. It is demonstrated that the algorithm is capable of handling large deformations, thus being more robust and reliable than previous approaches. Moreover, the time required to perform the elastic registration is compatible with the intraoperative navigation scenario.
A biomechanics-based graph matching method, which can handle large deformations and augment intraoperative CBCT, is presented and evaluated.
提出在图像引导肝脏治疗中，用术前计算机断层扫描（CT）数据增强术中锥形束 CT（CBCT）图像。预期的好处是提高肿瘤、血管系统和其他感兴趣的内部结构的可视化效果。
提出了一种基于从术前和术中图像中提取的血管树匹配的自动弹性配准方法。虽然存在专门用于非刚性图形匹配的方法，但当组织发生大的术中变形时，例如在肝脏手术中，它们的效率不高。贡献是使用高斯过程回归（GPR）扩展图形匹配算法（Serradell 等人，IEEE Trans Pattern Anal Mach Intell 37（3）：625-638，2015）：首先，通过在假设数量很大时在匹配过程中施加附加约束，引入了改进的 GPR 匹配；与原始算法一样，此扩展版本不需要手动初始化匹配。其次，采用快速生物力学模型使该方法能够处理大变形。
该算法在合成和真实数据上进行了评估。结果表明，该算法能够处理大变形，因此比以前的方法更稳健可靠。此外，执行弹性配准所需的时间与术中导航方案兼容。
提出并评估了一种基于生物力学的图形匹配方法，该方法可以处理大变形并增强术中 CBCT。
