Biomechanics-based graph matching for augmented CT-CBCT
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.
KeywordsBiomechanical Modeling Elastic graph registration CBCT Image-guided interventions Image registration
The authors are grateful for the support from Inria, the MIMESIS and MAGRIT teams, and IHU Strasbourg.
Compliance with ethical standards
Conflict of interest
All the authors declare that they have no conflict of interest.
All institutional and national guidelines for the care and use of laboratory animals were followed.
This articles does not contain patient data.
- 6.Dagon B, Baur C, Bettschart V (2008) Real-time update of 3D deformable models for computer aided liver surgery. In: 19th international conference on pattern recognition, pp 2–5Google Scholar
- 7.Oktay O, Zhang L, Mansi T, Mountney P, Mewes P, Nicolau S, Soler L, Chefd’hotel C (2013) Biomechanically driven registration of pre- to intra-operative 3d images for laparoscopic surgery. In: International conference MICCAI. Springer, Berlin, pp 1–9Google Scholar
- 10.Plantefève R, Kadoury S, Tang A, Peterlik I (2017) Robust automatic graph-based skeletonization of hepatic vascular trees, vol 10552. LNCS, Springer, Berlin, pp 20–28Google Scholar
- 11.Rasmussen C, Williams C (2006) Gaussian processes for machine learning. MIT Press, CambridgeGoogle Scholar
- 14.Peterlík I, Duriez C, Cotin S (2012) Modeling and real-time simulation of a vascularized liver tissue. In: International conference MICCAI. Springer, Berlin, pp 50–57Google Scholar
- 16.Marchesseau S, Chatelin S, Delingette H (2017) Non linear biomechanical model of the Liver. In: Payan Y, Ohayon J (eds) Biomechanics of living organs. Elsevier, Amsterdam, p 602Google Scholar
- 18.Boltcheva D, Yvinec M, Boissonnat JD (2009) Mesh generation from 3D multi-material images. In: International Conference MICCAI. Springer, Berlin, pp 283–290Google Scholar