Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery

Abstract

During the minimally-invasive liver surgery, only the partial surface view of the liver is usually provided to the surgeon via the laparoscopic camera. Therefore, it is necessary to estimate the actual position of the internal structures such as tumors and vessels from the pre-operative images. Nevertheless, such task can be highly challenging since during the intervention, the abdominal organs undergo important deformations due to the pneumoperitoneum, respiratory and cardiac motion and the interaction with the surgical tools. Therefore, a reliable automatic system for intra-operative guidance requires fast and reliable registration of the pre- and intra-operative data. In this paper we present a complete pipeline for the registration of pre-operative patient-specific image data to the sparse and incomplete intra-operative data. While the intra-operative data is represented by a point cloud extracted from the stereo-endoscopic images, the pre-operative data is used to reconstruct a biomechanical model which is necessary for accurate estimation of the position of the internal structures, considering the actual deformations. This model takes into account the patient-specific liver anatomy composed of parenchyma, vascularization and capsule, and is enriched with anatomical boundary conditions transferred from an atlas. The registration process employs the iterative closest point technique together with a penalty-based method. We perform a quantitative assessment based on the evaluation of the target registration error on synthetic data as well as a qualitative assessment on real patient data. We demonstrate that the proposed registration method provides good results in terms of both accuracy and robustness w.r.t. the quality of the intra-operative data.

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

“…This registration aligned the phantom and clinical data according to the salient features, but differences in organ size and extent remained. We should also note that others are also following this approach [25]. Following initial rigid alignment, the finite iterative closest point registration method by Kroon [26] incorporated scale and skew into the optimization of a transformation matrix, providing an affine registration which accounts for differences in data extent and organ size.…”

Biomechanics-based graph matching for augmented CT-CBCT

“…The biomechanical finite element (FE) model of liver [14] used makes the assumption of linear stress-strain relation, it employs a co-rotational formulation of the strain to handle large nonlinear displacements. The model reconstructed from patient-specific anatomy is reliable for physics-based augmented reality [15]. The proposed matching method is not limited to the chosen FE model which can be replaced by another elastic model, including non-linear ones [16].…”

“…The Dirichlet conditions are imposed via penalty method: physically, this method can be interpreted as adding a set of elastic linear springs which pull each node n i from its initial position x A i to the target position x B j . Similarly, the deformation is computed as a dynamic process given by the system Mü + Bu + K(u) = p where M is a mass matrix, K is the nonlinear co-rotational stiffness with contributions from elastic springs, B is a damping matrix approximated using Rayleigh stiffness r K and Rayleigh mass r M as B = r M M + r K K and p is a vector gathering the prescribed displacements given by the actual matching pairs [15]. The system is integrated by implicit Euler method with single linearization per integration step with r K = r M = 0.1.…”

Intraoperative on-the-fly organ-mosaicking for laparoscopic surgery

“…To remedy this problem, Plantefève et al 6 used anatomical landmarks to achieve a stable initial registration. The preoperative landmarks were labeled automatically while the intraoperative labeling required manual interaction.…”