Purpose: Augmenting intraoperative cone beam computed tomography (CBCT) images with preoperative computed tomography (CT) data in the context of image-guided liver therapy. The expected benefit is an improved visualization of tu-mor(s), vascular system and other internal structures of interest. Method: An automatic elastic registration based on matching of vascular trees extracted from both the preoperative and intraoperative images. Although methods dedicated to non-rigid graph matching exist, they are not efficient when large in-traoperative 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) [1]: 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. Results: The proposed automatic intraoperative augmentation is evaluated on both synthetic and real data. 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 intraopera-tive navigation scenario. Conclusion: A biomechanics-based graph matching method, which can handle large deformations and augment intraoperative CBCT, is presented and evaluated.