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

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), into the strain energy term to regularize a surface matching term. We propose a 3D-3D non-rigid registration method that incorporates a modified FEM into the surface matching term. The modified FEM alleviates the need to specify boundary conditions, which is achieved by modifying the stiffness matrix of a FEM and using diagonal loading for stabilization. As a result, the modified surface matching term does not require the specification of boundary conditions or an additional strain energy term to regularize the surface matching term. Optimization is achieved through an accelerated gradient algorithm, further enhanced by our proposed method for determining the optimal step size. We evaluated our method and compared it to several state-of-the-art methods across various datasets. Our straightforward and effective approach consistently outperformed or achieved comparable performance to the state-of-the-art methods. Our code and datasets are available at https://github.com/zixinyang9109/BCF-FEM.

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Publication Details

Journal
IEEE Transactions on Medical Imaging
Published
2024-12-11
DOI
https://doi.org/10.1109/tmi.2024.3515632
Citations
10
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
1.84

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article

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

Richard Simon, Kelly Merrell, Cristian A. Linte, Zixin Yang
10 citations
IEEE Transactions on Medical Imaging
Medical Imaging and Analysis
1.84
article

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

Richard Simon, Kelly Merrell, Cristian A. Linte, Zixin Yang
article en
10 citations

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), into the strain energy term to regularize a surface matching term. We propose a 3D-3D non-rigid registration method that incorporates a modified FEM into the surface matching term. The modified FEM alleviates the need to specify boundary conditions, which is achieved by modifying the stiffness matrix of a FEM and using diagonal loading for stabilization. As a result, the modified surface matching term does not require the specification of boundary conditions or an additional strain energy term to regularize the surface matching term. Optimization is achieved through an accelerated gradient algorithm, further enhanced by our proposed method for determining the optimal step size. We evaluated our method and compared it to several state-of-the-art methods across various datasets. Our straightforward and effective approach consistently outperformed or achieved comparable performance to the state-of-the-art methods. Our code and datasets are available at https://github.com/zixinyang9109/BCF-FEM.

IEEE Transactions on Medical ImagingVol. 44(4)
Rochester Institute of Technology (US)
Center for Hierarchical Manufacturing, National Science Foundation, Center for Information Technology, Office of Advanced Cyberinfrastructure
Zero hunger
Openalex Percentile: Top 15%
Medical Imaging and Analysis
1.84
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Boundary Constraint-Free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction — Richard Simon, Kelly Merrell, et al. · IEEE Transactions on Medical Imaging (2024) | TGRS Research Map | TGRS