Elastic modulus estimation based on local displacement observation of elastic body

Occlusion-robust scene flow-based tissue deformation recovery incorporating a mesh optimization model

PURPOSE

Tissue deformation recovery is to reconstruct the change in shape and surface strain caused by tool-tissue interaction or respiration, which is essential for providing motion and shape information that benefits the improvement of the safety of minimally invasive surgery. The binocular vision-based approach is a practical candidate for deformation recovery as no extra devices are required. However, previous methods suffer from limitations such as the reliance on biomechanical priors and the vulnerability to the occlusion caused by surgical instruments. To address the issues, we propose a deformation recovery method incorporating mesh structures and scene flow.

METHODS

The method can be divided into three modules. The first one is the implementation of the two-step scene flow generation module to extract the 3D motion from the binocular sequence. Second, we propose a strain-based filtering method to denoise the original scene flow. Third, a mesh optimization model is proposed that strengthens the robustness to occlusion by employing contextual connectivity.

RESULTS

In a phantom and an in vivo experiment, the feasibility of the method in recovering surface deformation in the presence of tool-induced occlusion was demonstrated. Surface reconstruction accuracy was quantitatively evaluated by comparing the recovered mesh surface with the 3D scanned model in the phantom experiment. Results show that the overall error is 0.70 ± 0.55 mm.

CONCLUSION

The method has been demonstrated to be capable of continuously recovering surface deformation using mesh representation with robustness to the occlusion caused by surgical forceps and promises to be suitable for the application in actual surgery.

Identification of Young's modulus and equivalent spring constraint boundary conditions of the soft tissue with locally observed displacements for endoscopic liver surgery

In endoscopic surgery, the surgical navigation system needs to calculate the deformation of soft tissue by biomechanical model which requires elastic properties and boundary conditions. However, patient-specific elastic parameters and boundary conditions of soft tissue are hard to measure accurately from the preoperative images, especially the boundary conditions will change during the operation due to the ligament cutting. In addition, simple boundary conditions such as fixed constraints and free-force constraints are not physically adequate to simulate the elastic effect of ligaments attached to the liver. In this paper, we present a novel method to identify the Young's modulus and equivalent spring constraint boundary conditions of a locally observed soft tissue. Based on the spring constraint boundary condition, a two-step inverse algorithm is developed based on the finite element method (FEM) with integration of energy regularized Gauss-Newton (GN) method and l1-regularized method, which takes external forces and displacements of observable nodes as inputs. A series of numerical simulations and physical hydrogel phantom experiments were conducted. The results of simulation and physical experiments show that the Young's modulus and equivalent spring constraint boundary conditions identified by the proposed method agree well with their setup true values.