Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction

• URL: http://arxiv.org/abs/2403.09964v2
• Date: Mon, 9 Sep 2024 10:41:31 GMT
• Title: Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction
• Authors: Zixin Yang, Richard Simon, Kelly Merrell, Cristian. A. Linte,
• Abstract summary: In image-guided liver surgery, 3D-3D non-rigid registration methods play a crucial role in estimating the mapping between the preoperative model and the intraoperative surface represented as point clouds. This paper introduces a novel 3D-3D non-rigid registration method. In contrast to the preceding techniques, our method uniquely incorporates the FEM within the surface matching term itself.
• Score: 0.6249768559720122
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In image-guided liver surgery, 3D-3D non-rigid registration methods play a crucial role in estimating the mapping between the preoperative model and the intraoperative surface represented as point clouds, addressing the challenge of tissue deformation. Typically, these methods incorporate a biomechanical model, represented as a finite element model (FEM), used to regularize a surface matching term. This paper introduces a novel 3D-3D non-rigid registration method. In contrast to the preceding techniques, our method uniquely incorporates the FEM within the surface matching term itself, ensuring that the estimated deformation maintains geometric consistency throughout the registration process. Additionally, we eliminate the need to determine zero-boundary conditions and applied force locations in the FEM. We achieve this by integrating soft springs into the stiffness matrix and allowing forces to be distributed across the entire liver surface. To further improve robustness, we introduce a regularization technique focused on the gradient of the force magnitudes. This regularization imposes spatial smoothness and helps prevent the overfitting of irregular noise in intraoperative data. Optimization is achieved through an accelerated proximal gradient algorithm, further enhanced by our proposed method for determining the optimal step size. Our method is evaluated and compared to both a learning-based method and a traditional method that features FEM regularization using data collected on our custom-developed phantom, as well as two publicly available datasets. Our method consistently outperforms or is comparable to the baseline techniques. Both the code and dataset will be made publicly available.

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